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<a href="primal__dual__hybrid__gradient_8cc.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="comment">// Copyright 2010-2021 Google LLC</span></div>
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<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span><span class="comment">// Licensed under the Apache License, Version 2.0 (the "License");</span></div>
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<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="comment">// you may not use this file except in compliance with the License.</span></div>
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="comment">// You may obtain a copy of the License at</span></div>
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="comment">//</span></div>
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<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span><span class="comment">// http://www.apache.org/licenses/LICENSE-2.0</span></div>
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span><span class="comment">//</span></div>
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="comment">// Unless required by applicable law or agreed to in writing, software</span></div>
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="comment">// distributed under the License is distributed on an "AS IS" BASIS,</span></div>
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span><span class="comment">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span></div>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="comment">// See the License for the specific language governing permissions and</span></div>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="comment">// limitations under the License.</span></div>
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> </div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span><span class="preprocessor">#include "<a class="code" href="primal__dual__hybrid__gradient_8h.html">ortools/pdlp/primal_dual_hybrid_gradient.h</a>"</span></div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> </div>
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span><span class="preprocessor">#include <algorithm></span></div>
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span><span class="preprocessor">#include <atomic></span></div>
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span><span class="preprocessor">#include <cmath></span></div>
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<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span><span class="preprocessor">#include <cstdint></span></div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span><span class="preprocessor">#include <functional></span></div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span><span class="preprocessor">#include <limits></span></div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span><span class="preprocessor">#include <random></span></div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span><span class="preprocessor">#include <string></span></div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span><span class="preprocessor">#include <utility></span></div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span><span class="preprocessor">#include <vector></span></div>
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<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> </div>
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<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span><span class="preprocessor">#include "Eigen/Core"</span></div>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span><span class="preprocessor">#include "Eigen/SparseCore"</span></div>
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span><span class="preprocessor">#include "absl/status/status.h"</span></div>
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span><span class="preprocessor">#include "absl/status/statusor.h"</span></div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span><span class="preprocessor">#include "absl/strings/str_cat.h"</span></div>
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span><span class="preprocessor">#include "absl/strings/str_format.h"</span></div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span><span class="preprocessor">#include "absl/strings/string_view.h"</span></div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span><span class="preprocessor">#include "absl/types/optional.h"</span></div>
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<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span><span class="preprocessor">#include "<a class="code" href="base_2logging_8h.html">ortools/base/logging.h</a>"</span></div>
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<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span><span class="preprocessor">#include "<a class="code" href="mathutil_8h.html">ortools/base/mathutil.h</a>"</span></div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span><span class="preprocessor">#include "<a class="code" href="timer_8h.html">ortools/base/timer.h</a>"</span></div>
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<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span><span class="preprocessor">#include "ortools/glop/parameters.pb.h"</span></div>
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<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span><span class="preprocessor">#include "<a class="code" href="preprocessor_8h.html">ortools/glop/preprocessor.h</a>"</span></div>
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<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span><span class="preprocessor">#include "ortools/linear_solver/linear_solver.pb.h"</span></div>
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<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span><span class="preprocessor">#include "<a class="code" href="lp__data_8h.html">ortools/lp_data/lp_data.h</a>"</span></div>
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<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span><span class="preprocessor">#include "<a class="code" href="lp__types_8h.html">ortools/lp_data/lp_types.h</a>"</span></div>
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<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span><span class="preprocessor">#include "<a class="code" href="lp__data_2proto__utils_8h.html">ortools/lp_data/proto_utils.h</a>"</span></div>
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<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span><span class="preprocessor">#include "<a class="code" href="iteration__stats_8h.html">ortools/pdlp/iteration_stats.h</a>"</span></div>
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<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span><span class="preprocessor">#include "<a class="code" href="quadratic__program_8h.html">ortools/pdlp/quadratic_program.h</a>"</span></div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span><span class="preprocessor">#include "<a class="code" href="sharded__optimization__utils_8h.html">ortools/pdlp/sharded_optimization_utils.h</a>"</span></div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span><span class="preprocessor">#include "<a class="code" href="sharded__quadratic__program_8h.html">ortools/pdlp/sharded_quadratic_program.h</a>"</span></div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span><span class="preprocessor">#include "<a class="code" href="sharder_8h.html">ortools/pdlp/sharder.h</a>"</span></div>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span><span class="preprocessor">#include "ortools/pdlp/solve_log.pb.h"</span></div>
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<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span><span class="preprocessor">#include "ortools/pdlp/solvers.pb.h"</span></div>
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<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span><span class="preprocessor">#include "<a class="code" href="solvers__proto__validation_8h.html">ortools/pdlp/solvers_proto_validation.h</a>"</span></div>
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span><span class="preprocessor">#include "<a class="code" href="termination_8h.html">ortools/pdlp/termination.h</a>"</span></div>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span><span class="preprocessor">#include "<a class="code" href="trust__region_8h.html">ortools/pdlp/trust_region.h</a>"</span></div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> </div>
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<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceoperations__research_1_1pdlp.html">operations_research::pdlp</a> {</div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> </div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span><span class="keyword">namespace </span>{</div>
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> </div>
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<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span>using ::Eigen::VectorXd;</div>
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<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> </div>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span><span class="keyword">using</span> IterationStatsCallback =</div>
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<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> std::function<void(<span class="keyword">const</span> IterationCallbackInfo&)>;</div>
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<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> </div>
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<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span><span class="comment">// Returns infinity norm of the given matrix viewed as a vector.</span></div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span><span class="keywordtype">double</span> MaxAbsCoefficient(</div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> <span class="keyword">const</span> Eigen::SparseMatrix<double, Eigen::ColMajor, int64_t>& matrix) {</div>
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<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> <span class="comment">// Note: matrix.coeffs().lpNorm<Eigen::Infinity>() gives a link error.</span></div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> <span class="keywordflow">return</span> matrix.nonZeros() ? matrix.coeffs().cwiseAbs().maxCoeff() : 0;</div>
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<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span>}</div>
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<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> </div>
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<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span><span class="comment">// If `num_shards' is positive, returns it. Otherwise returns a reasonable</span></div>
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span><span class="comment">// number of shards to use with ShardedQuadraticProgram for the given number of</span></div>
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span><span class="comment">// threads.</span></div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span><span class="keywordtype">int</span> NumShards(<span class="keyword">const</span> <span class="keywordtype">int</span> num_shards, <span class="keyword">const</span> <span class="keywordtype">int</span> num_threads) {</div>
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<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> <span class="keywordflow">if</span> (num_shards > 0) <span class="keywordflow">return</span> num_shards;</div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> <span class="keywordflow">return</span> num_threads == 1 ? 1 : 4 * num_threads;</div>
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<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span>}</div>
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<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> </div>
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span>std::string <a class="code hl_function" href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">ToString</a>(<span class="keyword">const</span> ConvergenceInformation& convergence_information,</div>
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<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> <span class="keyword">const</span> RelativeConvergenceInformation& relative_information,</div>
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<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="keyword">const</span> OptimalityNorm residual_norm) {</div>
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<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> <span class="keyword">constexpr</span> absl::string_view kFormatStr =</div>
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<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> <span class="stringliteral">"%#12.6g %#12.6g %#12.6g | %#12.6g %#12.6g %#12.6g | %#12.6g %#12.6g | "</span></div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> <span class="stringliteral">"%#12.6g %#12.6g"</span>;</div>
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="keywordflow">switch</span> (residual_norm) {</div>
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="keywordflow">case</span> OPTIMALITY_NORM_L2:</div>
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> <span class="keywordflow">return</span> absl::StrFormat(kFormatStr,</div>
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<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> relative_information.relative_l2_primal_residual,</div>
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<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> relative_information.relative_l2_dual_residual,</div>
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<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> relative_information.relative_optimality_gap,</div>
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<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> convergence_information.l2_primal_residual(),</div>
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<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> convergence_information.l2_dual_residual(),</div>
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<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> convergence_information.primal_objective() -</div>
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<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> convergence_information.dual_objective(),</div>
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<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> convergence_information.primal_objective(),</div>
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<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> convergence_information.dual_objective(),</div>
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<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> convergence_information.l2_primal_variable(),</div>
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<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> convergence_information.l2_dual_variable());</div>
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<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> <span class="keywordflow">case</span> OPTIMALITY_NORM_L_INF:</div>
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<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> <span class="keywordflow">return</span> absl::StrFormat(</div>
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|
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> kFormatStr, relative_information.relative_l_inf_primal_residual,</div>
|
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<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> relative_information.relative_l_inf_dual_residual,</div>
|
|
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> relative_information.relative_optimality_gap,</div>
|
|
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> convergence_information.l_inf_primal_residual(),</div>
|
|
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> convergence_information.l_inf_dual_residual(),</div>
|
|
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> convergence_information.primal_objective() -</div>
|
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> convergence_information.dual_objective(),</div>
|
|
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> convergence_information.primal_objective(),</div>
|
|
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> convergence_information.dual_objective(),</div>
|
|
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> convergence_information.l2_primal_variable(),</div>
|
|
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> convergence_information.l2_dual_variable());</div>
|
|
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> <span class="keywordflow">case</span> OPTIMALITY_NORM_UNSPECIFIED:</div>
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|
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#acdd38e3c9f22f127d7776920e3079eda">FATAL</a>) << <span class="stringliteral">"Invalid residual norm."</span>;</div>
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|
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> }</div>
|
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<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span>}</div>
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<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> </div>
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span>std::string ToShortString(</div>
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|
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> <span class="keyword">const</span> ConvergenceInformation& convergence_information,</div>
|
|
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> <span class="keyword">const</span> RelativeConvergenceInformation& relative_information,</div>
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<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <span class="keyword">const</span> OptimalityNorm residual_norm) {</div>
|
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<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> <span class="keyword">constexpr</span> absl::string_view kFormatStr =</div>
|
|
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> <span class="stringliteral">"%#10.4g %#10.4g %#10.4g | %#10.4g %#10.4g"</span>;</div>
|
|
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> <span class="keywordflow">switch</span> (residual_norm) {</div>
|
|
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> <span class="keywordflow">case</span> OPTIMALITY_NORM_L2:</div>
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|
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> <span class="keywordflow">return</span> absl::StrFormat(kFormatStr,</div>
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|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> relative_information.relative_l2_primal_residual,</div>
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|
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> relative_information.relative_l2_dual_residual,</div>
|
|
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> relative_information.relative_optimality_gap,</div>
|
|
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> convergence_information.primal_objective(),</div>
|
|
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> convergence_information.dual_objective());</div>
|
|
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <span class="keywordflow">case</span> OPTIMALITY_NORM_L_INF:</div>
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|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> <span class="keywordflow">return</span> absl::StrFormat(</div>
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|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> kFormatStr, relative_information.relative_l_inf_primal_residual,</div>
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|
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> relative_information.relative_l_inf_dual_residual,</div>
|
|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> relative_information.relative_optimality_gap,</div>
|
|
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> convergence_information.primal_objective(),</div>
|
|
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> convergence_information.dual_objective());</div>
|
|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> <span class="keywordflow">case</span> OPTIMALITY_NORM_UNSPECIFIED:</div>
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|
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#acdd38e3c9f22f127d7776920e3079eda">FATAL</a>) << <span class="stringliteral">"Invalid residual norm."</span>;</div>
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|
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> }</div>
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<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span>}</div>
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<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> </div>
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<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span><span class="comment">// Returns a string describing iter_stats, based on the ConvergenceInformation</span></div>
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|
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span><span class="comment">// with candidate_type==preferred_candidate if one exists, otherwise based on</span></div>
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|
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span><span class="comment">// the first value, if any. termination_criteria.optimality_norm determines the</span></div>
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<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span><span class="comment">// norm in which the residuals are displayed.</span></div>
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<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span>std::string <a class="code hl_function" href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">ToString</a>(<span class="keyword">const</span> IterationStats& iter_stats,</div>
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|
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> <span class="keyword">const</span> TerminationCriteria& termination_criteria,</div>
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<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> <span class="keyword">const</span> QuadraticProgramBoundNorms& bound_norms,</div>
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<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> PointType preferred_candidate) {</div>
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|
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> std::string iteration_string =</div>
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|
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> absl::StrFormat(<span class="stringliteral">"%6d %8.1f %6.1f"</span>, iter_stats.iteration_number(),</div>
|
|
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> iter_stats.cumulative_kkt_matrix_passes(),</div>
|
|
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> iter_stats.cumulative_time_sec());</div>
|
|
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="keyword">auto</span> convergence_information =</div>
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|
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a7b93e1d980b7d8112423361ac15a0c28">GetConvergenceInformation</a>(iter_stats, preferred_candidate);</div>
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|
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> <span class="keywordflow">if</span> (!convergence_information.has_value() &&</div>
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|
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> iter_stats.convergence_information_size() > 0) {</div>
|
|
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> convergence_information = iter_stats.convergence_information(0);</div>
|
|
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> }</div>
|
|
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> <span class="keywordflow">if</span> (convergence_information.has_value()) {</div>
|
|
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> <span class="keyword">const</span> RelativeConvergenceInformation relative_information =</div>
|
|
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a8e307fb8ac2854dd493d52760dd3aa30">ComputeRelativeResiduals</a>(termination_criteria.eps_optimal_absolute(),</div>
|
|
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> termination_criteria.eps_optimal_relative(),</div>
|
|
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> bound_norms, *convergence_information);</div>
|
|
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> <span class="keywordflow">return</span> absl::StrCat(iteration_string, <span class="stringliteral">" | "</span>,</div>
|
|
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> <a class="code hl_function" href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">ToString</a>(*convergence_information, relative_information,</div>
|
|
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> termination_criteria.optimality_norm()));</div>
|
|
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> }</div>
|
|
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> <span class="keywordflow">return</span> iteration_string;</div>
|
|
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span>}</div>
|
|
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> </div>
|
|
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span>std::string ToShortString(<span class="keyword">const</span> IterationStats& iter_stats,</div>
|
|
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> <span class="keyword">const</span> TerminationCriteria& termination_criteria,</div>
|
|
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> <span class="keyword">const</span> QuadraticProgramBoundNorms& bound_norms,</div>
|
|
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> PointType preferred_candidate) {</div>
|
|
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> std::string iteration_string =</div>
|
|
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> absl::StrFormat(<span class="stringliteral">"%6d %6.1f"</span>, iter_stats.iteration_number(),</div>
|
|
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> iter_stats.cumulative_time_sec());</div>
|
|
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> <span class="keyword">auto</span> convergence_information =</div>
|
|
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a7b93e1d980b7d8112423361ac15a0c28">GetConvergenceInformation</a>(iter_stats, preferred_candidate);</div>
|
|
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> <span class="keywordflow">if</span> (!convergence_information.has_value() &&</div>
|
|
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> iter_stats.convergence_information_size() > 0) {</div>
|
|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> convergence_information = iter_stats.convergence_information(0);</div>
|
|
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> }</div>
|
|
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> <span class="keywordflow">if</span> (convergence_information.has_value()) {</div>
|
|
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> <span class="keyword">const</span> RelativeConvergenceInformation relative_information =</div>
|
|
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a8e307fb8ac2854dd493d52760dd3aa30">ComputeRelativeResiduals</a>(termination_criteria.eps_optimal_absolute(),</div>
|
|
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> termination_criteria.eps_optimal_relative(),</div>
|
|
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> bound_norms, *convergence_information);</div>
|
|
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> <span class="keywordflow">return</span> absl::StrCat(</div>
|
|
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> iteration_string, <span class="stringliteral">" | "</span>,</div>
|
|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> ToShortString(*convergence_information, relative_information,</div>
|
|
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> termination_criteria.optimality_norm()));</div>
|
|
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> }</div>
|
|
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> <span class="keywordflow">return</span> iteration_string;</div>
|
|
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span>}</div>
|
|
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> </div>
|
|
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span><span class="comment">// Returns a label string corresponding to the format of ToString().</span></div>
|
|
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span>std::string ConvergenceInformationLabelString() {</div>
|
|
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> <span class="keywordflow">return</span> absl::StrFormat(</div>
|
|
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> <span class="stringliteral">"%12s %12s %12s | %12s %12s %12s | %12s %12s | %12s %12s"</span>, <span class="stringliteral">"rel_prim_res"</span>,</div>
|
|
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> <span class="stringliteral">"rel_dual_res"</span>, <span class="stringliteral">"rel_gap"</span>, <span class="stringliteral">"prim_resid"</span>, <span class="stringliteral">"dual_resid"</span>, <span class="stringliteral">"obj_gap"</span>,</div>
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|
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> <span class="stringliteral">"prim_obj"</span>, <span class="stringliteral">"dual_obj"</span>, <span class="stringliteral">"prim_var_l2"</span>, <span class="stringliteral">"dual_var_l2"</span>);</div>
|
|
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span>}</div>
|
|
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> </div>
|
|
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span>std::string ConvergenceInformationLabelShortString() {</div>
|
|
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> <span class="keywordflow">return</span> absl::StrFormat(<span class="stringliteral">"%10s %10s %10s | %10s %10s"</span>, <span class="stringliteral">"rel_p_res"</span>, <span class="stringliteral">"rel_d_res"</span>,</div>
|
|
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> <span class="stringliteral">"rel_gap"</span>, <span class="stringliteral">"prim_obj"</span>, <span class="stringliteral">"dual_obj"</span>);</div>
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<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span>}</div>
|
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<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> </div>
|
|
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span>std::string IterationStatsLabelString() {</div>
|
|
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> <span class="keywordflow">return</span> absl::StrCat(</div>
|
|
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> absl::StrFormat(<span class="stringliteral">"%6s %8s %6s"</span>, <span class="stringliteral">"iter#"</span>, <span class="stringliteral">"kkt_pass"</span>, <span class="stringliteral">"time"</span>), <span class="stringliteral">" | "</span>,</div>
|
|
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> ConvergenceInformationLabelString());</div>
|
|
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span>}</div>
|
|
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> </div>
|
|
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span>std::string IterationStatsLabelShortString() {</div>
|
|
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> <span class="keywordflow">return</span> absl::StrCat(absl::StrFormat(<span class="stringliteral">"%6s %6s"</span>, <span class="stringliteral">"iter#"</span>, <span class="stringliteral">"time"</span>), <span class="stringliteral">" | "</span>,</div>
|
|
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> ConvergenceInformationLabelShortString());</div>
|
|
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span>}</div>
|
|
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span> </div>
|
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<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span><span class="keyword">enum class</span> InnerStepOutcome {</div>
|
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<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> kSuccessful,</div>
|
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<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> kForceNumericalTermination,</div>
|
|
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span>};</div>
|
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<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> </div>
|
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<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span><span class="keyword">class </span>Solver {</div>
|
|
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"> 229</span> <span class="keyword">public</span>:</div>
|
|
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> <span class="comment">// Assumes that the qp and params are valid.</span></div>
|
|
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> <span class="comment">// Note that the qp is intentionally passed by value.</span></div>
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|
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> Solver(QuadraticProgram qp, <span class="keyword">const</span> PrimalDualHybridGradientParams& params);</div>
|
|
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> </div>
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<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> <span class="comment">// Not copyable or movable because of const and reference members.</span></div>
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<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> Solver(<span class="keyword">const</span> Solver&) = <span class="keyword">delete</span>;</div>
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<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> Solver& operator=(<span class="keyword">const</span> Solver&) = <span class="keyword">delete</span>;</div>
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<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> </div>
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<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> <span class="comment">// Zero is used if initial_solution is nullopt.</span></div>
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<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> SolverResult <a class="code hl_function" href="namespaceoperations__research_1_1math__opt.html#a5767ee23f380e72488d3c7ebf2d742b1">Solve</a>(absl::optional<PrimalAndDualSolution> initial_solution,</div>
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<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> <span class="keyword">const</span> std::atomic<bool>* interrupt_solve,</div>
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<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> IterationStatsCallback iteration_stats_callback);</div>
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<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> </div>
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<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> <span class="keyword">private</span>:</div>
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<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> <span class="keyword">struct </span>NextSolutionAndDelta {</div>
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<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#a730b1ea892f1f794d9bd5f16027acb63"> 245</a></span> VectorXd <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#a730b1ea892f1f794d9bd5f16027acb63">value</a>;</div>
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<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> <span class="comment">// delta is value - current_solution.</span></div>
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<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#ae99d92906640f8755574c1f93f39d320"> 247</a></span> VectorXd <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ae99d92906640f8755574c1f93f39d320">delta</a>;</div>
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<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> };</div>
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<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> </div>
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<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> <span class="keyword">struct </span>DistanceBasedRestartInfo {</div>
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<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#ae3146d59eb9e49c48bd3ea7b3e60ab65"> 251</a></span> <span class="keywordtype">double</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ae3146d59eb9e49c48bd3ea7b3e60ab65">distance_moved_last_restart_period</a>;</div>
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<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#af6ec6f87520da8de4c5522f7bd04dfe4"> 252</a></span> <span class="keywordtype">int</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#af6ec6f87520da8de4c5522f7bd04dfe4">length_of_last_restart_period</a>;</div>
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<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> };</div>
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<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> </div>
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<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> <span class="keyword">struct </span>PresolveInfo {</div>
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<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"> 256</span> <span class="keyword">explicit</span> PresolveInfo(ShardedQuadraticProgram original_qp,</div>
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<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> <span class="keyword">const</span> PrimalDualHybridGradientParams& params)</div>
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<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> : <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#afc562f1013a6986df081403ff83fdeac">preprocessor_parameters</a>(PreprocessorParameters(params)),</div>
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<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#a1339b97193af37ff85bd41146dba5290">preprocessor</a>(&<a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#afc562f1013a6986df081403ff83fdeac">preprocessor_parameters</a>),</div>
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<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ab4d0766b8b9bbc7fdfddbafe8dda4c97">sharded_original_qp</a>(std::move(original_qp)),</div>
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<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ac7a159e390ca0bbb5d8aba647055448b">trivial_col_scaling_vec</a>(</div>
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<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> VectorXd::Ones(<a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ab4d0766b8b9bbc7fdfddbafe8dda4c97">sharded_original_qp</a>.PrimalSize())),</div>
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<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#a8684c349890b354a93a8b977029fc58d">trivial_row_scaling_vec</a>(</div>
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<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> VectorXd::Ones(<a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ab4d0766b8b9bbc7fdfddbafe8dda4c97">sharded_original_qp</a>.DualSize())) {}</div>
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<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#afc562f1013a6986df081403ff83fdeac"> 265</a></span> glop::GlopParameters <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#afc562f1013a6986df081403ff83fdeac">preprocessor_parameters</a>;</div>
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<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#a1339b97193af37ff85bd41146dba5290"> 266</a></span> glop::MainLpPreprocessor <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#a1339b97193af37ff85bd41146dba5290">preprocessor</a>;</div>
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<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#ab4d0766b8b9bbc7fdfddbafe8dda4c97"> 267</a></span> ShardedQuadraticProgram <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ab4d0766b8b9bbc7fdfddbafe8dda4c97">sharded_original_qp</a>;</div>
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<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#a00a26e2a8ca2ce9d8f5492d1722f7be7"> 268</a></span> <span class="keywordtype">bool</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#a00a26e2a8ca2ce9d8f5492d1722f7be7">presolved_problem_was_maximization</a> = <span class="keyword">false</span>;</div>
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<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"><a class="line" href="primal__dual__hybrid__gradient_8cc.html#ac7a159e390ca0bbb5d8aba647055448b"> 269</a></span> <span class="keyword">const</span> VectorXd <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ac7a159e390ca0bbb5d8aba647055448b">trivial_col_scaling_vec</a>, <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#a8684c349890b354a93a8b977029fc58d">trivial_row_scaling_vec</a>;</div>
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<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> };</div>
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<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> </div>
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<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> <span class="comment">// Movement terms (weighted squared norms of primal and dual deltas) larger</span></div>
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<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> <span class="comment">// than this cause termination because iterates are diverging, and likely to</span></div>
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<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> <span class="comment">// cause infinite and NaN values.</span></div>
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<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="keyword">constexpr</span> <span class="keyword">static</span> <span class="keywordtype">double</span> kDivergentMovement = 1.0e100;</div>
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<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> </div>
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<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> NextSolutionAndDelta ComputeNextPrimalSolution(<span class="keywordtype">double</span> primal_step_size) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> </div>
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<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> NextSolutionAndDelta ComputeNextDualSolution(</div>
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<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> <span class="keywordtype">double</span> dual_step_size, <span class="keywordtype">double</span> extrapolation_factor,</div>
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<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> <span class="keyword">const</span> NextSolutionAndDelta& next_primal) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> </div>
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<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> <span class="keywordtype">double</span> ComputeMovement(<span class="keyword">const</span> VectorXd& delta_primal,</div>
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<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span> <span class="keyword">const</span> VectorXd& delta_dual) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> </div>
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<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> <span class="keywordtype">double</span> ComputeNonlinearity(<span class="keyword">const</span> VectorXd& delta_primal,</div>
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<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> <span class="keyword">const</span> VectorXd& next_dual_product) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> </div>
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<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> <span class="comment">// Creates all the simple-to-compute statistics in stats.</span></div>
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<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> IterationStats CreateSimpleIterationStats(RestartChoice restart_used) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> </div>
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<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> RestartChoice ChooseRestartToApply(<span class="keywordtype">bool</span> is_major_iteration);</div>
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<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> </div>
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<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> VectorXd PrimalAverage() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> </div>
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<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> VectorXd DualAverage() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> </div>
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<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> <span class="keywordtype">double</span> ComputeNewPrimalWeight() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> </div>
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<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> <span class="comment">// Picks the primal and dual solutions according to output_type, unscales them</span></div>
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<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> <span class="comment">// and makes the closing changes to the SolveLog. This function should only be</span></div>
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<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> <span class="comment">// called once the solver is finishing its execution.</span></div>
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<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"> 303</span> <span class="comment">// NOTE: The primal_solution and dual_solution are used as the output except</span></div>
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<div class="line"><a id="l00304" name="l00304"></a><span class="lineno"> 304</span> <span class="comment">// when output_type is POINT_TYPE_CURRENT_ITERATE or</span></div>
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<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"> 305</span> <span class="comment">// POINT_TYPE_ITERATE_DIFFERENCE, in which case the values are computed from</span></div>
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<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span> <span class="comment">// Solver data. NOTE: Both primal_solution and dual_solution are passed by</span></div>
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<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> <span class="comment">// copy. To avoid unnecessary copying, move these objects (i.e. use</span></div>
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<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> <span class="comment">// std::move()).</span></div>
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<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> SolverResult ConstructSolverResult(VectorXd primal_solution,</div>
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<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> VectorXd dual_solution,</div>
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<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> <span class="keyword">const</span> IterationStats& stats,</div>
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<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> <a class="code hl_enumeration" href="namespaceoperations__research_1_1math__opt.html#ad02e69a0531469b463df907c7b2ad194">TerminationReason</a> termination_reason,</div>
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<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"> 313</span> PointType output_type,</div>
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<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> SolveLog solve_log) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> </div>
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<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> <span class="comment">// Adds one entry of convergence information and infeasibility information to</span></div>
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<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> <span class="comment">// stats using the input solutions. The primal_solution and dual_solution are</span></div>
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<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> <span class="comment">// solutions for sharded_qp. The col_scaling_vec and row_scaling_vec are used</span></div>
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<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> <span class="comment">// to implicitly unscale sharded_qp when computing the relevant information.</span></div>
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<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> <span class="keywordtype">void</span> AddConvergenceAndInfeasibilityInformation(</div>
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<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> <span class="keyword">const</span> VectorXd& primal_solution, <span class="keyword">const</span> VectorXd& dual_solution,</div>
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<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> <span class="keyword">const</span> ShardedQuadraticProgram& sharded_qp,</div>
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<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> <span class="keyword">const</span> VectorXd& col_scaling_vec, <span class="keyword">const</span> VectorXd& row_scaling_vec,</div>
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<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> PointType candidate_type, IterationStats& stats) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> </div>
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<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> <span class="comment">// Adds one entry of PointMetadata to stats using the input solutions.</span></div>
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<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <span class="keywordtype">void</span> AddPointMetadata(<span class="keyword">const</span> VectorXd& primal_solution,</div>
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<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> <span class="keyword">const</span> VectorXd& dual_solution, PointType point_type,</div>
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<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> IterationStats& stats) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> </div>
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<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> <span class="comment">// Returns a TerminationReasonAndPointType when the termination criteria are</span></div>
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<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="comment">// satisfied, otherwise returns nothing. Uses the primal and dual vectors to</span></div>
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<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> <span class="comment">// compute solution statistics and adds them to the stats proto.</span></div>
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<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> <span class="comment">// NOTE: The primal and dual input pair should be a scaled solution.</span></div>
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<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> absl::optional<TerminationReasonAndPointType></div>
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<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> UpdateIterationStatsAndCheckTermination(<span class="keywordtype">bool</span> force_numerical_termination,</div>
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<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span> <span class="keyword">const</span> VectorXd& primal_average,</div>
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<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> <span class="keyword">const</span> VectorXd& dual_average,</div>
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<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> IterationStats& stats) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> </div>
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<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> <span class="keywordtype">double</span> DistanceTraveledFromLastStart(<span class="keyword">const</span> VectorXd& primal_solution,</div>
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<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <span class="keyword">const</span> VectorXd& dual_solution) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> </div>
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<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> LocalizedLagrangianBounds ComputeLocalizedBoundsAtCurrent() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span> </div>
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<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> LocalizedLagrangianBounds ComputeLocalizedBoundsAtAverage() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"> 347</span> </div>
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<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span> <span class="keywordtype">double</span> InitialPrimalWeight(<span class="keywordtype">double</span> l2_norm_primal_linear_objective,</div>
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<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span> <span class="keywordtype">double</span> l2_norm_constraint_bounds) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> </div>
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<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> <span class="keywordtype">void</span> ComputeAndApplyRescaling();</div>
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<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> </div>
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<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"> 353</span> <span class="comment">// Applies the given RestartChoice. If a restart is chosen, updates the</span></div>
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<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span> <span class="comment">// state of the algorithm accordingly and computes a new primal weight.</span></div>
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<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span> <span class="keywordtype">void</span> ApplyRestartChoice(RestartChoice restart_to_apply);</div>
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<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span> </div>
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<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"> 357</span> absl::optional<SolverResult> MajorIterationAndTerminationCheck(</div>
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<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span> <span class="keywordtype">bool</span> force_numerical_termination, SolveLog& solve_log);</div>
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<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"> 359</span> </div>
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<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> <span class="keywordtype">bool</span> ShouldDoAdaptiveRestartHeuristic(<span class="keywordtype">double</span> candidate_normalized_gap) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> </div>
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<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> RestartChoice DetermineDistanceBasedRestartChoice() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> </div>
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<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span> <span class="keywordtype">void</span> ResetAverageToCurrent();</div>
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<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> </div>
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<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> <span class="keywordtype">void</span> LogNumericalTermination() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> </div>
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<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> <span class="keywordtype">void</span> LogInnerIterationLimitHit() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> </div>
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<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> <span class="keywordtype">void</span> LogQuadraticProgramStats(<span class="keyword">const</span> QuadraticProgramStats& stats);</div>
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<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> </div>
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<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span> <span class="comment">// Takes a step based on the Malitsky and Pock linesearch algorithm.</span></div>
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<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span> <span class="comment">// (https://arxiv.org/pdf/1608.08883.pdf)</span></div>
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<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span> <span class="comment">// The current implementation is provably convergent (at an optimal rate)</span></div>
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<div class="line"><a id="l00375" name="l00375"></a><span class="lineno"> 375</span> <span class="comment">// for LP programs (provided we do not change the primal weight at every major</span></div>
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<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span> <span class="comment">// iteration). Further, we have observed that this rule is very sensitive to</span></div>
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<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> <span class="comment">// the parameter choice whenever we apply the primal weight recomputation</span></div>
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<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span> <span class="comment">// heuristic.</span></div>
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<div class="line"><a id="l00379" name="l00379"></a><span class="lineno"> 379</span> InnerStepOutcome TakeMalitskyPockStep();</div>
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<div class="line"><a id="l00380" name="l00380"></a><span class="lineno"> 380</span> </div>
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<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span> <span class="comment">// Takes a step based on the adaptive heuristic presented in Section 3.1 of</span></div>
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<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"> 382</span> <span class="comment">// https://arxiv.org/pdf/2106.04756.pdf (further generalized to QP).</span></div>
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<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span> InnerStepOutcome TakeAdaptiveStep();</div>
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<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> </div>
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<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span> <span class="comment">// Takes a constant-size step.</span></div>
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<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span> InnerStepOutcome TakeConstantSizeStep();</div>
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<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span> </div>
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<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> <span class="keyword">const</span> QuadraticProgram& WorkingQp()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> sharded_working_qp_.Qp(); }</div>
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<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> </div>
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<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span> <span class="comment">// TODO(user): experiment with different preprocessor types.</span></div>
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<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span> <span class="keyword">static</span> glop::GlopParameters PreprocessorParameters(</div>
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<div class="line"><a id="l00392" name="l00392"></a><span class="lineno"> 392</span> <span class="keyword">const</span> PrimalDualHybridGradientParams& params);</div>
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<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"> 393</span> </div>
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<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> <span class="comment">// If presolve is enabled, moves sharded_working_qp_ to</span></div>
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<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"> 395</span> <span class="comment">// presolve_info_.sharded_original_qp and computes the presolved linear</span></div>
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<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"> 396</span> <span class="comment">// program and installs it in sharded_working_qp_. Clears initial_solution if</span></div>
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<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span> <span class="comment">// presolve is enabled. If presolve solves the problem completely returns the</span></div>
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<div class="line"><a id="l00398" name="l00398"></a><span class="lineno"> 398</span> <span class="comment">// appropriate TerminationReason. Otherwise returns nullopt. If presolve</span></div>
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<div class="line"><a id="l00399" name="l00399"></a><span class="lineno"> 399</span> <span class="comment">// is disabled or an error occurs modifies nothing and returns nullopt.</span></div>
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<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"> 400</span> absl::optional<TerminationReason> ApplyPresolveIfEnabled(</div>
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<div class="line"><a id="l00401" name="l00401"></a><span class="lineno"> 401</span> absl::optional<PrimalAndDualSolution>* initial_solution);</div>
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<div class="line"><a id="l00402" name="l00402"></a><span class="lineno"> 402</span> </div>
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<div class="line"><a id="l00403" name="l00403"></a><span class="lineno"> 403</span> PrimalAndDualSolution RecoverOriginalSolution(</div>
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<div class="line"><a id="l00404" name="l00404"></a><span class="lineno"> 404</span> PrimalAndDualSolution working_solution) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span> </div>
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<div class="line"><a id="l00406" name="l00406"></a><span class="lineno"> 406</span> <a class="code hl_class" href="class_wall_timer.html">WallTimer</a> timer_;</div>
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<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"> 407</span> <span class="keyword">const</span> PrimalDualHybridGradientParams params_;</div>
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<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"> 408</span> <span class="keyword">const</span> <span class="keywordtype">int</span> num_shards_;</div>
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<div class="line"><a id="l00409" name="l00409"></a><span class="lineno"> 409</span> <span class="comment">// This is the QP that PDHG is run on. It has been reduced by presolve and/or</span></div>
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<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"> 410</span> <span class="comment">// rescaled, if those are enabled. The original problem is available in</span></div>
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<div class="line"><a id="l00411" name="l00411"></a><span class="lineno"> 411</span> <span class="comment">// presolve_info_->sharded_original_qp if presolve_info_.has_value(), and</span></div>
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<div class="line"><a id="l00412" name="l00412"></a><span class="lineno"> 412</span> <span class="comment">// otherwise can be obtained by undoing the scaling of sharded_working_qp_ by</span></div>
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<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"> 413</span> <span class="comment">// col_scaling_vec_ and row_scaling_vec_.</span></div>
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<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"> 414</span> ShardedQuadraticProgram sharded_working_qp_;</div>
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<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> ShardedWeightedAverage primal_average_;</div>
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<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> ShardedWeightedAverage dual_average_;</div>
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<div class="line"><a id="l00417" name="l00417"></a><span class="lineno"> 417</span> IterationStatsCallback iteration_stats_callback_;</div>
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<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"> 418</span> QuadraticProgramBoundNorms original_bound_norms_;</div>
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<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> </div>
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<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> <span class="comment">// Set iff presolve is enabled.</span></div>
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<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span> absl::optional<PresolveInfo> presolve_info_;</div>
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<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> </div>
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<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span> <span class="keywordtype">double</span> step_size_;</div>
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<div class="line"><a id="l00424" name="l00424"></a><span class="lineno"> 424</span> <span class="comment">// For Malitsky-Pock linesearch only: step_size_ / previous_step_size</span></div>
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<div class="line"><a id="l00425" name="l00425"></a><span class="lineno"> 425</span> <span class="keywordtype">double</span> ratio_last_two_step_sizes_;</div>
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<div class="line"><a id="l00426" name="l00426"></a><span class="lineno"> 426</span> <span class="keywordtype">double</span> primal_weight_;</div>
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<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"> 427</span> <span class="comment">// For adaptive restarts only.</span></div>
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<div class="line"><a id="l00428" name="l00428"></a><span class="lineno"> 428</span> <span class="keywordtype">double</span> normalized_gap_at_last_trial_ =</div>
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<div class="line"><a id="l00429" name="l00429"></a><span class="lineno"> 429</span> std::numeric_limits<double>::infinity();</div>
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<div class="line"><a id="l00430" name="l00430"></a><span class="lineno"> 430</span> <span class="comment">// For adaptive restarts only.</span></div>
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<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"> 431</span> <span class="keywordtype">double</span> normalized_gap_at_last_restart_ =</div>
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<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"> 432</span> std::numeric_limits<double>::infinity();</div>
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<div class="line"><a id="l00433" name="l00433"></a><span class="lineno"> 433</span> <span class="keywordtype">int</span> iterations_completed_;</div>
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<div class="line"><a id="l00434" name="l00434"></a><span class="lineno"> 434</span> <span class="keywordtype">int</span> num_rejected_steps_;</div>
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<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> VectorXd current_primal_solution_;</div>
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<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"> 436</span> VectorXd current_dual_solution_;</div>
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<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"> 437</span> VectorXd current_primal_delta_;</div>
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<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"> 438</span> VectorXd current_dual_delta_;</div>
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<div class="line"><a id="l00439" name="l00439"></a><span class="lineno"> 439</span> <span class="comment">// A cache of constraint_matrix.transpose() * current_dual_solution.</span></div>
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<div class="line"><a id="l00440" name="l00440"></a><span class="lineno"> 440</span> VectorXd current_dual_product_;</div>
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<div class="line"><a id="l00441" name="l00441"></a><span class="lineno"> 441</span> <span class="comment">// The primal point at which the algorithm was last restarted from, or</span></div>
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<div class="line"><a id="l00442" name="l00442"></a><span class="lineno"> 442</span> <span class="comment">// the initial primal starting point if no restart has occurred.</span></div>
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<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"> 443</span> VectorXd last_primal_start_point_;</div>
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<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"> 444</span> <span class="comment">// The dual point at which the algorithm was last restarted from, or</span></div>
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<div class="line"><a id="l00445" name="l00445"></a><span class="lineno"> 445</span> <span class="comment">// the initial dual starting point if no restart has occurred.</span></div>
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<div class="line"><a id="l00446" name="l00446"></a><span class="lineno"> 446</span> VectorXd last_dual_start_point_;</div>
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<div class="line"><a id="l00447" name="l00447"></a><span class="lineno"> 447</span> <span class="comment">// Information for deciding whether to trigger a distance-based restart.</span></div>
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<div class="line"><a id="l00448" name="l00448"></a><span class="lineno"> 448</span> <span class="comment">// The distances are initialized to +inf to force a restart during the first</span></div>
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<div class="line"><a id="l00449" name="l00449"></a><span class="lineno"> 449</span> <span class="comment">// major iteration check.</span></div>
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<div class="line"><a id="l00450" name="l00450"></a><span class="lineno"> 450</span> DistanceBasedRestartInfo distance_based_restart_info_ = {</div>
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<div class="line"><a id="l00451" name="l00451"></a><span class="lineno"> 451</span> .distance_moved_last_restart_period =</div>
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<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"> 452</span> std::numeric_limits<double>::infinity(),</div>
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<div class="line"><a id="l00453" name="l00453"></a><span class="lineno"> 453</span> .length_of_last_restart_period = 1,</div>
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<div class="line"><a id="l00454" name="l00454"></a><span class="lineno"> 454</span> };</div>
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<div class="line"><a id="l00455" name="l00455"></a><span class="lineno"> 455</span> <span class="comment">// The scaling vectors that map the original (or presolved) quadratic program</span></div>
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<div class="line"><a id="l00456" name="l00456"></a><span class="lineno"> 456</span> <span class="comment">// to the working version. See</span></div>
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<div class="line"><a id="l00457" name="l00457"></a><span class="lineno"> 457</span> <span class="comment">// ShardedQuadraticProgram::RescaleQuadraticProgram() for details.</span></div>
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<div class="line"><a id="l00458" name="l00458"></a><span class="lineno"> 458</span> VectorXd col_scaling_vec_;</div>
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<div class="line"><a id="l00459" name="l00459"></a><span class="lineno"> 459</span> VectorXd row_scaling_vec_;</div>
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<div class="line"><a id="l00460" name="l00460"></a><span class="lineno"> 460</span>};</div>
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<div class="line"><a id="l00461" name="l00461"></a><span class="lineno"> 461</span> </div>
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<div class="line"><a id="l00462" name="l00462"></a><span class="lineno"> 462</span><a class="code hl_function" href="classoperations__research_1_1_solver.html#abac10873a1af49f1dce33a34f3afaa56">Solver::Solver</a>(QuadraticProgram qp,</div>
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<div class="line"><a id="l00463" name="l00463"></a><span class="lineno"> 463</span> <span class="keyword">const</span> PrimalDualHybridGradientParams& params)</div>
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<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"> 464</span> : params_(params),</div>
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<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> num_shards_(NumShards(params.num_shards(), params.num_threads())),</div>
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<div class="line"><a id="l00466" name="l00466"></a><span class="lineno"> 466</span> sharded_working_qp_(<a class="code hl_namespace" href="namespacestd.html">std</a>::move(qp), params.num_threads(), num_shards_),</div>
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<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"> 467</span> primal_average_(&sharded_working_qp_.PrimalSharder()),</div>
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<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> dual_average_(&sharded_working_qp_.DualSharder()) {}</div>
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<div class="line"><a id="l00469" name="l00469"></a><span class="lineno"> 469</span> </div>
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<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span>Solver::NextSolutionAndDelta Solver::ComputeNextPrimalSolution(</div>
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<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> <span class="keywordtype">double</span> primal_step_size)<span class="keyword"> const </span>{</div>
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<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> <span class="keyword">const</span> int64_t primal_size = sharded_working_qp_.PrimalSize();</div>
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<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> NextSolutionAndDelta result = {</div>
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<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> .value = VectorXd(primal_size),</div>
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<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span> .delta = VectorXd(primal_size),</div>
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<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"> 476</span> };</div>
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<div class="line"><a id="l00477" name="l00477"></a><span class="lineno"> 477</span> <span class="keyword">const</span> QuadraticProgram& qp = WorkingQp();</div>
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<div class="line"><a id="l00478" name="l00478"></a><span class="lineno"> 478</span> <span class="comment">// This computes the primal portion of the PDHG algorithm:</span></div>
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<div class="line"><a id="l00479" name="l00479"></a><span class="lineno"> 479</span> <span class="comment">// argmin_x[gradient(f)(current_primal_solution)'x + g(x)</span></div>
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<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"> 480</span> <span class="comment">// + current_dual_solution' K x</span></div>
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<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"> 481</span> <span class="comment">// + (0.5 / primal_step_size) * norm(x - current_primal_solution) ^ 2]</span></div>
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<div class="line"><a id="l00482" name="l00482"></a><span class="lineno"> 482</span> <span class="comment">// See Sections 2 - 3 of Chambolle and Pock and the comment in the header.</span></div>
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<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"> 483</span> <span class="comment">// We omitted the constant terms from Chambolle and Pock's (7).</span></div>
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<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"> 484</span> <span class="comment">// This minimization is easy to do in closed form since it can be separated</span></div>
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<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"> 485</span> <span class="comment">// into independent problems for each of the primal variables.</span></div>
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<div class="line"><a id="l00486" name="l00486"></a><span class="lineno"> 486</span> sharded_working_qp_.PrimalSharder().ParallelForEachShard(</div>
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<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"> 487</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
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<div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span> <span class="keywordflow">if</span> (!<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a850865b3deabb2a623e130691df99f15">IsLinearProgram</a>(qp)) {</div>
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<div class="line"><a id="l00489" name="l00489"></a><span class="lineno"> 489</span> <span class="keyword">const</span> VectorXd diagonal_scaling =</div>
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<div class="line"><a id="l00490" name="l00490"></a><span class="lineno"> 490</span> primal_step_size *</div>
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<div class="line"><a id="l00491" name="l00491"></a><span class="lineno"> 491</span> shard(qp.objective_matrix->diagonal()).array() +</div>
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<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"> 492</span> 1.0;</div>
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<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span> shard(result.value) =</div>
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<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"> 494</span> (shard(current_primal_solution_) -</div>
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<div class="line"><a id="l00495" name="l00495"></a><span class="lineno"> 495</span> primal_step_size *</div>
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<div class="line"><a id="l00496" name="l00496"></a><span class="lineno"> 496</span> (shard(qp.objective_vector) - shard(current_dual_product_)))</div>
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<div class="line"><a id="l00497" name="l00497"></a><span class="lineno"> 497</span> <span class="comment">// Scale i-th element by 1 / (1 + primal_step_size * Q_{ii})</span></div>
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<div class="line"><a id="l00498" name="l00498"></a><span class="lineno"> 498</span> .cwiseQuotient(diagonal_scaling)</div>
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<div class="line"><a id="l00499" name="l00499"></a><span class="lineno"> 499</span> .cwiseMin(shard(qp.variable_upper_bounds))</div>
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<div class="line"><a id="l00500" name="l00500"></a><span class="lineno"> 500</span> .cwiseMax(shard(qp.variable_lower_bounds));</div>
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<div class="line"><a id="l00501" name="l00501"></a><span class="lineno"> 501</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l00502" name="l00502"></a><span class="lineno"> 502</span> <span class="comment">// The formula in the LP case is simplified for better performance.</span></div>
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<div class="line"><a id="l00503" name="l00503"></a><span class="lineno"> 503</span> shard(result.value) =</div>
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<div class="line"><a id="l00504" name="l00504"></a><span class="lineno"> 504</span> (shard(current_primal_solution_) -</div>
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<div class="line"><a id="l00505" name="l00505"></a><span class="lineno"> 505</span> primal_step_size *</div>
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<div class="line"><a id="l00506" name="l00506"></a><span class="lineno"> 506</span> (shard(qp.objective_vector) - shard(current_dual_product_)))</div>
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<div class="line"><a id="l00507" name="l00507"></a><span class="lineno"> 507</span> .cwiseMin(shard(qp.variable_upper_bounds))</div>
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<div class="line"><a id="l00508" name="l00508"></a><span class="lineno"> 508</span> .cwiseMax(shard(qp.variable_lower_bounds));</div>
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<div class="line"><a id="l00509" name="l00509"></a><span class="lineno"> 509</span> }</div>
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<div class="line"><a id="l00510" name="l00510"></a><span class="lineno"> 510</span> shard(result.delta) =</div>
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<div class="line"><a id="l00511" name="l00511"></a><span class="lineno"> 511</span> shard(result.value) - shard(current_primal_solution_);</div>
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<div class="line"><a id="l00512" name="l00512"></a><span class="lineno"> 512</span> });</div>
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<div class="line"><a id="l00513" name="l00513"></a><span class="lineno"> 513</span> <span class="keywordflow">return</span> result;</div>
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<div class="line"><a id="l00514" name="l00514"></a><span class="lineno"> 514</span>}</div>
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<div class="line"><a id="l00515" name="l00515"></a><span class="lineno"> 515</span> </div>
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<div class="line"><a id="l00516" name="l00516"></a><span class="lineno"> 516</span>Solver::NextSolutionAndDelta Solver::ComputeNextDualSolution(</div>
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<div class="line"><a id="l00517" name="l00517"></a><span class="lineno"> 517</span> <span class="keywordtype">double</span> dual_step_size, <span class="keywordtype">double</span> extrapolation_factor,</div>
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<div class="line"><a id="l00518" name="l00518"></a><span class="lineno"> 518</span> <span class="keyword">const</span> NextSolutionAndDelta& next_primal_solution)<span class="keyword"> const </span>{</div>
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<div class="line"><a id="l00519" name="l00519"></a><span class="lineno"> 519</span> <span class="keyword">const</span> int64_t dual_size = sharded_working_qp_.DualSize();</div>
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<div class="line"><a id="l00520" name="l00520"></a><span class="lineno"> 520</span> NextSolutionAndDelta result = {</div>
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<div class="line"><a id="l00521" name="l00521"></a><span class="lineno"> 521</span> .value = VectorXd(dual_size),</div>
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<div class="line"><a id="l00522" name="l00522"></a><span class="lineno"> 522</span> .delta = VectorXd(dual_size),</div>
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<div class="line"><a id="l00523" name="l00523"></a><span class="lineno"> 523</span> };</div>
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<div class="line"><a id="l00524" name="l00524"></a><span class="lineno"> 524</span> <span class="keyword">const</span> QuadraticProgram& qp = WorkingQp();</div>
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<div class="line"><a id="l00525" name="l00525"></a><span class="lineno"> 525</span> VectorXd extrapolated_primal(sharded_working_qp_.PrimalSize());</div>
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<div class="line"><a id="l00526" name="l00526"></a><span class="lineno"> 526</span> sharded_working_qp_.PrimalSharder().ParallelForEachShard(</div>
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<div class="line"><a id="l00527" name="l00527"></a><span class="lineno"> 527</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
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<div class="line"><a id="l00528" name="l00528"></a><span class="lineno"> 528</span> shard(extrapolated_primal) =</div>
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<div class="line"><a id="l00529" name="l00529"></a><span class="lineno"> 529</span> (shard(next_primal_solution.value) +</div>
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<div class="line"><a id="l00530" name="l00530"></a><span class="lineno"> 530</span> extrapolation_factor * shard(next_primal_solution.delta));</div>
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<div class="line"><a id="l00531" name="l00531"></a><span class="lineno"> 531</span> });</div>
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<div class="line"><a id="l00532" name="l00532"></a><span class="lineno"> 532</span> <span class="comment">// TODO(user): Refactor this multiplication so that we only do one matrix</span></div>
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<div class="line"><a id="l00533" name="l00533"></a><span class="lineno"> 533</span> <span class="comment">// vector mutiply for the primal variable. This only applies to Malitsky and</span></div>
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<div class="line"><a id="l00534" name="l00534"></a><span class="lineno"> 534</span> <span class="comment">// Pock and not to the adaptive step size rule.</span></div>
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|
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno"> 535</span> sharded_working_qp_.TransposedConstraintMatrixSharder().ParallelForEachShard(</div>
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<div class="line"><a id="l00536" name="l00536"></a><span class="lineno"> 536</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
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<div class="line"><a id="l00537" name="l00537"></a><span class="lineno"> 537</span> VectorXd temp =</div>
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<div class="line"><a id="l00538" name="l00538"></a><span class="lineno"> 538</span> shard(current_dual_solution_) -</div>
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|
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno"> 539</span> dual_step_size *</div>
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|
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno"> 540</span> shard(sharded_working_qp_.TransposedConstraintMatrix())</div>
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|
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno"> 541</span> .transpose() *</div>
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|
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno"> 542</span> extrapolated_primal;</div>
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<div class="line"><a id="l00543" name="l00543"></a><span class="lineno"> 543</span> <span class="comment">// Each element of the argument of the cwiseMin is the critical point</span></div>
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<div class="line"><a id="l00544" name="l00544"></a><span class="lineno"> 544</span> <span class="comment">// of the respective 1D minimization problem if it's negative.</span></div>
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|
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno"> 545</span> <span class="comment">// Likewise the argument to the cwiseMax is the critical point if</span></div>
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<div class="line"><a id="l00546" name="l00546"></a><span class="lineno"> 546</span> <span class="comment">// positive.</span></div>
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<div class="line"><a id="l00547" name="l00547"></a><span class="lineno"> 547</span> shard(result.value) =</div>
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<div class="line"><a id="l00548" name="l00548"></a><span class="lineno"> 548</span> <a class="code hl_function" href="namespaceoperations__research.html#a5a9881f8a07b166ef2cbde572cea27b6">VectorXd::Zero</a>(temp.size())</div>
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<div class="line"><a id="l00549" name="l00549"></a><span class="lineno"> 549</span> .cwiseMin(temp +</div>
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<div class="line"><a id="l00550" name="l00550"></a><span class="lineno"> 550</span> dual_step_size * shard(qp.constraint_upper_bounds))</div>
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<div class="line"><a id="l00551" name="l00551"></a><span class="lineno"> 551</span> .cwiseMax(temp +</div>
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<div class="line"><a id="l00552" name="l00552"></a><span class="lineno"> 552</span> dual_step_size * shard(qp.constraint_lower_bounds));</div>
|
|
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno"> 553</span> shard(result.delta) =</div>
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<div class="line"><a id="l00554" name="l00554"></a><span class="lineno"> 554</span> (shard(result.value) - shard(current_dual_solution_));</div>
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<div class="line"><a id="l00555" name="l00555"></a><span class="lineno"> 555</span> });</div>
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|
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno"> 556</span> <span class="keywordflow">return</span> result;</div>
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|
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno"> 557</span>}</div>
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|
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno"> 558</span> </div>
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<div class="line"><a id="l00559" name="l00559"></a><span class="lineno"> 559</span><span class="keywordtype">double</span> Solver::ComputeMovement(<span class="keyword">const</span> VectorXd& delta_primal,</div>
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<div class="line"><a id="l00560" name="l00560"></a><span class="lineno"> 560</span> <span class="keyword">const</span> VectorXd& delta_dual)<span class="keyword"> const </span>{</div>
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<div class="line"><a id="l00561" name="l00561"></a><span class="lineno"> 561</span> <span class="keyword">const</span> <span class="keywordtype">double</span> primal_movement =</div>
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<div class="line"><a id="l00562" name="l00562"></a><span class="lineno"> 562</span> (0.5 * primal_weight_) *</div>
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<div class="line"><a id="l00563" name="l00563"></a><span class="lineno"> 563</span> <a class="code hl_function" href="namespaceoperations__research_1_1glop.html#a2d53948bf5e999d006e781105aa8bc77">SquaredNorm</a>(delta_primal, sharded_working_qp_.PrimalSharder());</div>
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<div class="line"><a id="l00564" name="l00564"></a><span class="lineno"> 564</span> <span class="keyword">const</span> <span class="keywordtype">double</span> dual_movement =</div>
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<div class="line"><a id="l00565" name="l00565"></a><span class="lineno"> 565</span> (0.5 / primal_weight_) *</div>
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<div class="line"><a id="l00566" name="l00566"></a><span class="lineno"> 566</span> <a class="code hl_function" href="namespaceoperations__research_1_1glop.html#a2d53948bf5e999d006e781105aa8bc77">SquaredNorm</a>(delta_dual, sharded_working_qp_.DualSharder());</div>
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<div class="line"><a id="l00567" name="l00567"></a><span class="lineno"> 567</span> <span class="keywordflow">return</span> primal_movement + dual_movement;</div>
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|
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno"> 568</span>}</div>
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|
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno"> 569</span> </div>
|
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<div class="line"><a id="l00570" name="l00570"></a><span class="lineno"> 570</span><span class="keywordtype">double</span> Solver::ComputeNonlinearity(<span class="keyword">const</span> VectorXd& delta_primal,</div>
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<div class="line"><a id="l00571" name="l00571"></a><span class="lineno"> 571</span> <span class="keyword">const</span> VectorXd& next_dual_product)<span class="keyword"> const </span>{</div>
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<div class="line"><a id="l00572" name="l00572"></a><span class="lineno"> 572</span> <span class="comment">// Lemma 1 in Chambolle and Pock includes a term with L_f, the Lipshitz</span></div>
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<div class="line"><a id="l00573" name="l00573"></a><span class="lineno"> 573</span> <span class="comment">// constant of f. This is zero in our formulation.</span></div>
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|
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno"> 574</span> <span class="keywordflow">return</span> sharded_working_qp_.PrimalSharder().ParallelSumOverShards(</div>
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|
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno"> 575</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
|
|
<div class="line"><a id="l00576" name="l00576"></a><span class="lineno"> 576</span> <span class="keywordflow">return</span> -shard(delta_primal)</div>
|
|
<div class="line"><a id="l00577" name="l00577"></a><span class="lineno"> 577</span> .dot(shard(next_dual_product) -</div>
|
|
<div class="line"><a id="l00578" name="l00578"></a><span class="lineno"> 578</span> shard(current_dual_product_));</div>
|
|
<div class="line"><a id="l00579" name="l00579"></a><span class="lineno"> 579</span> });</div>
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|
<div class="line"><a id="l00580" name="l00580"></a><span class="lineno"> 580</span>}</div>
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<div class="line"><a id="l00581" name="l00581"></a><span class="lineno"> 581</span> </div>
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<div class="line"><a id="l00582" name="l00582"></a><span class="lineno"> 582</span>IterationStats Solver::CreateSimpleIterationStats(</div>
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<div class="line"><a id="l00583" name="l00583"></a><span class="lineno"> 583</span> RestartChoice restart_used)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00584" name="l00584"></a><span class="lineno"> 584</span> IterationStats stats;</div>
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|
<div class="line"><a id="l00585" name="l00585"></a><span class="lineno"> 585</span> <span class="keywordtype">double</span> num_kkt_passes_per_rejected_step = 1.0;</div>
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<div class="line"><a id="l00586" name="l00586"></a><span class="lineno"> 586</span> <span class="keywordflow">if</span> (params_.linesearch_rule() ==</div>
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<div class="line"><a id="l00587" name="l00587"></a><span class="lineno"> 587</span> PrimalDualHybridGradientParams::MALITSKY_POCK_LINESEARCH_RULE) {</div>
|
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<div class="line"><a id="l00588" name="l00588"></a><span class="lineno"> 588</span> num_kkt_passes_per_rejected_step = 0.5;</div>
|
|
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno"> 589</span> }</div>
|
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<div class="line"><a id="l00590" name="l00590"></a><span class="lineno"> 590</span> stats.set_iteration_number(iterations_completed_);</div>
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<div class="line"><a id="l00591" name="l00591"></a><span class="lineno"> 591</span> stats.set_cumulative_rejected_steps(num_rejected_steps_);</div>
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<div class="line"><a id="l00592" name="l00592"></a><span class="lineno"> 592</span> <span class="comment">// TODO(user): This formula doesn't account for kkt passes in major</span></div>
|
|
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno"> 593</span> <span class="comment">// iterations.</span></div>
|
|
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno"> 594</span> stats.set_cumulative_kkt_matrix_passes(iterations_completed_ +</div>
|
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<div class="line"><a id="l00595" name="l00595"></a><span class="lineno"> 595</span> num_kkt_passes_per_rejected_step *</div>
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<div class="line"><a id="l00596" name="l00596"></a><span class="lineno"> 596</span> num_rejected_steps_);</div>
|
|
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno"> 597</span> stats.set_cumulative_time_sec(timer_.<a class="code hl_function" href="class_wall_timer.html#aec56fe080959ecebec3feaed9dafde84">Get</a>());</div>
|
|
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno"> 598</span> stats.set_restart_used(restart_used);</div>
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|
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno"> 599</span> stats.set_step_size(step_size_);</div>
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<div class="line"><a id="l00600" name="l00600"></a><span class="lineno"> 600</span> stats.set_primal_weight(primal_weight_);</div>
|
|
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno"> 601</span> <span class="keywordflow">return</span> stats;</div>
|
|
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno"> 602</span>}</div>
|
|
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno"> 603</span> </div>
|
|
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno"> 604</span><span class="keywordtype">double</span> Solver::DistanceTraveledFromLastStart(</div>
|
|
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno"> 605</span> <span class="keyword">const</span> VectorXd& primal_solution, <span class="keyword">const</span> VectorXd& dual_solution)<span class="keyword"> const </span>{</div>
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<div class="line"><a id="l00606" name="l00606"></a><span class="lineno"> 606</span> <span class="keywordflow">return</span> std::sqrt((0.5 * primal_weight_) *</div>
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|
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno"> 607</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a32389515e696df20cec86493cf9852e6">SquaredDistance</a>(primal_solution,</div>
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|
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno"> 608</span> last_primal_start_point_,</div>
|
|
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno"> 609</span> sharded_working_qp_.PrimalSharder()) +</div>
|
|
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno"> 610</span> (0.5 / primal_weight_) *</div>
|
|
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno"> 611</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a32389515e696df20cec86493cf9852e6">SquaredDistance</a>(dual_solution, last_dual_start_point_,</div>
|
|
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno"> 612</span> sharded_working_qp_.DualSharder()));</div>
|
|
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno"> 613</span>}</div>
|
|
<div class="line"><a id="l00614" name="l00614"></a><span class="lineno"> 614</span> </div>
|
|
<div class="line"><a id="l00615" name="l00615"></a><span class="lineno"> 615</span>LocalizedLagrangianBounds Solver::ComputeLocalizedBoundsAtCurrent()<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno"> 616</span> <span class="keyword">const</span> <span class="keywordtype">double</span> distance_traveled_by_current = DistanceTraveledFromLastStart(</div>
|
|
<div class="line"><a id="l00617" name="l00617"></a><span class="lineno"> 617</span> current_primal_solution_, current_dual_solution_);</div>
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|
<div class="line"><a id="l00618" name="l00618"></a><span class="lineno"> 618</span> <span class="keywordflow">return</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#adb77e7cede2fecf6bccfa93226b49c0b">ComputeLocalizedLagrangianBounds</a>(</div>
|
|
<div class="line"><a id="l00619" name="l00619"></a><span class="lineno"> 619</span> sharded_working_qp_, current_primal_solution_, current_dual_solution_,</div>
|
|
<div class="line"><a id="l00620" name="l00620"></a><span class="lineno"> 620</span> PrimalDualNorm::kEuclideanNorm, primal_weight_,</div>
|
|
<div class="line"><a id="l00621" name="l00621"></a><span class="lineno"> 621</span> distance_traveled_by_current,</div>
|
|
<div class="line"><a id="l00622" name="l00622"></a><span class="lineno"> 622</span> <span class="comment">/*primal_product=*/</span><span class="keyword">nullptr</span>, &current_dual_product_,</div>
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|
<div class="line"><a id="l00623" name="l00623"></a><span class="lineno"> 623</span> params_.use_diagonal_qp_trust_region_solver(),</div>
|
|
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno"> 624</span> params_.diagonal_qp_trust_region_solver_tolerance());</div>
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<div class="line"><a id="l00625" name="l00625"></a><span class="lineno"> 625</span>}</div>
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<div class="line"><a id="l00626" name="l00626"></a><span class="lineno"> 626</span> </div>
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<div class="line"><a id="l00627" name="l00627"></a><span class="lineno"> 627</span>LocalizedLagrangianBounds Solver::ComputeLocalizedBoundsAtAverage()<span class="keyword"> const </span>{</div>
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<div class="line"><a id="l00628" name="l00628"></a><span class="lineno"> 628</span> <span class="comment">// TODO(user): These vectors are recomputed again for termination checks</span></div>
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<div class="line"><a id="l00629" name="l00629"></a><span class="lineno"> 629</span> <span class="comment">// and again if we eventually restart to the average.</span></div>
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<div class="line"><a id="l00630" name="l00630"></a><span class="lineno"> 630</span> VectorXd average_primal = PrimalAverage();</div>
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<div class="line"><a id="l00631" name="l00631"></a><span class="lineno"> 631</span> VectorXd average_dual = DualAverage();</div>
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<div class="line"><a id="l00632" name="l00632"></a><span class="lineno"> 632</span> </div>
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<div class="line"><a id="l00633" name="l00633"></a><span class="lineno"> 633</span> <span class="keyword">const</span> <span class="keywordtype">double</span> distance_traveled_by_average =</div>
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<div class="line"><a id="l00634" name="l00634"></a><span class="lineno"> 634</span> DistanceTraveledFromLastStart(average_primal, average_dual);</div>
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<div class="line"><a id="l00635" name="l00635"></a><span class="lineno"> 635</span> </div>
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<div class="line"><a id="l00636" name="l00636"></a><span class="lineno"> 636</span> <span class="keywordflow">return</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#adb77e7cede2fecf6bccfa93226b49c0b">ComputeLocalizedLagrangianBounds</a>(</div>
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<div class="line"><a id="l00637" name="l00637"></a><span class="lineno"> 637</span> sharded_working_qp_, average_primal, average_dual,</div>
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<div class="line"><a id="l00638" name="l00638"></a><span class="lineno"> 638</span> PrimalDualNorm::kEuclideanNorm, primal_weight_,</div>
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<div class="line"><a id="l00639" name="l00639"></a><span class="lineno"> 639</span> distance_traveled_by_average,</div>
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<div class="line"><a id="l00640" name="l00640"></a><span class="lineno"> 640</span> <span class="comment">/*primal_product=*/</span><span class="keyword">nullptr</span>, <span class="comment">/*dual_product=*/</span><span class="keyword">nullptr</span>,</div>
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<div class="line"><a id="l00641" name="l00641"></a><span class="lineno"> 641</span> params_.use_diagonal_qp_trust_region_solver(),</div>
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<div class="line"><a id="l00642" name="l00642"></a><span class="lineno"> 642</span> params_.diagonal_qp_trust_region_solver_tolerance());</div>
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<div class="line"><a id="l00643" name="l00643"></a><span class="lineno"> 643</span>}</div>
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<div class="line"><a id="l00644" name="l00644"></a><span class="lineno"> 644</span> </div>
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<div class="line"><a id="l00645" name="l00645"></a><span class="lineno"> 645</span><span class="keywordtype">bool</span> AverageHasBetterPotential(</div>
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<div class="line"><a id="l00646" name="l00646"></a><span class="lineno"> 646</span> <span class="keyword">const</span> LocalizedLagrangianBounds& local_bounds_at_average,</div>
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<div class="line"><a id="l00647" name="l00647"></a><span class="lineno"> 647</span> <span class="keyword">const</span> LocalizedLagrangianBounds& local_bounds_at_current) {</div>
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<div class="line"><a id="l00648" name="l00648"></a><span class="lineno"> 648</span> <span class="keywordflow">return</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afdd1506c32f697aeb13c4b9a9f05ba03">BoundGap</a>(local_bounds_at_average) /</div>
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<div class="line"><a id="l00649" name="l00649"></a><span class="lineno"> 649</span> <a class="code hl_function" href="namespaceoperations__research_1_1glop.html#a1dcd08b0f6c19cd4a302bb5a3a6ea06e">MathUtil::Square</a>(local_bounds_at_average.radius) <</div>
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<div class="line"><a id="l00650" name="l00650"></a><span class="lineno"> 650</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afdd1506c32f697aeb13c4b9a9f05ba03">BoundGap</a>(local_bounds_at_current) /</div>
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<div class="line"><a id="l00651" name="l00651"></a><span class="lineno"> 651</span> <a class="code hl_function" href="namespaceoperations__research_1_1glop.html#a1dcd08b0f6c19cd4a302bb5a3a6ea06e">MathUtil::Square</a>(local_bounds_at_current.radius);</div>
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<div class="line"><a id="l00652" name="l00652"></a><span class="lineno"> 652</span>}</div>
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<div class="line"><a id="l00653" name="l00653"></a><span class="lineno"> 653</span> </div>
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<div class="line"><a id="l00654" name="l00654"></a><span class="lineno"> 654</span><span class="keywordtype">double</span> NormalizedGap(</div>
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<div class="line"><a id="l00655" name="l00655"></a><span class="lineno"> 655</span> <span class="keyword">const</span> LocalizedLagrangianBounds& local_bounds_at_candidate) {</div>
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<div class="line"><a id="l00656" name="l00656"></a><span class="lineno"> 656</span> <span class="keyword">const</span> <span class="keywordtype">double</span> distance_traveled_by_candidate =</div>
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<div class="line"><a id="l00657" name="l00657"></a><span class="lineno"> 657</span> local_bounds_at_candidate.radius;</div>
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<div class="line"><a id="l00658" name="l00658"></a><span class="lineno"> 658</span> <span class="keywordflow">return</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afdd1506c32f697aeb13c4b9a9f05ba03">BoundGap</a>(local_bounds_at_candidate) / distance_traveled_by_candidate;</div>
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<div class="line"><a id="l00659" name="l00659"></a><span class="lineno"> 659</span>}</div>
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<div class="line"><a id="l00660" name="l00660"></a><span class="lineno"> 660</span> </div>
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<div class="line"><a id="l00661" name="l00661"></a><span class="lineno"> 661</span><span class="comment">// TODO(user): Review / cleanup adaptive heuristic.</span></div>
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<div class="line"><a id="l00662" name="l00662"></a><span class="lineno"> 662</span><span class="keywordtype">bool</span> Solver::ShouldDoAdaptiveRestartHeuristic(</div>
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<div class="line"><a id="l00663" name="l00663"></a><span class="lineno"> 663</span> <span class="keywordtype">double</span> candidate_normalized_gap)<span class="keyword"> const </span>{</div>
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<div class="line"><a id="l00664" name="l00664"></a><span class="lineno"> 664</span> <span class="keyword">const</span> <span class="keywordtype">double</span> gap_reduction_ratio =</div>
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<div class="line"><a id="l00665" name="l00665"></a><span class="lineno"> 665</span> candidate_normalized_gap / normalized_gap_at_last_restart_;</div>
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<div class="line"><a id="l00666" name="l00666"></a><span class="lineno"> 666</span> <span class="keywordflow">if</span> (gap_reduction_ratio < params_.sufficient_reduction_for_restart()) {</div>
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<div class="line"><a id="l00667" name="l00667"></a><span class="lineno"> 667</span> <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
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<div class="line"><a id="l00668" name="l00668"></a><span class="lineno"> 668</span> }</div>
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<div class="line"><a id="l00669" name="l00669"></a><span class="lineno"> 669</span> <span class="keywordflow">if</span> (gap_reduction_ratio < params_.necessary_reduction_for_restart() &&</div>
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<div class="line"><a id="l00670" name="l00670"></a><span class="lineno"> 670</span> candidate_normalized_gap > normalized_gap_at_last_trial_) {</div>
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<div class="line"><a id="l00671" name="l00671"></a><span class="lineno"> 671</span> <span class="comment">// We've made the "necessary" amount of progress, and iterates appear to</span></div>
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<div class="line"><a id="l00672" name="l00672"></a><span class="lineno"> 672</span> <span class="comment">// be getting worse, so restart.</span></div>
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<div class="line"><a id="l00673" name="l00673"></a><span class="lineno"> 673</span> <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
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|
<div class="line"><a id="l00674" name="l00674"></a><span class="lineno"> 674</span> }</div>
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<div class="line"><a id="l00675" name="l00675"></a><span class="lineno"> 675</span> <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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|
<div class="line"><a id="l00676" name="l00676"></a><span class="lineno"> 676</span>}</div>
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|
<div class="line"><a id="l00677" name="l00677"></a><span class="lineno"> 677</span> </div>
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<div class="line"><a id="l00678" name="l00678"></a><span class="lineno"> 678</span>RestartChoice Solver::DetermineDistanceBasedRestartChoice()<span class="keyword"> const </span>{</div>
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|
<div class="line"><a id="l00679" name="l00679"></a><span class="lineno"> 679</span> <span class="comment">// The following checks are safeguards that normally should not be triggered.</span></div>
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|
<div class="line"><a id="l00680" name="l00680"></a><span class="lineno"> 680</span> <span class="keywordflow">if</span> (primal_average_.NumTerms() == 0) {</div>
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<div class="line"><a id="l00681" name="l00681"></a><span class="lineno"> 681</span> <span class="keywordflow">return</span> RESTART_CHOICE_NO_RESTART;</div>
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|
<div class="line"><a id="l00682" name="l00682"></a><span class="lineno"> 682</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (distance_based_restart_info_.length_of_last_restart_period == 0) {</div>
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|
<div class="line"><a id="l00683" name="l00683"></a><span class="lineno"> 683</span> <span class="keywordflow">return</span> RESTART_CHOICE_RESTART_TO_AVERAGE;</div>
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|
<div class="line"><a id="l00684" name="l00684"></a><span class="lineno"> 684</span> }</div>
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|
<div class="line"><a id="l00685" name="l00685"></a><span class="lineno"> 685</span> <span class="keyword">const</span> <span class="keywordtype">int</span> restart_period_length = primal_average_.NumTerms();</div>
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|
<div class="line"><a id="l00686" name="l00686"></a><span class="lineno"> 686</span> <span class="keyword">const</span> <span class="keywordtype">double</span> distance_moved_this_restart_period_by_average =</div>
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|
<div class="line"><a id="l00687" name="l00687"></a><span class="lineno"> 687</span> DistanceTraveledFromLastStart(primal_average_.ComputeAverage(),</div>
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|
<div class="line"><a id="l00688" name="l00688"></a><span class="lineno"> 688</span> dual_average_.ComputeAverage());</div>
|
|
<div class="line"><a id="l00689" name="l00689"></a><span class="lineno"> 689</span> <span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ae3146d59eb9e49c48bd3ea7b3e60ab65">distance_moved_last_restart_period</a> =</div>
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|
<div class="line"><a id="l00690" name="l00690"></a><span class="lineno"> 690</span> distance_based_restart_info_.distance_moved_last_restart_period;</div>
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|
<div class="line"><a id="l00691" name="l00691"></a><span class="lineno"> 691</span> </div>
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|
<div class="line"><a id="l00692" name="l00692"></a><span class="lineno"> 692</span> <span class="comment">// A restart should be triggered when the normalized distance traveled by</span></div>
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|
<div class="line"><a id="l00693" name="l00693"></a><span class="lineno"> 693</span> <span class="comment">// the average is at least a constant factor smaller than the last.</span></div>
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|
<div class="line"><a id="l00694" name="l00694"></a><span class="lineno"> 694</span> <span class="comment">// TODO(user): Experiment with using .necessary_reduction_for_restart()</span></div>
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|
<div class="line"><a id="l00695" name="l00695"></a><span class="lineno"> 695</span> <span class="comment">// as a heuristic when deciding if a restart should be triggered.</span></div>
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|
<div class="line"><a id="l00696" name="l00696"></a><span class="lineno"> 696</span> <span class="keywordflow">if</span> ((distance_moved_this_restart_period_by_average / restart_period_length) <</div>
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|
<div class="line"><a id="l00697" name="l00697"></a><span class="lineno"> 697</span> params_.sufficient_reduction_for_restart() *</div>
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|
<div class="line"><a id="l00698" name="l00698"></a><span class="lineno"> 698</span> (<a class="code hl_variable" href="primal__dual__hybrid__gradient_8cc.html#ae3146d59eb9e49c48bd3ea7b3e60ab65">distance_moved_last_restart_period</a> /</div>
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|
<div class="line"><a id="l00699" name="l00699"></a><span class="lineno"> 699</span> distance_based_restart_info_.length_of_last_restart_period)) {</div>
|
|
<div class="line"><a id="l00700" name="l00700"></a><span class="lineno"> 700</span> <span class="comment">// Restart at current solution when it yields a smaller normalized potential</span></div>
|
|
<div class="line"><a id="l00701" name="l00701"></a><span class="lineno"> 701</span> <span class="comment">// function value than the average (heuristic suggested by ohinder@).</span></div>
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|
<div class="line"><a id="l00702" name="l00702"></a><span class="lineno"> 702</span> <span class="keywordflow">if</span> (AverageHasBetterPotential(ComputeLocalizedBoundsAtAverage(),</div>
|
|
<div class="line"><a id="l00703" name="l00703"></a><span class="lineno"> 703</span> ComputeLocalizedBoundsAtCurrent())) {</div>
|
|
<div class="line"><a id="l00704" name="l00704"></a><span class="lineno"> 704</span> <span class="keywordflow">return</span> RESTART_CHOICE_RESTART_TO_AVERAGE;</div>
|
|
<div class="line"><a id="l00705" name="l00705"></a><span class="lineno"> 705</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00706" name="l00706"></a><span class="lineno"> 706</span> <span class="keywordflow">return</span> RESTART_CHOICE_WEIGHTED_AVERAGE_RESET;</div>
|
|
<div class="line"><a id="l00707" name="l00707"></a><span class="lineno"> 707</span> }</div>
|
|
<div class="line"><a id="l00708" name="l00708"></a><span class="lineno"> 708</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00709" name="l00709"></a><span class="lineno"> 709</span> <span class="keywordflow">return</span> RESTART_CHOICE_NO_RESTART;</div>
|
|
<div class="line"><a id="l00710" name="l00710"></a><span class="lineno"> 710</span> }</div>
|
|
<div class="line"><a id="l00711" name="l00711"></a><span class="lineno"> 711</span>}</div>
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|
<div class="line"><a id="l00712" name="l00712"></a><span class="lineno"> 712</span> </div>
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|
<div class="line"><a id="l00713" name="l00713"></a><span class="lineno"> 713</span>RestartChoice Solver::ChooseRestartToApply(<span class="keyword">const</span> <span class="keywordtype">bool</span> is_major_iteration) {</div>
|
|
<div class="line"><a id="l00714" name="l00714"></a><span class="lineno"> 714</span> <span class="keywordflow">if</span> (!primal_average_.HasNonzeroWeight() &&</div>
|
|
<div class="line"><a id="l00715" name="l00715"></a><span class="lineno"> 715</span> !dual_average_.HasNonzeroWeight()) {</div>
|
|
<div class="line"><a id="l00716" name="l00716"></a><span class="lineno"> 716</span> <span class="keywordflow">return</span> RESTART_CHOICE_NO_RESTART;</div>
|
|
<div class="line"><a id="l00717" name="l00717"></a><span class="lineno"> 717</span> }</div>
|
|
<div class="line"><a id="l00718" name="l00718"></a><span class="lineno"> 718</span> <span class="comment">// TODO(user): This forced restart is very important for the performance of</span></div>
|
|
<div class="line"><a id="l00719" name="l00719"></a><span class="lineno"> 719</span> <span class="comment">// ADAPTIVE_HEURISTIC. Test if the impact comes primarily from the first</span></div>
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|
<div class="line"><a id="l00720" name="l00720"></a><span class="lineno"> 720</span> <span class="comment">// forced restart (which would unseat a good initial starting point that could</span></div>
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|
<div class="line"><a id="l00721" name="l00721"></a><span class="lineno"> 721</span> <span class="comment">// prevent restarts early in the solve) or if it's really needed for the full</span></div>
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|
<div class="line"><a id="l00722" name="l00722"></a><span class="lineno"> 722</span> <span class="comment">// duration of the solve. If it is really needed, should we then trigger major</span></div>
|
|
<div class="line"><a id="l00723" name="l00723"></a><span class="lineno"> 723</span> <span class="comment">// iterations on powers of two?</span></div>
|
|
<div class="line"><a id="l00724" name="l00724"></a><span class="lineno"> 724</span> <span class="keyword">const</span> <span class="keywordtype">int</span> restart_length = primal_average_.NumTerms();</div>
|
|
<div class="line"><a id="l00725" name="l00725"></a><span class="lineno"> 725</span> <span class="keywordflow">if</span> (restart_length >= iterations_completed_ / 2 &&</div>
|
|
<div class="line"><a id="l00726" name="l00726"></a><span class="lineno"> 726</span> params_.restart_strategy() ==</div>
|
|
<div class="line"><a id="l00727" name="l00727"></a><span class="lineno"> 727</span> PrimalDualHybridGradientParams::ADAPTIVE_HEURISTIC) {</div>
|
|
<div class="line"><a id="l00728" name="l00728"></a><span class="lineno"> 728</span> <span class="keywordflow">if</span> (AverageHasBetterPotential(ComputeLocalizedBoundsAtAverage(),</div>
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|
<div class="line"><a id="l00729" name="l00729"></a><span class="lineno"> 729</span> ComputeLocalizedBoundsAtCurrent())) {</div>
|
|
<div class="line"><a id="l00730" name="l00730"></a><span class="lineno"> 730</span> <span class="keywordflow">return</span> RESTART_CHOICE_RESTART_TO_AVERAGE;</div>
|
|
<div class="line"><a id="l00731" name="l00731"></a><span class="lineno"> 731</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00732" name="l00732"></a><span class="lineno"> 732</span> <span class="keywordflow">return</span> RESTART_CHOICE_WEIGHTED_AVERAGE_RESET;</div>
|
|
<div class="line"><a id="l00733" name="l00733"></a><span class="lineno"> 733</span> }</div>
|
|
<div class="line"><a id="l00734" name="l00734"></a><span class="lineno"> 734</span> }</div>
|
|
<div class="line"><a id="l00735" name="l00735"></a><span class="lineno"> 735</span> <span class="keywordflow">if</span> (is_major_iteration) {</div>
|
|
<div class="line"><a id="l00736" name="l00736"></a><span class="lineno"> 736</span> <span class="keywordflow">switch</span> (params_.restart_strategy()) {</div>
|
|
<div class="line"><a id="l00737" name="l00737"></a><span class="lineno"> 737</span> <span class="keywordflow">case</span> PrimalDualHybridGradientParams::NO_RESTARTS:</div>
|
|
<div class="line"><a id="l00738" name="l00738"></a><span class="lineno"> 738</span> <span class="keywordflow">return</span> RESTART_CHOICE_WEIGHTED_AVERAGE_RESET;</div>
|
|
<div class="line"><a id="l00739" name="l00739"></a><span class="lineno"> 739</span> <span class="keywordflow">case</span> PrimalDualHybridGradientParams::EVERY_MAJOR_ITERATION:</div>
|
|
<div class="line"><a id="l00740" name="l00740"></a><span class="lineno"> 740</span> <span class="keywordflow">return</span> RESTART_CHOICE_RESTART_TO_AVERAGE;</div>
|
|
<div class="line"><a id="l00741" name="l00741"></a><span class="lineno"> 741</span> <span class="keywordflow">case</span> PrimalDualHybridGradientParams::ADAPTIVE_HEURISTIC: {</div>
|
|
<div class="line"><a id="l00742" name="l00742"></a><span class="lineno"> 742</span> <span class="keyword">const</span> LocalizedLagrangianBounds local_bounds_at_average =</div>
|
|
<div class="line"><a id="l00743" name="l00743"></a><span class="lineno"> 743</span> ComputeLocalizedBoundsAtAverage();</div>
|
|
<div class="line"><a id="l00744" name="l00744"></a><span class="lineno"> 744</span> <span class="keyword">const</span> LocalizedLagrangianBounds local_bounds_at_current =</div>
|
|
<div class="line"><a id="l00745" name="l00745"></a><span class="lineno"> 745</span> ComputeLocalizedBoundsAtCurrent();</div>
|
|
<div class="line"><a id="l00746" name="l00746"></a><span class="lineno"> 746</span> <span class="keywordtype">double</span> normalized_gap;</div>
|
|
<div class="line"><a id="l00747" name="l00747"></a><span class="lineno"> 747</span> RestartChoice choice;</div>
|
|
<div class="line"><a id="l00748" name="l00748"></a><span class="lineno"> 748</span> <span class="keywordflow">if</span> (AverageHasBetterPotential(local_bounds_at_average,</div>
|
|
<div class="line"><a id="l00749" name="l00749"></a><span class="lineno"> 749</span> local_bounds_at_current)) {</div>
|
|
<div class="line"><a id="l00750" name="l00750"></a><span class="lineno"> 750</span> normalized_gap = NormalizedGap(local_bounds_at_average);</div>
|
|
<div class="line"><a id="l00751" name="l00751"></a><span class="lineno"> 751</span> choice = RESTART_CHOICE_RESTART_TO_AVERAGE;</div>
|
|
<div class="line"><a id="l00752" name="l00752"></a><span class="lineno"> 752</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00753" name="l00753"></a><span class="lineno"> 753</span> normalized_gap = NormalizedGap(local_bounds_at_current);</div>
|
|
<div class="line"><a id="l00754" name="l00754"></a><span class="lineno"> 754</span> choice = RESTART_CHOICE_WEIGHTED_AVERAGE_RESET;</div>
|
|
<div class="line"><a id="l00755" name="l00755"></a><span class="lineno"> 755</span> }</div>
|
|
<div class="line"><a id="l00756" name="l00756"></a><span class="lineno"> 756</span> <span class="keywordflow">if</span> (ShouldDoAdaptiveRestartHeuristic(normalized_gap)) {</div>
|
|
<div class="line"><a id="l00757" name="l00757"></a><span class="lineno"> 757</span> <span class="keywordflow">return</span> choice;</div>
|
|
<div class="line"><a id="l00758" name="l00758"></a><span class="lineno"> 758</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00759" name="l00759"></a><span class="lineno"> 759</span> normalized_gap_at_last_trial_ = normalized_gap;</div>
|
|
<div class="line"><a id="l00760" name="l00760"></a><span class="lineno"> 760</span> <span class="keywordflow">return</span> RESTART_CHOICE_NO_RESTART;</div>
|
|
<div class="line"><a id="l00761" name="l00761"></a><span class="lineno"> 761</span> }</div>
|
|
<div class="line"><a id="l00762" name="l00762"></a><span class="lineno"> 762</span> }</div>
|
|
<div class="line"><a id="l00763" name="l00763"></a><span class="lineno"> 763</span> <span class="keywordflow">case</span> PrimalDualHybridGradientParams::ADAPTIVE_DISTANCE_BASED: {</div>
|
|
<div class="line"><a id="l00764" name="l00764"></a><span class="lineno"> 764</span> <span class="keywordflow">return</span> DetermineDistanceBasedRestartChoice();</div>
|
|
<div class="line"><a id="l00765" name="l00765"></a><span class="lineno"> 765</span> }</div>
|
|
<div class="line"><a id="l00766" name="l00766"></a><span class="lineno"> 766</span> <span class="keywordflow">default</span>:</div>
|
|
<div class="line"><a id="l00767" name="l00767"></a><span class="lineno"> 767</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#acdd38e3c9f22f127d7776920e3079eda">FATAL</a>) << <span class="stringliteral">"Unrecognized restart_strategy "</span></div>
|
|
<div class="line"><a id="l00768" name="l00768"></a><span class="lineno"> 768</span> << params_.restart_strategy();</div>
|
|
<div class="line"><a id="l00769" name="l00769"></a><span class="lineno"> 769</span> <span class="keywordflow">return</span> RESTART_CHOICE_UNSPECIFIED;</div>
|
|
<div class="line"><a id="l00770" name="l00770"></a><span class="lineno"> 770</span> }</div>
|
|
<div class="line"><a id="l00771" name="l00771"></a><span class="lineno"> 771</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00772" name="l00772"></a><span class="lineno"> 772</span> <span class="keywordflow">return</span> RESTART_CHOICE_NO_RESTART;</div>
|
|
<div class="line"><a id="l00773" name="l00773"></a><span class="lineno"> 773</span> }</div>
|
|
<div class="line"><a id="l00774" name="l00774"></a><span class="lineno"> 774</span>}</div>
|
|
<div class="line"><a id="l00775" name="l00775"></a><span class="lineno"> 775</span> </div>
|
|
<div class="line"><a id="l00776" name="l00776"></a><span class="lineno"> 776</span>VectorXd Solver::PrimalAverage()<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00777" name="l00777"></a><span class="lineno"> 777</span> <span class="keywordflow">if</span> (primal_average_.HasNonzeroWeight()) {</div>
|
|
<div class="line"><a id="l00778" name="l00778"></a><span class="lineno"> 778</span> <span class="keywordflow">return</span> primal_average_.ComputeAverage();</div>
|
|
<div class="line"><a id="l00779" name="l00779"></a><span class="lineno"> 779</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00780" name="l00780"></a><span class="lineno"> 780</span> <span class="keywordflow">return</span> current_primal_solution_;</div>
|
|
<div class="line"><a id="l00781" name="l00781"></a><span class="lineno"> 781</span> }</div>
|
|
<div class="line"><a id="l00782" name="l00782"></a><span class="lineno"> 782</span>}</div>
|
|
<div class="line"><a id="l00783" name="l00783"></a><span class="lineno"> 783</span> </div>
|
|
<div class="line"><a id="l00784" name="l00784"></a><span class="lineno"> 784</span>VectorXd Solver::DualAverage()<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00785" name="l00785"></a><span class="lineno"> 785</span> <span class="keywordflow">if</span> (dual_average_.HasNonzeroWeight()) {</div>
|
|
<div class="line"><a id="l00786" name="l00786"></a><span class="lineno"> 786</span> <span class="keywordflow">return</span> dual_average_.ComputeAverage();</div>
|
|
<div class="line"><a id="l00787" name="l00787"></a><span class="lineno"> 787</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00788" name="l00788"></a><span class="lineno"> 788</span> <span class="keywordflow">return</span> current_dual_solution_;</div>
|
|
<div class="line"><a id="l00789" name="l00789"></a><span class="lineno"> 789</span> }</div>
|
|
<div class="line"><a id="l00790" name="l00790"></a><span class="lineno"> 790</span>}</div>
|
|
<div class="line"><a id="l00791" name="l00791"></a><span class="lineno"> 791</span> </div>
|
|
<div class="line"><a id="l00792" name="l00792"></a><span class="lineno"> 792</span><span class="keywordtype">double</span> Solver::ComputeNewPrimalWeight()<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00793" name="l00793"></a><span class="lineno"> 793</span> <span class="keyword">const</span> <span class="keywordtype">double</span> primal_distance =</div>
|
|
<div class="line"><a id="l00794" name="l00794"></a><span class="lineno"> 794</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a3e28f45b9c1ccdec8d926b4034d3679b">Distance</a>(current_primal_solution_, last_primal_start_point_,</div>
|
|
<div class="line"><a id="l00795" name="l00795"></a><span class="lineno"> 795</span> sharded_working_qp_.PrimalSharder());</div>
|
|
<div class="line"><a id="l00796" name="l00796"></a><span class="lineno"> 796</span> <span class="keyword">const</span> <span class="keywordtype">double</span> dual_distance =</div>
|
|
<div class="line"><a id="l00797" name="l00797"></a><span class="lineno"> 797</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a3e28f45b9c1ccdec8d926b4034d3679b">Distance</a>(current_dual_solution_, last_dual_start_point_,</div>
|
|
<div class="line"><a id="l00798" name="l00798"></a><span class="lineno"> 798</span> sharded_working_qp_.DualSharder());</div>
|
|
<div class="line"><a id="l00799" name="l00799"></a><span class="lineno"> 799</span> <span class="comment">// This choice of a nonzero tolerance balances performance and numerical</span></div>
|
|
<div class="line"><a id="l00800" name="l00800"></a><span class="lineno"> 800</span> <span class="comment">// issues caused by very huge or very tiny weights. It was picked as the best</span></div>
|
|
<div class="line"><a id="l00801" name="l00801"></a><span class="lineno"> 801</span> <span class="comment">// among {0.0, 1.0e-20, 2.0e-16, 1.0e-10, 1.0e-5} on the preprocessed MIPLIB</span></div>
|
|
<div class="line"><a id="l00802" name="l00802"></a><span class="lineno"> 802</span> <span class="comment">// dataset. The effect of changing this value is relatively minor overall.</span></div>
|
|
<div class="line"><a id="l00803" name="l00803"></a><span class="lineno"> 803</span> <span class="keyword">constexpr</span> <span class="keywordtype">double</span> kNonzeroTol = 1.0e-10;</div>
|
|
<div class="line"><a id="l00804" name="l00804"></a><span class="lineno"> 804</span> <span class="keywordflow">if</span> (primal_distance <= kNonzeroTol || primal_distance >= 1.0 / kNonzeroTol ||</div>
|
|
<div class="line"><a id="l00805" name="l00805"></a><span class="lineno"> 805</span> dual_distance <= kNonzeroTol || dual_distance >= 1.0 / kNonzeroTol) {</div>
|
|
<div class="line"><a id="l00806" name="l00806"></a><span class="lineno"> 806</span> <span class="keywordflow">return</span> primal_weight_;</div>
|
|
<div class="line"><a id="l00807" name="l00807"></a><span class="lineno"> 807</span> }</div>
|
|
<div class="line"><a id="l00808" name="l00808"></a><span class="lineno"> 808</span> <span class="keyword">const</span> <span class="keywordtype">double</span> smoothing_param = params_.primal_weight_update_smoothing();</div>
|
|
<div class="line"><a id="l00809" name="l00809"></a><span class="lineno"> 809</span> <span class="keyword">const</span> <span class="keywordtype">double</span> unsmoothed_new_primal_weight = dual_distance / primal_distance;</div>
|
|
<div class="line"><a id="l00810" name="l00810"></a><span class="lineno"> 810</span> <span class="keyword">const</span> <span class="keywordtype">double</span> new_primal_weight =</div>
|
|
<div class="line"><a id="l00811" name="l00811"></a><span class="lineno"> 811</span> std::exp(smoothing_param * std::log(unsmoothed_new_primal_weight) +</div>
|
|
<div class="line"><a id="l00812" name="l00812"></a><span class="lineno"> 812</span> (1.0 - smoothing_param) * std::log(primal_weight_));</div>
|
|
<div class="line"><a id="l00813" name="l00813"></a><span class="lineno"> 813</span> <a class="code hl_define" href="base_2logging_8h.html#a09f7d88282cf92c9f231270ac113e5c6">LOG_IF</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>, params_.verbosity_level() >= 4)</div>
|
|
<div class="line"><a id="l00814" name="l00814"></a><span class="lineno"> 814</span> << <span class="stringliteral">"New computed primal weight is "</span> << new_primal_weight</div>
|
|
<div class="line"><a id="l00815" name="l00815"></a><span class="lineno"> 815</span> << <span class="stringliteral">" at iteration "</span> << iterations_completed_;</div>
|
|
<div class="line"><a id="l00816" name="l00816"></a><span class="lineno"> 816</span> <span class="keywordflow">return</span> new_primal_weight;</div>
|
|
<div class="line"><a id="l00817" name="l00817"></a><span class="lineno"> 817</span>}</div>
|
|
<div class="line"><a id="l00818" name="l00818"></a><span class="lineno"> 818</span> </div>
|
|
<div class="line"><a id="l00819" name="l00819"></a><span class="lineno"> 819</span>SolverResult Solver::ConstructSolverResult(VectorXd primal_solution,</div>
|
|
<div class="line"><a id="l00820" name="l00820"></a><span class="lineno"> 820</span> VectorXd dual_solution,</div>
|
|
<div class="line"><a id="l00821" name="l00821"></a><span class="lineno"> 821</span> <span class="keyword">const</span> IterationStats& stats,</div>
|
|
<div class="line"><a id="l00822" name="l00822"></a><span class="lineno"> 822</span> <a class="code hl_enumeration" href="namespaceoperations__research_1_1math__opt.html#ad02e69a0531469b463df907c7b2ad194">TerminationReason</a> termination_reason,</div>
|
|
<div class="line"><a id="l00823" name="l00823"></a><span class="lineno"> 823</span> PointType output_type,</div>
|
|
<div class="line"><a id="l00824" name="l00824"></a><span class="lineno"> 824</span> SolveLog solve_log)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00825" name="l00825"></a><span class="lineno"> 825</span> <span class="keywordflow">switch</span> (output_type) {</div>
|
|
<div class="line"><a id="l00826" name="l00826"></a><span class="lineno"> 826</span> <span class="keywordflow">case</span> POINT_TYPE_AVERAGE_ITERATE:</div>
|
|
<div class="line"><a id="l00827" name="l00827"></a><span class="lineno"> 827</span> solve_log.set_solution_type(POINT_TYPE_AVERAGE_ITERATE);</div>
|
|
<div class="line"><a id="l00828" name="l00828"></a><span class="lineno"> 828</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l00829" name="l00829"></a><span class="lineno"> 829</span> <span class="keywordflow">case</span> POINT_TYPE_CURRENT_ITERATE:</div>
|
|
<div class="line"><a id="l00830" name="l00830"></a><span class="lineno"> 830</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afca8f74da7e8301c8aee45f33c93896c">AssignVector</a>(current_primal_solution_,</div>
|
|
<div class="line"><a id="l00831" name="l00831"></a><span class="lineno"> 831</span> sharded_working_qp_.PrimalSharder(), primal_solution);</div>
|
|
<div class="line"><a id="l00832" name="l00832"></a><span class="lineno"> 832</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afca8f74da7e8301c8aee45f33c93896c">AssignVector</a>(current_dual_solution_, sharded_working_qp_.DualSharder(),</div>
|
|
<div class="line"><a id="l00833" name="l00833"></a><span class="lineno"> 833</span> dual_solution);</div>
|
|
<div class="line"><a id="l00834" name="l00834"></a><span class="lineno"> 834</span> solve_log.set_solution_type(POINT_TYPE_CURRENT_ITERATE);</div>
|
|
<div class="line"><a id="l00835" name="l00835"></a><span class="lineno"> 835</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l00836" name="l00836"></a><span class="lineno"> 836</span> <span class="keywordflow">case</span> POINT_TYPE_ITERATE_DIFFERENCE:</div>
|
|
<div class="line"><a id="l00837" name="l00837"></a><span class="lineno"> 837</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afca8f74da7e8301c8aee45f33c93896c">AssignVector</a>(current_primal_delta_, sharded_working_qp_.PrimalSharder(),</div>
|
|
<div class="line"><a id="l00838" name="l00838"></a><span class="lineno"> 838</span> primal_solution);</div>
|
|
<div class="line"><a id="l00839" name="l00839"></a><span class="lineno"> 839</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afca8f74da7e8301c8aee45f33c93896c">AssignVector</a>(current_dual_delta_, sharded_working_qp_.DualSharder(),</div>
|
|
<div class="line"><a id="l00840" name="l00840"></a><span class="lineno"> 840</span> dual_solution);</div>
|
|
<div class="line"><a id="l00841" name="l00841"></a><span class="lineno"> 841</span> solve_log.set_solution_type(POINT_TYPE_ITERATE_DIFFERENCE);</div>
|
|
<div class="line"><a id="l00842" name="l00842"></a><span class="lineno"> 842</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l00843" name="l00843"></a><span class="lineno"> 843</span> <span class="keywordflow">case</span> POINT_TYPE_PRESOLVER_SOLUTION:</div>
|
|
<div class="line"><a id="l00844" name="l00844"></a><span class="lineno"> 844</span> solve_log.set_solution_type(POINT_TYPE_PRESOLVER_SOLUTION);</div>
|
|
<div class="line"><a id="l00845" name="l00845"></a><span class="lineno"> 845</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l00846" name="l00846"></a><span class="lineno"> 846</span> <span class="keywordflow">default</span>:</div>
|
|
<div class="line"><a id="l00847" name="l00847"></a><span class="lineno"> 847</span> <span class="comment">// Default to average whenever the type is POINT_TYPE_NONE.</span></div>
|
|
<div class="line"><a id="l00848" name="l00848"></a><span class="lineno"> 848</span> solve_log.set_solution_type(POINT_TYPE_AVERAGE_ITERATE);</div>
|
|
<div class="line"><a id="l00849" name="l00849"></a><span class="lineno"> 849</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l00850" name="l00850"></a><span class="lineno"> 850</span> }</div>
|
|
<div class="line"><a id="l00851" name="l00851"></a><span class="lineno"> 851</span> VectorXd reduced_costs;</div>
|
|
<div class="line"><a id="l00852" name="l00852"></a><span class="lineno"> 852</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> use_zero_primal_objective =</div>
|
|
<div class="line"><a id="l00853" name="l00853"></a><span class="lineno"> 853</span> termination_reason == TERMINATION_REASON_PRIMAL_INFEASIBLE;</div>
|
|
<div class="line"><a id="l00854" name="l00854"></a><span class="lineno"> 854</span> <span class="keywordflow">if</span> (presolve_info_.has_value()) {</div>
|
|
<div class="line"><a id="l00855" name="l00855"></a><span class="lineno"> 855</span> <span class="comment">// Transform the solutions so they match the original unscaled problem.</span></div>
|
|
<div class="line"><a id="l00856" name="l00856"></a><span class="lineno"> 856</span> PrimalAndDualSolution original_solution =</div>
|
|
<div class="line"><a id="l00857" name="l00857"></a><span class="lineno"> 857</span> RecoverOriginalSolution({.primal_solution = std::move(primal_solution),</div>
|
|
<div class="line"><a id="l00858" name="l00858"></a><span class="lineno"> 858</span> .dual_solution = std::move(dual_solution)});</div>
|
|
<div class="line"><a id="l00859" name="l00859"></a><span class="lineno"> 859</span> primal_solution = std::move(original_solution.primal_solution);</div>
|
|
<div class="line"><a id="l00860" name="l00860"></a><span class="lineno"> 860</span> dual_solution = std::move(original_solution.dual_solution);</div>
|
|
<div class="line"><a id="l00861" name="l00861"></a><span class="lineno"> 861</span> <span class="comment">// RecoverOriginalSolution doesn't recover reduced costs so we need to</span></div>
|
|
<div class="line"><a id="l00862" name="l00862"></a><span class="lineno"> 862</span> <span class="comment">// compute them with respect to the original problem.</span></div>
|
|
<div class="line"><a id="l00863" name="l00863"></a><span class="lineno"> 863</span> reduced_costs =</div>
|
|
<div class="line"><a id="l00864" name="l00864"></a><span class="lineno"> 864</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a64bfea523f69cba6f7be8ac302c18f2f">ReducedCosts</a>(presolve_info_->sharded_original_qp, primal_solution,</div>
|
|
<div class="line"><a id="l00865" name="l00865"></a><span class="lineno"> 865</span> dual_solution, use_zero_primal_objective);</div>
|
|
<div class="line"><a id="l00866" name="l00866"></a><span class="lineno"> 866</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00867" name="l00867"></a><span class="lineno"> 867</span> reduced_costs = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a64bfea523f69cba6f7be8ac302c18f2f">ReducedCosts</a>(sharded_working_qp_, primal_solution,</div>
|
|
<div class="line"><a id="l00868" name="l00868"></a><span class="lineno"> 868</span> dual_solution, use_zero_primal_objective);</div>
|
|
<div class="line"><a id="l00869" name="l00869"></a><span class="lineno"> 869</span> <span class="comment">// Transform the solutions so they match the original unscaled problem.</span></div>
|
|
<div class="line"><a id="l00870" name="l00870"></a><span class="lineno"> 870</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a920005e41b36a7a0c7f4ad148ad7069d">CoefficientWiseProductInPlace</a>(</div>
|
|
<div class="line"><a id="l00871" name="l00871"></a><span class="lineno"> 871</span> col_scaling_vec_, sharded_working_qp_.PrimalSharder(), primal_solution);</div>
|
|
<div class="line"><a id="l00872" name="l00872"></a><span class="lineno"> 872</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a920005e41b36a7a0c7f4ad148ad7069d">CoefficientWiseProductInPlace</a>(</div>
|
|
<div class="line"><a id="l00873" name="l00873"></a><span class="lineno"> 873</span> row_scaling_vec_, sharded_working_qp_.DualSharder(), dual_solution);</div>
|
|
<div class="line"><a id="l00874" name="l00874"></a><span class="lineno"> 874</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a92c8ca6bf2bb288c322e1d8fbd6ea2bc">CoefficientWiseQuotientInPlace</a>(</div>
|
|
<div class="line"><a id="l00875" name="l00875"></a><span class="lineno"> 875</span> col_scaling_vec_, sharded_working_qp_.PrimalSharder(), reduced_costs);</div>
|
|
<div class="line"><a id="l00876" name="l00876"></a><span class="lineno"> 876</span> }</div>
|
|
<div class="line"><a id="l00877" name="l00877"></a><span class="lineno"> 877</span> <span class="keywordflow">if</span> (iteration_stats_callback_ != <span class="keyword">nullptr</span>) {</div>
|
|
<div class="line"><a id="l00878" name="l00878"></a><span class="lineno"> 878</span> iteration_stats_callback_(</div>
|
|
<div class="line"><a id="l00879" name="l00879"></a><span class="lineno"> 879</span> {.termination_criteria = params_.termination_criteria(),</div>
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<div class="line"><a id="l00880" name="l00880"></a><span class="lineno"> 880</span> .iteration_stats = stats,</div>
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<div class="line"><a id="l00881" name="l00881"></a><span class="lineno"> 881</span> .bound_norms = original_bound_norms_});</div>
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<div class="line"><a id="l00882" name="l00882"></a><span class="lineno"> 882</span> }</div>
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<div class="line"><a id="l00883" name="l00883"></a><span class="lineno"> 883</span> </div>
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<div class="line"><a id="l00884" name="l00884"></a><span class="lineno"> 884</span> <span class="keywordflow">if</span> (params_.verbosity_level() >= 1) {</div>
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<div class="line"><a id="l00885" name="l00885"></a><span class="lineno"> 885</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Termination reason: "</span></div>
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<div class="line"><a id="l00886" name="l00886"></a><span class="lineno"> 886</span> << TerminationReason_Name(termination_reason);</div>
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<div class="line"><a id="l00887" name="l00887"></a><span class="lineno"> 887</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Solution point type: "</span> << PointType_Name(output_type);</div>
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<div class="line"><a id="l00888" name="l00888"></a><span class="lineno"> 888</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Final solution stats:"</span>;</div>
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<div class="line"><a id="l00889" name="l00889"></a><span class="lineno"> 889</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << IterationStatsLabelString();</div>
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<div class="line"><a id="l00890" name="l00890"></a><span class="lineno"> 890</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <a class="code hl_function" href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">ToString</a>(stats, params_.termination_criteria(),</div>
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|
<div class="line"><a id="l00891" name="l00891"></a><span class="lineno"> 891</span> original_bound_norms_, solve_log.solution_type());</div>
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|
<div class="line"><a id="l00892" name="l00892"></a><span class="lineno"> 892</span> }</div>
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|
<div class="line"><a id="l00893" name="l00893"></a><span class="lineno"> 893</span> solve_log.set_iteration_count(stats.iteration_number());</div>
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|
<div class="line"><a id="l00894" name="l00894"></a><span class="lineno"> 894</span> solve_log.set_termination_reason(termination_reason);</div>
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|
<div class="line"><a id="l00895" name="l00895"></a><span class="lineno"> 895</span> solve_log.set_solve_time_sec(stats.cumulative_time_sec());</div>
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|
<div class="line"><a id="l00896" name="l00896"></a><span class="lineno"> 896</span> *solve_log.mutable_solution_stats() = stats;</div>
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|
<div class="line"><a id="l00897" name="l00897"></a><span class="lineno"> 897</span> <span class="keywordflow">return</span> SolverResult{.primal_solution = std::move(primal_solution),</div>
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|
<div class="line"><a id="l00898" name="l00898"></a><span class="lineno"> 898</span> .dual_solution = std::move(dual_solution),</div>
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|
<div class="line"><a id="l00899" name="l00899"></a><span class="lineno"> 899</span> .reduced_costs = std::move(reduced_costs),</div>
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|
<div class="line"><a id="l00900" name="l00900"></a><span class="lineno"> 900</span> .solve_log = std::move(solve_log)};</div>
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|
<div class="line"><a id="l00901" name="l00901"></a><span class="lineno"> 901</span>}</div>
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<div class="line"><a id="l00902" name="l00902"></a><span class="lineno"> 902</span> </div>
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<div class="line"><a id="l00903" name="l00903"></a><span class="lineno"> 903</span><span class="keywordtype">void</span> SetActiveSetInformation(<span class="keyword">const</span> ShardedQuadraticProgram& sharded_qp,</div>
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|
<div class="line"><a id="l00904" name="l00904"></a><span class="lineno"> 904</span> <span class="keyword">const</span> VectorXd& primal_solution,</div>
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|
<div class="line"><a id="l00905" name="l00905"></a><span class="lineno"> 905</span> <span class="keyword">const</span> VectorXd& dual_solution,</div>
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|
<div class="line"><a id="l00906" name="l00906"></a><span class="lineno"> 906</span> <span class="keyword">const</span> VectorXd& primal_start_point,</div>
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|
<div class="line"><a id="l00907" name="l00907"></a><span class="lineno"> 907</span> <span class="keyword">const</span> VectorXd& dual_start_point,</div>
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|
<div class="line"><a id="l00908" name="l00908"></a><span class="lineno"> 908</span> PointMetadata& metadata) {</div>
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|
<div class="line"><a id="l00909" name="l00909"></a><span class="lineno"> 909</span> <a class="code hl_define" href="base_2logging_8h.html#a7c0ce053b28d53aa4eaf3eb7fb71663b">CHECK_EQ</a>(primal_solution.size(), sharded_qp.PrimalSize());</div>
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|
<div class="line"><a id="l00910" name="l00910"></a><span class="lineno"> 910</span> <a class="code hl_define" href="base_2logging_8h.html#a7c0ce053b28d53aa4eaf3eb7fb71663b">CHECK_EQ</a>(dual_solution.size(), sharded_qp.DualSize());</div>
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|
<div class="line"><a id="l00911" name="l00911"></a><span class="lineno"> 911</span> <a class="code hl_define" href="base_2logging_8h.html#a7c0ce053b28d53aa4eaf3eb7fb71663b">CHECK_EQ</a>(primal_start_point.size(), sharded_qp.PrimalSize());</div>
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|
<div class="line"><a id="l00912" name="l00912"></a><span class="lineno"> 912</span> <a class="code hl_define" href="base_2logging_8h.html#a7c0ce053b28d53aa4eaf3eb7fb71663b">CHECK_EQ</a>(dual_start_point.size(), sharded_qp.DualSize());</div>
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|
<div class="line"><a id="l00913" name="l00913"></a><span class="lineno"> 913</span> </div>
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|
<div class="line"><a id="l00914" name="l00914"></a><span class="lineno"> 914</span> <span class="keyword">const</span> QuadraticProgram& qp = sharded_qp.Qp();</div>
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|
<div class="line"><a id="l00915" name="l00915"></a><span class="lineno"> 915</span> metadata.set_active_primal_variable_count(</div>
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|
<div class="line"><a id="l00916" name="l00916"></a><span class="lineno"> 916</span> <span class="keyword">static_cast<</span>int64_t<span class="keyword">></span>(sharded_qp.PrimalSharder().ParallelSumOverShards(</div>
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|
<div class="line"><a id="l00917" name="l00917"></a><span class="lineno"> 917</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
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|
<div class="line"><a id="l00918" name="l00918"></a><span class="lineno"> 918</span> const auto primal_shard = shard(primal_solution);</div>
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|
<div class="line"><a id="l00919" name="l00919"></a><span class="lineno"> 919</span> const auto lower_bound_shard = shard(qp.variable_lower_bounds);</div>
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|
<div class="line"><a id="l00920" name="l00920"></a><span class="lineno"> 920</span> const auto upper_bound_shard = shard(qp.variable_upper_bounds);</div>
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|
<div class="line"><a id="l00921" name="l00921"></a><span class="lineno"> 921</span> return (primal_shard.array() > lower_bound_shard.array() &&</div>
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|
<div class="line"><a id="l00922" name="l00922"></a><span class="lineno"> 922</span> primal_shard.array() < upper_bound_shard.array())</div>
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|
<div class="line"><a id="l00923" name="l00923"></a><span class="lineno"> 923</span> .count();</div>
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|
<div class="line"><a id="l00924" name="l00924"></a><span class="lineno"> 924</span> })));</div>
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|
<div class="line"><a id="l00925" name="l00925"></a><span class="lineno"> 925</span> </div>
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|
<div class="line"><a id="l00926" name="l00926"></a><span class="lineno"> 926</span> <span class="comment">// Most of the computation from the previous ParallelSumOverShards is</span></div>
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|
<div class="line"><a id="l00927" name="l00927"></a><span class="lineno"> 927</span> <span class="comment">// duplicated here. However the overhead shouldn't be too large, and using</span></div>
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|
<div class="line"><a id="l00928" name="l00928"></a><span class="lineno"> 928</span> <span class="comment">// ParallelSumOverShards is simpler than just using ParallelForEachShard.</span></div>
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|
<div class="line"><a id="l00929" name="l00929"></a><span class="lineno"> 929</span> metadata.set_active_primal_variable_change(</div>
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|
<div class="line"><a id="l00930" name="l00930"></a><span class="lineno"> 930</span> <span class="keyword">static_cast<</span>int64_t<span class="keyword">></span>(sharded_qp.PrimalSharder().ParallelSumOverShards(</div>
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|
<div class="line"><a id="l00931" name="l00931"></a><span class="lineno"> 931</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
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|
<div class="line"><a id="l00932" name="l00932"></a><span class="lineno"> 932</span> const auto primal_shard = shard(primal_solution);</div>
|
|
<div class="line"><a id="l00933" name="l00933"></a><span class="lineno"> 933</span> const auto primal_start_shard = shard(primal_start_point);</div>
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|
<div class="line"><a id="l00934" name="l00934"></a><span class="lineno"> 934</span> const auto lower_bound_shard = shard(qp.variable_lower_bounds);</div>
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|
<div class="line"><a id="l00935" name="l00935"></a><span class="lineno"> 935</span> const auto upper_bound_shard = shard(qp.variable_upper_bounds);</div>
|
|
<div class="line"><a id="l00936" name="l00936"></a><span class="lineno"> 936</span> return ((primal_shard.array() > lower_bound_shard.array() &&</div>
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|
<div class="line"><a id="l00937" name="l00937"></a><span class="lineno"> 937</span> primal_shard.array() < upper_bound_shard.array()) !=</div>
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|
<div class="line"><a id="l00938" name="l00938"></a><span class="lineno"> 938</span> (primal_start_shard.array() > lower_bound_shard.array() &&</div>
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|
<div class="line"><a id="l00939" name="l00939"></a><span class="lineno"> 939</span> primal_start_shard.array() < upper_bound_shard.array()))</div>
|
|
<div class="line"><a id="l00940" name="l00940"></a><span class="lineno"> 940</span> .count();</div>
|
|
<div class="line"><a id="l00941" name="l00941"></a><span class="lineno"> 941</span> })));</div>
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|
<div class="line"><a id="l00942" name="l00942"></a><span class="lineno"> 942</span> </div>
|
|
<div class="line"><a id="l00943" name="l00943"></a><span class="lineno"> 943</span> metadata.set_active_dual_variable_count(</div>
|
|
<div class="line"><a id="l00944" name="l00944"></a><span class="lineno"> 944</span> <span class="keyword">static_cast<</span>int64_t<span class="keyword">></span>(sharded_qp.DualSharder().ParallelSumOverShards(</div>
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|
<div class="line"><a id="l00945" name="l00945"></a><span class="lineno"> 945</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
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|
<div class="line"><a id="l00946" name="l00946"></a><span class="lineno"> 946</span> const auto dual_shard = shard(dual_solution);</div>
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|
<div class="line"><a id="l00947" name="l00947"></a><span class="lineno"> 947</span> const auto lower_bound_shard = shard(qp.constraint_lower_bounds);</div>
|
|
<div class="line"><a id="l00948" name="l00948"></a><span class="lineno"> 948</span> const auto upper_bound_shard = shard(qp.constraint_upper_bounds);</div>
|
|
<div class="line"><a id="l00949" name="l00949"></a><span class="lineno"> 949</span> const double kInfinity = std::numeric_limits<double>::infinity();</div>
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|
<div class="line"><a id="l00950" name="l00950"></a><span class="lineno"> 950</span> return (dual_shard.array() != 0.0 ||</div>
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|
<div class="line"><a id="l00951" name="l00951"></a><span class="lineno"> 951</span> (lower_bound_shard.array() == -kInfinity &&</div>
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|
<div class="line"><a id="l00952" name="l00952"></a><span class="lineno"> 952</span> upper_bound_shard.array() == kInfinity))</div>
|
|
<div class="line"><a id="l00953" name="l00953"></a><span class="lineno"> 953</span> .count();</div>
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|
<div class="line"><a id="l00954" name="l00954"></a><span class="lineno"> 954</span> })));</div>
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|
<div class="line"><a id="l00955" name="l00955"></a><span class="lineno"> 955</span> </div>
|
|
<div class="line"><a id="l00956" name="l00956"></a><span class="lineno"> 956</span> metadata.set_active_dual_variable_change(</div>
|
|
<div class="line"><a id="l00957" name="l00957"></a><span class="lineno"> 957</span> <span class="keyword">static_cast<</span>int64_t<span class="keyword">></span>(sharded_qp.DualSharder().ParallelSumOverShards(</div>
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|
<div class="line"><a id="l00958" name="l00958"></a><span class="lineno"> 958</span> [&](<span class="keyword">const</span> Sharder::Shard& shard) {</div>
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|
<div class="line"><a id="l00959" name="l00959"></a><span class="lineno"> 959</span> const auto dual_shard = shard(dual_solution);</div>
|
|
<div class="line"><a id="l00960" name="l00960"></a><span class="lineno"> 960</span> const auto dual_start_shard = shard(dual_start_point);</div>
|
|
<div class="line"><a id="l00961" name="l00961"></a><span class="lineno"> 961</span> const auto lower_bound_shard = shard(qp.constraint_lower_bounds);</div>
|
|
<div class="line"><a id="l00962" name="l00962"></a><span class="lineno"> 962</span> const auto upper_bound_shard = shard(qp.constraint_upper_bounds);</div>
|
|
<div class="line"><a id="l00963" name="l00963"></a><span class="lineno"> 963</span> const double kInfinity = std::numeric_limits<double>::infinity();</div>
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|
<div class="line"><a id="l00964" name="l00964"></a><span class="lineno"> 964</span> return ((dual_shard.array() != 0.0 ||</div>
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|
<div class="line"><a id="l00965" name="l00965"></a><span class="lineno"> 965</span> (lower_bound_shard.array() == -kInfinity &&</div>
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|
<div class="line"><a id="l00966" name="l00966"></a><span class="lineno"> 966</span> upper_bound_shard.array() == kInfinity)) !=</div>
|
|
<div class="line"><a id="l00967" name="l00967"></a><span class="lineno"> 967</span> (dual_start_shard.array() != 0.0 ||</div>
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|
<div class="line"><a id="l00968" name="l00968"></a><span class="lineno"> 968</span> (lower_bound_shard.array() == -kInfinity &&</div>
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|
<div class="line"><a id="l00969" name="l00969"></a><span class="lineno"> 969</span> upper_bound_shard.array() == kInfinity)))</div>
|
|
<div class="line"><a id="l00970" name="l00970"></a><span class="lineno"> 970</span> .count();</div>
|
|
<div class="line"><a id="l00971" name="l00971"></a><span class="lineno"> 971</span> })));</div>
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|
<div class="line"><a id="l00972" name="l00972"></a><span class="lineno"> 972</span>}</div>
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|
<div class="line"><a id="l00973" name="l00973"></a><span class="lineno"> 973</span> </div>
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|
<div class="line"><a id="l00974" name="l00974"></a><span class="lineno"> 974</span><span class="keywordtype">void</span> Solver::AddConvergenceAndInfeasibilityInformation(</div>
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|
<div class="line"><a id="l00975" name="l00975"></a><span class="lineno"> 975</span> <span class="keyword">const</span> VectorXd& primal_solution, <span class="keyword">const</span> VectorXd& dual_solution,</div>
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|
<div class="line"><a id="l00976" name="l00976"></a><span class="lineno"> 976</span> <span class="keyword">const</span> ShardedQuadraticProgram& sharded_qp, <span class="keyword">const</span> VectorXd& col_scaling_vec,</div>
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|
<div class="line"><a id="l00977" name="l00977"></a><span class="lineno"> 977</span> <span class="keyword">const</span> VectorXd& row_scaling_vec, PointType candidate_type,</div>
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|
<div class="line"><a id="l00978" name="l00978"></a><span class="lineno"> 978</span> IterationStats& stats)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00979" name="l00979"></a><span class="lineno"> 979</span> *stats.add_convergence_information() = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ac77694ebaac0adfa0fce8422782c48c8">ComputeConvergenceInformation</a>(</div>
|
|
<div class="line"><a id="l00980" name="l00980"></a><span class="lineno"> 980</span> sharded_qp, col_scaling_vec, row_scaling_vec, primal_solution,</div>
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|
<div class="line"><a id="l00981" name="l00981"></a><span class="lineno"> 981</span> dual_solution, candidate_type);</div>
|
|
<div class="line"><a id="l00982" name="l00982"></a><span class="lineno"> 982</span> *stats.add_infeasibility_information() = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a74af5ceb7b6e37fbfca92e2c59b99e3e">ComputeInfeasibilityInformation</a>(</div>
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|
<div class="line"><a id="l00983" name="l00983"></a><span class="lineno"> 983</span> sharded_qp, col_scaling_vec, row_scaling_vec, primal_solution,</div>
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|
<div class="line"><a id="l00984" name="l00984"></a><span class="lineno"> 984</span> dual_solution, candidate_type);</div>
|
|
<div class="line"><a id="l00985" name="l00985"></a><span class="lineno"> 985</span>}</div>
|
|
<div class="line"><a id="l00986" name="l00986"></a><span class="lineno"> 986</span> </div>
|
|
<div class="line"><a id="l00987" name="l00987"></a><span class="lineno"> 987</span><span class="keywordtype">void</span> Solver::AddPointMetadata(<span class="keyword">const</span> VectorXd& primal_solution,</div>
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|
<div class="line"><a id="l00988" name="l00988"></a><span class="lineno"> 988</span> <span class="keyword">const</span> VectorXd& dual_solution,</div>
|
|
<div class="line"><a id="l00989" name="l00989"></a><span class="lineno"> 989</span> PointType point_type,</div>
|
|
<div class="line"><a id="l00990" name="l00990"></a><span class="lineno"> 990</span> IterationStats& stats)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00991" name="l00991"></a><span class="lineno"> 991</span> PointMetadata metadata;</div>
|
|
<div class="line"><a id="l00992" name="l00992"></a><span class="lineno"> 992</span> metadata.set_point_type(point_type);</div>
|
|
<div class="line"><a id="l00993" name="l00993"></a><span class="lineno"> 993</span> std::vector<int> random_projection_seeds(</div>
|
|
<div class="line"><a id="l00994" name="l00994"></a><span class="lineno"> 994</span> params_.random_projection_seeds().begin(),</div>
|
|
<div class="line"><a id="l00995" name="l00995"></a><span class="lineno"> 995</span> params_.random_projection_seeds().end());</div>
|
|
<div class="line"><a id="l00996" name="l00996"></a><span class="lineno"> 996</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a85cd9828e35e9f00a622d0376bc81325">SetRandomProjections</a>(sharded_working_qp_, primal_solution, dual_solution,</div>
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|
<div class="line"><a id="l00997" name="l00997"></a><span class="lineno"> 997</span> random_projection_seeds, metadata);</div>
|
|
<div class="line"><a id="l00998" name="l00998"></a><span class="lineno"> 998</span> <span class="keywordflow">if</span> (point_type != POINT_TYPE_ITERATE_DIFFERENCE) {</div>
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|
<div class="line"><a id="l00999" name="l00999"></a><span class="lineno"> 999</span> SetActiveSetInformation(sharded_working_qp_, primal_solution, dual_solution,</div>
|
|
<div class="line"><a id="l01000" name="l01000"></a><span class="lineno"> 1000</span> last_primal_start_point_, last_dual_start_point_,</div>
|
|
<div class="line"><a id="l01001" name="l01001"></a><span class="lineno"> 1001</span> metadata);</div>
|
|
<div class="line"><a id="l01002" name="l01002"></a><span class="lineno"> 1002</span> }</div>
|
|
<div class="line"><a id="l01003" name="l01003"></a><span class="lineno"> 1003</span> *stats.add_point_metadata() = metadata;</div>
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|
<div class="line"><a id="l01004" name="l01004"></a><span class="lineno"> 1004</span>}</div>
|
|
<div class="line"><a id="l01005" name="l01005"></a><span class="lineno"> 1005</span> </div>
|
|
<div class="line"><a id="l01006" name="l01006"></a><span class="lineno"> 1006</span><span class="keywordtype">void</span> LogInfoWithoutPrefix(absl::string_view <a class="code hl_variable" href="trace_8cc.html#a36bd74109f547f7f8198faf5a12d2879">message</a>) {</div>
|
|
<div class="line"><a id="l01007" name="l01007"></a><span class="lineno"> 1007</span> <a class="code hl_class" href="classgoogle_1_1_log_message.html">google::LogMessage</a>(<span class="stringliteral">""</span>, <a class="code hl_enumvalue" href="classgoogle_1_1_log_message.html#a99fb83031ce9923c84392b4e92f956b5af8756cb52a8aac6329e43782e08f69e5">google::LogMessage::kNoLogPrefix</a>, <a class="code hl_variable" href="namespacegoogle.html#af3c2db675e75f2074724f754d3cf7885">google::GLOG_INFO</a>)</div>
|
|
<div class="line"><a id="l01008" name="l01008"></a><span class="lineno"> 1008</span> .<a class="code hl_function" href="classgoogle_1_1_log_message.html#a48387141df3f5afb48b012cc28ac244c">stream</a>()</div>
|
|
<div class="line"><a id="l01009" name="l01009"></a><span class="lineno"> 1009</span> << <a class="code hl_variable" href="trace_8cc.html#a36bd74109f547f7f8198faf5a12d2879">message</a>;</div>
|
|
<div class="line"><a id="l01010" name="l01010"></a><span class="lineno"> 1010</span>}</div>
|
|
<div class="line"><a id="l01011" name="l01011"></a><span class="lineno"> 1011</span> </div>
|
|
<div class="line"><a id="l01012" name="l01012"></a><span class="lineno"> 1012</span>absl::optional<TerminationReasonAndPointType></div>
|
|
<div class="line"><a id="l01013" name="l01013"></a><span class="lineno"> 1013</span>Solver::UpdateIterationStatsAndCheckTermination(</div>
|
|
<div class="line"><a id="l01014" name="l01014"></a><span class="lineno"> 1014</span> <span class="keywordtype">bool</span> force_numerical_termination, <span class="keyword">const</span> VectorXd& working_primal_average,</div>
|
|
<div class="line"><a id="l01015" name="l01015"></a><span class="lineno"> 1015</span> <span class="keyword">const</span> VectorXd& working_dual_average, IterationStats& stats)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l01016" name="l01016"></a><span class="lineno"> 1016</span> <span class="keywordflow">if</span> (presolve_info_.has_value()) {</div>
|
|
<div class="line"><a id="l01017" name="l01017"></a><span class="lineno"> 1017</span> { <span class="comment">// This block exists to destroy `original_current` to save RAM.</span></div>
|
|
<div class="line"><a id="l01018" name="l01018"></a><span class="lineno"> 1018</span> PrimalAndDualSolution original_current =</div>
|
|
<div class="line"><a id="l01019" name="l01019"></a><span class="lineno"> 1019</span> RecoverOriginalSolution({.primal_solution = current_primal_solution_,</div>
|
|
<div class="line"><a id="l01020" name="l01020"></a><span class="lineno"> 1020</span> .dual_solution = current_dual_solution_});</div>
|
|
<div class="line"><a id="l01021" name="l01021"></a><span class="lineno"> 1021</span> AddConvergenceAndInfeasibilityInformation(</div>
|
|
<div class="line"><a id="l01022" name="l01022"></a><span class="lineno"> 1022</span> original_current.primal_solution, original_current.dual_solution,</div>
|
|
<div class="line"><a id="l01023" name="l01023"></a><span class="lineno"> 1023</span> presolve_info_->sharded_original_qp,</div>
|
|
<div class="line"><a id="l01024" name="l01024"></a><span class="lineno"> 1024</span> presolve_info_->trivial_col_scaling_vec,</div>
|
|
<div class="line"><a id="l01025" name="l01025"></a><span class="lineno"> 1025</span> presolve_info_->trivial_row_scaling_vec, POINT_TYPE_CURRENT_ITERATE,</div>
|
|
<div class="line"><a id="l01026" name="l01026"></a><span class="lineno"> 1026</span> stats);</div>
|
|
<div class="line"><a id="l01027" name="l01027"></a><span class="lineno"> 1027</span> }</div>
|
|
<div class="line"><a id="l01028" name="l01028"></a><span class="lineno"> 1028</span> <span class="keywordflow">if</span> (primal_average_.HasNonzeroWeight()) {</div>
|
|
<div class="line"><a id="l01029" name="l01029"></a><span class="lineno"> 1029</span> PrimalAndDualSolution original_average =</div>
|
|
<div class="line"><a id="l01030" name="l01030"></a><span class="lineno"> 1030</span> RecoverOriginalSolution({.primal_solution = working_primal_average,</div>
|
|
<div class="line"><a id="l01031" name="l01031"></a><span class="lineno"> 1031</span> .dual_solution = working_dual_average});</div>
|
|
<div class="line"><a id="l01032" name="l01032"></a><span class="lineno"> 1032</span> AddConvergenceAndInfeasibilityInformation(</div>
|
|
<div class="line"><a id="l01033" name="l01033"></a><span class="lineno"> 1033</span> original_average.primal_solution, original_average.dual_solution,</div>
|
|
<div class="line"><a id="l01034" name="l01034"></a><span class="lineno"> 1034</span> presolve_info_->sharded_original_qp,</div>
|
|
<div class="line"><a id="l01035" name="l01035"></a><span class="lineno"> 1035</span> presolve_info_->trivial_col_scaling_vec,</div>
|
|
<div class="line"><a id="l01036" name="l01036"></a><span class="lineno"> 1036</span> presolve_info_->trivial_row_scaling_vec, POINT_TYPE_AVERAGE_ITERATE,</div>
|
|
<div class="line"><a id="l01037" name="l01037"></a><span class="lineno"> 1037</span> stats);</div>
|
|
<div class="line"><a id="l01038" name="l01038"></a><span class="lineno"> 1038</span> }</div>
|
|
<div class="line"><a id="l01039" name="l01039"></a><span class="lineno"> 1039</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01040" name="l01040"></a><span class="lineno"> 1040</span> AddConvergenceAndInfeasibilityInformation(</div>
|
|
<div class="line"><a id="l01041" name="l01041"></a><span class="lineno"> 1041</span> current_primal_solution_, current_dual_solution_, sharded_working_qp_,</div>
|
|
<div class="line"><a id="l01042" name="l01042"></a><span class="lineno"> 1042</span> col_scaling_vec_, row_scaling_vec_, POINT_TYPE_CURRENT_ITERATE, stats);</div>
|
|
<div class="line"><a id="l01043" name="l01043"></a><span class="lineno"> 1043</span> <span class="keywordflow">if</span> (primal_average_.HasNonzeroWeight()) {</div>
|
|
<div class="line"><a id="l01044" name="l01044"></a><span class="lineno"> 1044</span> AddConvergenceAndInfeasibilityInformation(</div>
|
|
<div class="line"><a id="l01045" name="l01045"></a><span class="lineno"> 1045</span> working_primal_average, working_dual_average, sharded_working_qp_,</div>
|
|
<div class="line"><a id="l01046" name="l01046"></a><span class="lineno"> 1046</span> col_scaling_vec_, row_scaling_vec_, POINT_TYPE_AVERAGE_ITERATE,</div>
|
|
<div class="line"><a id="l01047" name="l01047"></a><span class="lineno"> 1047</span> stats);</div>
|
|
<div class="line"><a id="l01048" name="l01048"></a><span class="lineno"> 1048</span> }</div>
|
|
<div class="line"><a id="l01049" name="l01049"></a><span class="lineno"> 1049</span> }</div>
|
|
<div class="line"><a id="l01050" name="l01050"></a><span class="lineno"> 1050</span> AddPointMetadata(current_primal_solution_, current_dual_solution_,</div>
|
|
<div class="line"><a id="l01051" name="l01051"></a><span class="lineno"> 1051</span> POINT_TYPE_CURRENT_ITERATE, stats);</div>
|
|
<div class="line"><a id="l01052" name="l01052"></a><span class="lineno"> 1052</span> <span class="keywordflow">if</span> (primal_average_.HasNonzeroWeight()) {</div>
|
|
<div class="line"><a id="l01053" name="l01053"></a><span class="lineno"> 1053</span> AddPointMetadata(working_primal_average, working_dual_average,</div>
|
|
<div class="line"><a id="l01054" name="l01054"></a><span class="lineno"> 1054</span> POINT_TYPE_AVERAGE_ITERATE, stats);</div>
|
|
<div class="line"><a id="l01055" name="l01055"></a><span class="lineno"> 1055</span> }</div>
|
|
<div class="line"><a id="l01056" name="l01056"></a><span class="lineno"> 1056</span> <span class="keywordflow">if</span> (current_primal_delta_.size() > 0 && current_dual_delta_.size() > 0) {</div>
|
|
<div class="line"><a id="l01057" name="l01057"></a><span class="lineno"> 1057</span> <span class="keywordflow">if</span> (presolve_info_.has_value()) {</div>
|
|
<div class="line"><a id="l01058" name="l01058"></a><span class="lineno"> 1058</span> PrimalAndDualSolution original_delta =</div>
|
|
<div class="line"><a id="l01059" name="l01059"></a><span class="lineno"> 1059</span> RecoverOriginalSolution({.primal_solution = current_primal_delta_,</div>
|
|
<div class="line"><a id="l01060" name="l01060"></a><span class="lineno"> 1060</span> .dual_solution = current_dual_delta_});</div>
|
|
<div class="line"><a id="l01061" name="l01061"></a><span class="lineno"> 1061</span> *stats.add_infeasibility_information() = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a74af5ceb7b6e37fbfca92e2c59b99e3e">ComputeInfeasibilityInformation</a>(</div>
|
|
<div class="line"><a id="l01062" name="l01062"></a><span class="lineno"> 1062</span> presolve_info_->sharded_original_qp,</div>
|
|
<div class="line"><a id="l01063" name="l01063"></a><span class="lineno"> 1063</span> presolve_info_->trivial_col_scaling_vec,</div>
|
|
<div class="line"><a id="l01064" name="l01064"></a><span class="lineno"> 1064</span> presolve_info_->trivial_row_scaling_vec,</div>
|
|
<div class="line"><a id="l01065" name="l01065"></a><span class="lineno"> 1065</span> original_delta.primal_solution, original_delta.dual_solution,</div>
|
|
<div class="line"><a id="l01066" name="l01066"></a><span class="lineno"> 1066</span> POINT_TYPE_ITERATE_DIFFERENCE);</div>
|
|
<div class="line"><a id="l01067" name="l01067"></a><span class="lineno"> 1067</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01068" name="l01068"></a><span class="lineno"> 1068</span> *stats.add_infeasibility_information() = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a74af5ceb7b6e37fbfca92e2c59b99e3e">ComputeInfeasibilityInformation</a>(</div>
|
|
<div class="line"><a id="l01069" name="l01069"></a><span class="lineno"> 1069</span> sharded_working_qp_, col_scaling_vec_, row_scaling_vec_,</div>
|
|
<div class="line"><a id="l01070" name="l01070"></a><span class="lineno"> 1070</span> current_primal_delta_, current_dual_delta_,</div>
|
|
<div class="line"><a id="l01071" name="l01071"></a><span class="lineno"> 1071</span> POINT_TYPE_ITERATE_DIFFERENCE);</div>
|
|
<div class="line"><a id="l01072" name="l01072"></a><span class="lineno"> 1072</span> }</div>
|
|
<div class="line"><a id="l01073" name="l01073"></a><span class="lineno"> 1073</span> AddPointMetadata(current_primal_delta_, current_dual_delta_,</div>
|
|
<div class="line"><a id="l01074" name="l01074"></a><span class="lineno"> 1074</span> POINT_TYPE_ITERATE_DIFFERENCE, stats);</div>
|
|
<div class="line"><a id="l01075" name="l01075"></a><span class="lineno"> 1075</span> }</div>
|
|
<div class="line"><a id="l01076" name="l01076"></a><span class="lineno"> 1076</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> kLogEvery = 15;</div>
|
|
<div class="line"><a id="l01077" name="l01077"></a><span class="lineno"> 1077</span> <span class="keyword">static</span> std::atomic_int log_counter{0};</div>
|
|
<div class="line"><a id="l01078" name="l01078"></a><span class="lineno"> 1078</span> <span class="keywordflow">if</span> (params_.verbosity_level() >= 4) {</div>
|
|
<div class="line"><a id="l01079" name="l01079"></a><span class="lineno"> 1079</span> <span class="keywordflow">if</span> (log_counter == 0) {</div>
|
|
<div class="line"><a id="l01080" name="l01080"></a><span class="lineno"> 1080</span> LogInfoWithoutPrefix(absl::StrCat(<span class="stringliteral">"I "</span>, IterationStatsLabelString()));</div>
|
|
<div class="line"><a id="l01081" name="l01081"></a><span class="lineno"> 1081</span> }</div>
|
|
<div class="line"><a id="l01082" name="l01082"></a><span class="lineno"> 1082</span> LogInfoWithoutPrefix(absl::StrCat(</div>
|
|
<div class="line"><a id="l01083" name="l01083"></a><span class="lineno"> 1083</span> <span class="stringliteral">"A "</span>, <a class="code hl_function" href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">ToString</a>(stats, params_.termination_criteria(),</div>
|
|
<div class="line"><a id="l01084" name="l01084"></a><span class="lineno"> 1084</span> original_bound_norms_, POINT_TYPE_AVERAGE_ITERATE)));</div>
|
|
<div class="line"><a id="l01085" name="l01085"></a><span class="lineno"> 1085</span> LogInfoWithoutPrefix(absl::StrCat(</div>
|
|
<div class="line"><a id="l01086" name="l01086"></a><span class="lineno"> 1086</span> <span class="stringliteral">"C "</span>, <a class="code hl_function" href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">ToString</a>(stats, params_.termination_criteria(),</div>
|
|
<div class="line"><a id="l01087" name="l01087"></a><span class="lineno"> 1087</span> original_bound_norms_, POINT_TYPE_CURRENT_ITERATE)));</div>
|
|
<div class="line"><a id="l01088" name="l01088"></a><span class="lineno"> 1088</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (params_.verbosity_level() >= 3) {</div>
|
|
<div class="line"><a id="l01089" name="l01089"></a><span class="lineno"> 1089</span> <span class="keywordflow">if</span> (log_counter == 0) {</div>
|
|
<div class="line"><a id="l01090" name="l01090"></a><span class="lineno"> 1090</span> LogInfoWithoutPrefix(IterationStatsLabelString());</div>
|
|
<div class="line"><a id="l01091" name="l01091"></a><span class="lineno"> 1091</span> }</div>
|
|
<div class="line"><a id="l01092" name="l01092"></a><span class="lineno"> 1092</span> LogInfoWithoutPrefix(<a class="code hl_function" href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">ToString</a>(stats, params_.termination_criteria(),</div>
|
|
<div class="line"><a id="l01093" name="l01093"></a><span class="lineno"> 1093</span> original_bound_norms_,</div>
|
|
<div class="line"><a id="l01094" name="l01094"></a><span class="lineno"> 1094</span> POINT_TYPE_AVERAGE_ITERATE));</div>
|
|
<div class="line"><a id="l01095" name="l01095"></a><span class="lineno"> 1095</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (params_.verbosity_level() >= 2) {</div>
|
|
<div class="line"><a id="l01096" name="l01096"></a><span class="lineno"> 1096</span> <span class="keywordflow">if</span> (log_counter == 0) {</div>
|
|
<div class="line"><a id="l01097" name="l01097"></a><span class="lineno"> 1097</span> LogInfoWithoutPrefix(IterationStatsLabelShortString());</div>
|
|
<div class="line"><a id="l01098" name="l01098"></a><span class="lineno"> 1098</span> }</div>
|
|
<div class="line"><a id="l01099" name="l01099"></a><span class="lineno"> 1099</span> LogInfoWithoutPrefix(ToShortString(stats, params_.termination_criteria(),</div>
|
|
<div class="line"><a id="l01100" name="l01100"></a><span class="lineno"> 1100</span> original_bound_norms_,</div>
|
|
<div class="line"><a id="l01101" name="l01101"></a><span class="lineno"> 1101</span> POINT_TYPE_AVERAGE_ITERATE));</div>
|
|
<div class="line"><a id="l01102" name="l01102"></a><span class="lineno"> 1102</span> }</div>
|
|
<div class="line"><a id="l01103" name="l01103"></a><span class="lineno"> 1103</span> <span class="keywordflow">if</span> (++log_counter >= kLogEvery) {</div>
|
|
<div class="line"><a id="l01104" name="l01104"></a><span class="lineno"> 1104</span> log_counter = 0;</div>
|
|
<div class="line"><a id="l01105" name="l01105"></a><span class="lineno"> 1105</span> }</div>
|
|
<div class="line"><a id="l01106" name="l01106"></a><span class="lineno"> 1106</span> <span class="keywordflow">if</span> (iteration_stats_callback_ != <span class="keyword">nullptr</span>) {</div>
|
|
<div class="line"><a id="l01107" name="l01107"></a><span class="lineno"> 1107</span> iteration_stats_callback_(</div>
|
|
<div class="line"><a id="l01108" name="l01108"></a><span class="lineno"> 1108</span> {.termination_criteria = params_.termination_criteria(),</div>
|
|
<div class="line"><a id="l01109" name="l01109"></a><span class="lineno"> 1109</span> .iteration_stats = stats,</div>
|
|
<div class="line"><a id="l01110" name="l01110"></a><span class="lineno"> 1110</span> .bound_norms = original_bound_norms_});</div>
|
|
<div class="line"><a id="l01111" name="l01111"></a><span class="lineno"> 1111</span> }</div>
|
|
<div class="line"><a id="l01112" name="l01112"></a><span class="lineno"> 1112</span> </div>
|
|
<div class="line"><a id="l01113" name="l01113"></a><span class="lineno"> 1113</span> <span class="keywordflow">return</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a7d2e7889c98661aba130697a142fdf4b">CheckTerminationCriteria</a>(params_.termination_criteria(), stats,</div>
|
|
<div class="line"><a id="l01114" name="l01114"></a><span class="lineno"> 1114</span> original_bound_norms_,</div>
|
|
<div class="line"><a id="l01115" name="l01115"></a><span class="lineno"> 1115</span> force_numerical_termination);</div>
|
|
<div class="line"><a id="l01116" name="l01116"></a><span class="lineno"> 1116</span>}</div>
|
|
<div class="line"><a id="l01117" name="l01117"></a><span class="lineno"> 1117</span> </div>
|
|
<div class="line"><a id="l01118" name="l01118"></a><span class="lineno"> 1118</span><span class="keywordtype">double</span> Solver::InitialPrimalWeight(</div>
|
|
<div class="line"><a id="l01119" name="l01119"></a><span class="lineno"> 1119</span> <span class="keyword">const</span> <span class="keywordtype">double</span> l2_norm_primal_linear_objective,</div>
|
|
<div class="line"><a id="l01120" name="l01120"></a><span class="lineno"> 1120</span> <span class="keyword">const</span> <span class="keywordtype">double</span> l2_norm_constraint_bounds)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l01121" name="l01121"></a><span class="lineno"> 1121</span> <span class="keywordflow">if</span> (params_.has_initial_primal_weight()) {</div>
|
|
<div class="line"><a id="l01122" name="l01122"></a><span class="lineno"> 1122</span> <span class="keywordflow">return</span> params_.initial_primal_weight();</div>
|
|
<div class="line"><a id="l01123" name="l01123"></a><span class="lineno"> 1123</span> }</div>
|
|
<div class="line"><a id="l01124" name="l01124"></a><span class="lineno"> 1124</span> <span class="keywordflow">if</span> (l2_norm_primal_linear_objective > 0.0 &&</div>
|
|
<div class="line"><a id="l01125" name="l01125"></a><span class="lineno"> 1125</span> l2_norm_constraint_bounds > 0.0) {</div>
|
|
<div class="line"><a id="l01126" name="l01126"></a><span class="lineno"> 1126</span> <span class="comment">// The hand-wavy motivation for this choice is that the objective vector</span></div>
|
|
<div class="line"><a id="l01127" name="l01127"></a><span class="lineno"> 1127</span> <span class="comment">// has units of (objective units)/(primal units) and the constraint</span></div>
|
|
<div class="line"><a id="l01128" name="l01128"></a><span class="lineno"> 1128</span> <span class="comment">// bounds vector has units of (objective units)/(dual units),</span></div>
|
|
<div class="line"><a id="l01129" name="l01129"></a><span class="lineno"> 1129</span> <span class="comment">// therefore this ratio has units (dual units)/(primal units). By</span></div>
|
|
<div class="line"><a id="l01130" name="l01130"></a><span class="lineno"> 1130</span> <span class="comment">// dimensional analysis, these are the same units as the primal weight.</span></div>
|
|
<div class="line"><a id="l01131" name="l01131"></a><span class="lineno"> 1131</span> <span class="keywordflow">return</span> l2_norm_primal_linear_objective / l2_norm_constraint_bounds;</div>
|
|
<div class="line"><a id="l01132" name="l01132"></a><span class="lineno"> 1132</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01133" name="l01133"></a><span class="lineno"> 1133</span> <span class="keywordflow">return</span> 1.0;</div>
|
|
<div class="line"><a id="l01134" name="l01134"></a><span class="lineno"> 1134</span> }</div>
|
|
<div class="line"><a id="l01135" name="l01135"></a><span class="lineno"> 1135</span>}</div>
|
|
<div class="line"><a id="l01136" name="l01136"></a><span class="lineno"> 1136</span> </div>
|
|
<div class="line"><a id="l01137" name="l01137"></a><span class="lineno"> 1137</span><span class="keywordtype">void</span> Solver::ComputeAndApplyRescaling() {</div>
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|
<div class="line"><a id="l01138" name="l01138"></a><span class="lineno"> 1138</span> ScalingVectors scaling = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#aac68304831a1bc81557fb03623a619d6">ApplyRescaling</a>(</div>
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|
<div class="line"><a id="l01139" name="l01139"></a><span class="lineno"> 1139</span> RescalingOptions{.l_inf_ruiz_iterations = params_.l_inf_ruiz_iterations(),</div>
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|
<div class="line"><a id="l01140" name="l01140"></a><span class="lineno"> 1140</span> .l2_norm_rescaling = params_.l2_norm_rescaling()},</div>
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|
<div class="line"><a id="l01141" name="l01141"></a><span class="lineno"> 1141</span> sharded_working_qp_);</div>
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|
<div class="line"><a id="l01142" name="l01142"></a><span class="lineno"> 1142</span> row_scaling_vec_ = std::move(scaling.row_scaling_vec);</div>
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|
<div class="line"><a id="l01143" name="l01143"></a><span class="lineno"> 1143</span> col_scaling_vec_ = std::move(scaling.col_scaling_vec);</div>
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|
<div class="line"><a id="l01144" name="l01144"></a><span class="lineno"> 1144</span> </div>
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|
<div class="line"><a id="l01145" name="l01145"></a><span class="lineno"> 1145</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a92c8ca6bf2bb288c322e1d8fbd6ea2bc">CoefficientWiseQuotientInPlace</a>(col_scaling_vec_,</div>
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|
<div class="line"><a id="l01146" name="l01146"></a><span class="lineno"> 1146</span> sharded_working_qp_.PrimalSharder(),</div>
|
|
<div class="line"><a id="l01147" name="l01147"></a><span class="lineno"> 1147</span> current_primal_solution_);</div>
|
|
<div class="line"><a id="l01148" name="l01148"></a><span class="lineno"> 1148</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a92c8ca6bf2bb288c322e1d8fbd6ea2bc">CoefficientWiseQuotientInPlace</a>(row_scaling_vec_,</div>
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|
<div class="line"><a id="l01149" name="l01149"></a><span class="lineno"> 1149</span> sharded_working_qp_.DualSharder(),</div>
|
|
<div class="line"><a id="l01150" name="l01150"></a><span class="lineno"> 1150</span> current_dual_solution_);</div>
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|
<div class="line"><a id="l01151" name="l01151"></a><span class="lineno"> 1151</span>}</div>
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|
<div class="line"><a id="l01152" name="l01152"></a><span class="lineno"> 1152</span> </div>
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|
<div class="line"><a id="l01153" name="l01153"></a><span class="lineno"> 1153</span><span class="keywordtype">void</span> Solver::ApplyRestartChoice(<span class="keyword">const</span> RestartChoice restart_to_apply) {</div>
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|
<div class="line"><a id="l01154" name="l01154"></a><span class="lineno"> 1154</span> <span class="keywordflow">switch</span> (restart_to_apply) {</div>
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|
<div class="line"><a id="l01155" name="l01155"></a><span class="lineno"> 1155</span> <span class="keywordflow">case</span> RESTART_CHOICE_UNSPECIFIED:</div>
|
|
<div class="line"><a id="l01156" name="l01156"></a><span class="lineno"> 1156</span> <span class="keywordflow">case</span> RESTART_CHOICE_NO_RESTART:</div>
|
|
<div class="line"><a id="l01157" name="l01157"></a><span class="lineno"> 1157</span> <span class="keywordflow">return</span>;</div>
|
|
<div class="line"><a id="l01158" name="l01158"></a><span class="lineno"> 1158</span> <span class="keywordflow">case</span> RESTART_CHOICE_WEIGHTED_AVERAGE_RESET:</div>
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|
<div class="line"><a id="l01159" name="l01159"></a><span class="lineno"> 1159</span> <a class="code hl_define" href="base_2logging_8h.html#a09f7d88282cf92c9f231270ac113e5c6">LOG_IF</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>, params_.verbosity_level() >= 4)</div>
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|
<div class="line"><a id="l01160" name="l01160"></a><span class="lineno"> 1160</span> << <span class="stringliteral">"Restarted to current on iteration "</span> << iterations_completed_</div>
|
|
<div class="line"><a id="l01161" name="l01161"></a><span class="lineno"> 1161</span> << <span class="stringliteral">" after "</span> << primal_average_.NumTerms() << <span class="stringliteral">" iterations"</span>;</div>
|
|
<div class="line"><a id="l01162" name="l01162"></a><span class="lineno"> 1162</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01163" name="l01163"></a><span class="lineno"> 1163</span> <span class="keywordflow">case</span> RESTART_CHOICE_RESTART_TO_AVERAGE:</div>
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|
<div class="line"><a id="l01164" name="l01164"></a><span class="lineno"> 1164</span> <a class="code hl_define" href="base_2logging_8h.html#a09f7d88282cf92c9f231270ac113e5c6">LOG_IF</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>, params_.verbosity_level() >= 4)</div>
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|
<div class="line"><a id="l01165" name="l01165"></a><span class="lineno"> 1165</span> << <span class="stringliteral">"Restarted to average on iteration "</span> << iterations_completed_</div>
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|
<div class="line"><a id="l01166" name="l01166"></a><span class="lineno"> 1166</span> << <span class="stringliteral">" after "</span> << primal_average_.NumTerms() << <span class="stringliteral">" iterations"</span>;</div>
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|
<div class="line"><a id="l01167" name="l01167"></a><span class="lineno"> 1167</span> current_primal_solution_ = primal_average_.ComputeAverage();</div>
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|
<div class="line"><a id="l01168" name="l01168"></a><span class="lineno"> 1168</span> current_dual_solution_ = dual_average_.ComputeAverage();</div>
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|
<div class="line"><a id="l01169" name="l01169"></a><span class="lineno"> 1169</span> current_dual_product_ = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a463586ded0a114d3ca4b97a048d37d8a">TransposedMatrixVectorProduct</a>(</div>
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|
<div class="line"><a id="l01170" name="l01170"></a><span class="lineno"> 1170</span> WorkingQp().constraint_matrix, current_dual_solution_,</div>
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|
<div class="line"><a id="l01171" name="l01171"></a><span class="lineno"> 1171</span> sharded_working_qp_.ConstraintMatrixSharder());</div>
|
|
<div class="line"><a id="l01172" name="l01172"></a><span class="lineno"> 1172</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01173" name="l01173"></a><span class="lineno"> 1173</span> }</div>
|
|
<div class="line"><a id="l01174" name="l01174"></a><span class="lineno"> 1174</span> primal_weight_ = ComputeNewPrimalWeight();</div>
|
|
<div class="line"><a id="l01175" name="l01175"></a><span class="lineno"> 1175</span> ratio_last_two_step_sizes_ = 1;</div>
|
|
<div class="line"><a id="l01176" name="l01176"></a><span class="lineno"> 1176</span> <span class="keywordflow">if</span> (params_.restart_strategy() ==</div>
|
|
<div class="line"><a id="l01177" name="l01177"></a><span class="lineno"> 1177</span> PrimalDualHybridGradientParams::ADAPTIVE_HEURISTIC) {</div>
|
|
<div class="line"><a id="l01178" name="l01178"></a><span class="lineno"> 1178</span> <span class="comment">// It's important for the theory that the distances here are calculated</span></div>
|
|
<div class="line"><a id="l01179" name="l01179"></a><span class="lineno"> 1179</span> <span class="comment">// given the new primal weight.</span></div>
|
|
<div class="line"><a id="l01180" name="l01180"></a><span class="lineno"> 1180</span> <span class="keyword">const</span> LocalizedLagrangianBounds local_bounds_at_last_restart =</div>
|
|
<div class="line"><a id="l01181" name="l01181"></a><span class="lineno"> 1181</span> ComputeLocalizedBoundsAtCurrent();</div>
|
|
<div class="line"><a id="l01182" name="l01182"></a><span class="lineno"> 1182</span> <span class="keyword">const</span> <span class="keywordtype">double</span> distance_traveled_since_last_restart =</div>
|
|
<div class="line"><a id="l01183" name="l01183"></a><span class="lineno"> 1183</span> local_bounds_at_last_restart.radius;</div>
|
|
<div class="line"><a id="l01184" name="l01184"></a><span class="lineno"> 1184</span> normalized_gap_at_last_restart_ = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afdd1506c32f697aeb13c4b9a9f05ba03">BoundGap</a>(local_bounds_at_last_restart) /</div>
|
|
<div class="line"><a id="l01185" name="l01185"></a><span class="lineno"> 1185</span> distance_traveled_since_last_restart;</div>
|
|
<div class="line"><a id="l01186" name="l01186"></a><span class="lineno"> 1186</span> normalized_gap_at_last_trial_ = std::numeric_limits<double>::infinity();</div>
|
|
<div class="line"><a id="l01187" name="l01187"></a><span class="lineno"> 1187</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (params_.restart_strategy() ==</div>
|
|
<div class="line"><a id="l01188" name="l01188"></a><span class="lineno"> 1188</span> PrimalDualHybridGradientParams::ADAPTIVE_DISTANCE_BASED) {</div>
|
|
<div class="line"><a id="l01189" name="l01189"></a><span class="lineno"> 1189</span> <span class="comment">// Update parameters for distance-based restarts.</span></div>
|
|
<div class="line"><a id="l01190" name="l01190"></a><span class="lineno"> 1190</span> distance_based_restart_info_ = {</div>
|
|
<div class="line"><a id="l01191" name="l01191"></a><span class="lineno"> 1191</span> .distance_moved_last_restart_period = DistanceTraveledFromLastStart(</div>
|
|
<div class="line"><a id="l01192" name="l01192"></a><span class="lineno"> 1192</span> current_primal_solution_, current_dual_solution_),</div>
|
|
<div class="line"><a id="l01193" name="l01193"></a><span class="lineno"> 1193</span> .length_of_last_restart_period = primal_average_.NumTerms()};</div>
|
|
<div class="line"><a id="l01194" name="l01194"></a><span class="lineno"> 1194</span> }</div>
|
|
<div class="line"><a id="l01195" name="l01195"></a><span class="lineno"> 1195</span> primal_average_.Clear();</div>
|
|
<div class="line"><a id="l01196" name="l01196"></a><span class="lineno"> 1196</span> dual_average_.Clear();</div>
|
|
<div class="line"><a id="l01197" name="l01197"></a><span class="lineno"> 1197</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afca8f74da7e8301c8aee45f33c93896c">AssignVector</a>(current_primal_solution_, sharded_working_qp_.PrimalSharder(),</div>
|
|
<div class="line"><a id="l01198" name="l01198"></a><span class="lineno"> 1198</span> <span class="comment">/*dest=*/</span>last_primal_start_point_);</div>
|
|
<div class="line"><a id="l01199" name="l01199"></a><span class="lineno"> 1199</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afca8f74da7e8301c8aee45f33c93896c">AssignVector</a>(current_dual_solution_, sharded_working_qp_.DualSharder(),</div>
|
|
<div class="line"><a id="l01200" name="l01200"></a><span class="lineno"> 1200</span> <span class="comment">/*dest=*/</span>last_dual_start_point_);</div>
|
|
<div class="line"><a id="l01201" name="l01201"></a><span class="lineno"> 1201</span>}</div>
|
|
<div class="line"><a id="l01202" name="l01202"></a><span class="lineno"> 1202</span> </div>
|
|
<div class="line"><a id="l01203" name="l01203"></a><span class="lineno"> 1203</span>absl::optional<SolverResult> Solver::MajorIterationAndTerminationCheck(</div>
|
|
<div class="line"><a id="l01204" name="l01204"></a><span class="lineno"> 1204</span> <span class="keywordtype">bool</span> force_numerical_termination, SolveLog& solve_log) {</div>
|
|
<div class="line"><a id="l01205" name="l01205"></a><span class="lineno"> 1205</span> <span class="keyword">const</span> <span class="keywordtype">int</span> iteration_limit = params_.termination_criteria().iteration_limit();</div>
|
|
<div class="line"><a id="l01206" name="l01206"></a><span class="lineno"> 1206</span> <span class="keyword">const</span> <span class="keywordtype">int</span> major_iteration_cycle =</div>
|
|
<div class="line"><a id="l01207" name="l01207"></a><span class="lineno"> 1207</span> iterations_completed_ % params_.major_iteration_frequency();</div>
|
|
<div class="line"><a id="l01208" name="l01208"></a><span class="lineno"> 1208</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> is_major_iteration =</div>
|
|
<div class="line"><a id="l01209" name="l01209"></a><span class="lineno"> 1209</span> major_iteration_cycle == 0 && iterations_completed_ > 0;</div>
|
|
<div class="line"><a id="l01210" name="l01210"></a><span class="lineno"> 1210</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> check_termination =</div>
|
|
<div class="line"><a id="l01211" name="l01211"></a><span class="lineno"> 1211</span> major_iteration_cycle % params_.termination_check_frequency() == 0 ||</div>
|
|
<div class="line"><a id="l01212" name="l01212"></a><span class="lineno"> 1212</span> iterations_completed_ == iteration_limit || force_numerical_termination;</div>
|
|
<div class="line"><a id="l01213" name="l01213"></a><span class="lineno"> 1213</span> <span class="comment">// We check termination on every major iteration.</span></div>
|
|
<div class="line"><a id="l01214" name="l01214"></a><span class="lineno"> 1214</span> <a class="code hl_define" href="base_2logging_8h.html#ae17f8119c108cf3070bad3449c7e0006">DCHECK</a>(!is_major_iteration || check_termination);</div>
|
|
<div class="line"><a id="l01215" name="l01215"></a><span class="lineno"> 1215</span> <span class="comment">// Just decide what to do for now. The actual restart, if any, is</span></div>
|
|
<div class="line"><a id="l01216" name="l01216"></a><span class="lineno"> 1216</span> <span class="comment">// performed after the termination check.</span></div>
|
|
<div class="line"><a id="l01217" name="l01217"></a><span class="lineno"> 1217</span> <span class="keyword">const</span> RestartChoice restart = force_numerical_termination</div>
|
|
<div class="line"><a id="l01218" name="l01218"></a><span class="lineno"> 1218</span> ? RESTART_CHOICE_NO_RESTART</div>
|
|
<div class="line"><a id="l01219" name="l01219"></a><span class="lineno"> 1219</span> : ChooseRestartToApply(is_major_iteration);</div>
|
|
<div class="line"><a id="l01220" name="l01220"></a><span class="lineno"> 1220</span> IterationStats stats = CreateSimpleIterationStats(restart);</div>
|
|
<div class="line"><a id="l01221" name="l01221"></a><span class="lineno"> 1221</span> <span class="keywordflow">if</span> (check_termination) {</div>
|
|
<div class="line"><a id="l01222" name="l01222"></a><span class="lineno"> 1222</span> <span class="comment">// Check for termination and update iteration stats with both simple and</span></div>
|
|
<div class="line"><a id="l01223" name="l01223"></a><span class="lineno"> 1223</span> <span class="comment">// solution statistics. The later are computationally harder to compute and</span></div>
|
|
<div class="line"><a id="l01224" name="l01224"></a><span class="lineno"> 1224</span> <span class="comment">// hence only computed here.</span></div>
|
|
<div class="line"><a id="l01225" name="l01225"></a><span class="lineno"> 1225</span> VectorXd primal_average = PrimalAverage();</div>
|
|
<div class="line"><a id="l01226" name="l01226"></a><span class="lineno"> 1226</span> VectorXd dual_average = DualAverage();</div>
|
|
<div class="line"><a id="l01227" name="l01227"></a><span class="lineno"> 1227</span> </div>
|
|
<div class="line"><a id="l01228" name="l01228"></a><span class="lineno"> 1228</span> <span class="keyword">const</span> absl::optional<TerminationReasonAndPointType></div>
|
|
<div class="line"><a id="l01229" name="l01229"></a><span class="lineno"> 1229</span> maybe_termination_reason = UpdateIterationStatsAndCheckTermination(</div>
|
|
<div class="line"><a id="l01230" name="l01230"></a><span class="lineno"> 1230</span> force_numerical_termination, primal_average, dual_average, stats);</div>
|
|
<div class="line"><a id="l01231" name="l01231"></a><span class="lineno"> 1231</span> <span class="keywordflow">if</span> (params_.record_iteration_stats()) {</div>
|
|
<div class="line"><a id="l01232" name="l01232"></a><span class="lineno"> 1232</span> *solve_log.add_iteration_stats() = stats;</div>
|
|
<div class="line"><a id="l01233" name="l01233"></a><span class="lineno"> 1233</span> }</div>
|
|
<div class="line"><a id="l01234" name="l01234"></a><span class="lineno"> 1234</span> <span class="comment">// We've terminated.</span></div>
|
|
<div class="line"><a id="l01235" name="l01235"></a><span class="lineno"> 1235</span> <span class="keywordflow">if</span> (maybe_termination_reason.has_value()) {</div>
|
|
<div class="line"><a id="l01236" name="l01236"></a><span class="lineno"> 1236</span> <span class="keywordflow">return</span> ConstructSolverResult(std::move(primal_average),</div>
|
|
<div class="line"><a id="l01237" name="l01237"></a><span class="lineno"> 1237</span> std::move(dual_average), stats,</div>
|
|
<div class="line"><a id="l01238" name="l01238"></a><span class="lineno"> 1238</span> maybe_termination_reason->reason,</div>
|
|
<div class="line"><a id="l01239" name="l01239"></a><span class="lineno"> 1239</span> maybe_termination_reason->type, solve_log);</div>
|
|
<div class="line"><a id="l01240" name="l01240"></a><span class="lineno"> 1240</span> }</div>
|
|
<div class="line"><a id="l01241" name="l01241"></a><span class="lineno"> 1241</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (params_.record_iteration_stats()) {</div>
|
|
<div class="line"><a id="l01242" name="l01242"></a><span class="lineno"> 1242</span> <span class="comment">// Record simple iteration stats only.</span></div>
|
|
<div class="line"><a id="l01243" name="l01243"></a><span class="lineno"> 1243</span> *solve_log.add_iteration_stats() = stats;</div>
|
|
<div class="line"><a id="l01244" name="l01244"></a><span class="lineno"> 1244</span> }</div>
|
|
<div class="line"><a id="l01245" name="l01245"></a><span class="lineno"> 1245</span> ApplyRestartChoice(restart);</div>
|
|
<div class="line"><a id="l01246" name="l01246"></a><span class="lineno"> 1246</span> <span class="keywordflow">return</span> absl::nullopt;</div>
|
|
<div class="line"><a id="l01247" name="l01247"></a><span class="lineno"> 1247</span>}</div>
|
|
<div class="line"><a id="l01248" name="l01248"></a><span class="lineno"> 1248</span> </div>
|
|
<div class="line"><a id="l01249" name="l01249"></a><span class="lineno"> 1249</span><span class="keywordtype">void</span> Solver::ResetAverageToCurrent() {</div>
|
|
<div class="line"><a id="l01250" name="l01250"></a><span class="lineno"> 1250</span> primal_average_.Clear();</div>
|
|
<div class="line"><a id="l01251" name="l01251"></a><span class="lineno"> 1251</span> dual_average_.Clear();</div>
|
|
<div class="line"><a id="l01252" name="l01252"></a><span class="lineno"> 1252</span> primal_average_.Add(current_primal_solution_, <span class="comment">/*weight=*/</span>1.0);</div>
|
|
<div class="line"><a id="l01253" name="l01253"></a><span class="lineno"> 1253</span> dual_average_.Add(current_dual_solution_, <span class="comment">/*weight=*/</span>1.0);</div>
|
|
<div class="line"><a id="l01254" name="l01254"></a><span class="lineno"> 1254</span>}</div>
|
|
<div class="line"><a id="l01255" name="l01255"></a><span class="lineno"> 1255</span> </div>
|
|
<div class="line"><a id="l01256" name="l01256"></a><span class="lineno"> 1256</span><span class="keywordtype">void</span> Solver::LogNumericalTermination()<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l01257" name="l01257"></a><span class="lineno"> 1257</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>) << <span class="stringliteral">"Forced numerical termination at iteration "</span></div>
|
|
<div class="line"><a id="l01258" name="l01258"></a><span class="lineno"> 1258</span> << iterations_completed_;</div>
|
|
<div class="line"><a id="l01259" name="l01259"></a><span class="lineno"> 1259</span>}</div>
|
|
<div class="line"><a id="l01260" name="l01260"></a><span class="lineno"> 1260</span> </div>
|
|
<div class="line"><a id="l01261" name="l01261"></a><span class="lineno"> 1261</span><span class="keywordtype">void</span> Solver::LogInnerIterationLimitHit()<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l01262" name="l01262"></a><span class="lineno"> 1262</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>) << <span class="stringliteral">"Inner iteration limit reached at iteration "</span></div>
|
|
<div class="line"><a id="l01263" name="l01263"></a><span class="lineno"> 1263</span> << iterations_completed_;</div>
|
|
<div class="line"><a id="l01264" name="l01264"></a><span class="lineno"> 1264</span>}</div>
|
|
<div class="line"><a id="l01265" name="l01265"></a><span class="lineno"> 1265</span> </div>
|
|
<div class="line"><a id="l01266" name="l01266"></a><span class="lineno"> 1266</span><span class="keywordtype">void</span> Solver::LogQuadraticProgramStats(<span class="keyword">const</span> QuadraticProgramStats& stats) {</div>
|
|
<div class="line"><a id="l01267" name="l01267"></a><span class="lineno"> 1267</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << absl::StrFormat(</div>
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|
<div class="line"><a id="l01268" name="l01268"></a><span class="lineno"> 1268</span> <span class="stringliteral">"There are %i variables, %i constraints, and %i "</span>,</div>
|
|
<div class="line"><a id="l01269" name="l01269"></a><span class="lineno"> 1269</span> stats.num_variables(), stats.num_constraints(),</div>
|
|
<div class="line"><a id="l01270" name="l01270"></a><span class="lineno"> 1270</span> stats.constraint_matrix_num_nonzeros())</div>
|
|
<div class="line"><a id="l01271" name="l01271"></a><span class="lineno"> 1271</span> << <span class="stringliteral">"constraint matrix nonzeros."</span>;</div>
|
|
<div class="line"><a id="l01272" name="l01272"></a><span class="lineno"> 1272</span> <span class="keywordflow">if</span> (WorkingQp().constraint_matrix.nonZeros() > 0) {</div>
|
|
<div class="line"><a id="l01273" name="l01273"></a><span class="lineno"> 1273</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Absolute values of nonzero constraint matrix elements: "</span></div>
|
|
<div class="line"><a id="l01274" name="l01274"></a><span class="lineno"> 1274</span> << absl::StrFormat(<span class="stringliteral">"largest=%f, smallest=%f, avg=%f"</span>,</div>
|
|
<div class="line"><a id="l01275" name="l01275"></a><span class="lineno"> 1275</span> stats.constraint_matrix_abs_max(),</div>
|
|
<div class="line"><a id="l01276" name="l01276"></a><span class="lineno"> 1276</span> stats.constraint_matrix_abs_min(),</div>
|
|
<div class="line"><a id="l01277" name="l01277"></a><span class="lineno"> 1277</span> stats.constraint_matrix_abs_avg());</div>
|
|
<div class="line"><a id="l01278" name="l01278"></a><span class="lineno"> 1278</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Constraint matrix, infinity norm: "</span></div>
|
|
<div class="line"><a id="l01279" name="l01279"></a><span class="lineno"> 1279</span> << absl::StrFormat(<span class="stringliteral">"max(row & col)=%f, min_col=%f, min_row=%f"</span>,</div>
|
|
<div class="line"><a id="l01280" name="l01280"></a><span class="lineno"> 1280</span> stats.constraint_matrix_abs_max(),</div>
|
|
<div class="line"><a id="l01281" name="l01281"></a><span class="lineno"> 1281</span> stats.constraint_matrix_col_min_l_inf_norm(),</div>
|
|
<div class="line"><a id="l01282" name="l01282"></a><span class="lineno"> 1282</span> stats.constraint_matrix_row_min_l_inf_norm());</div>
|
|
<div class="line"><a id="l01283" name="l01283"></a><span class="lineno"> 1283</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Constraint bounds statistics (max absolute value per row): "</span></div>
|
|
<div class="line"><a id="l01284" name="l01284"></a><span class="lineno"> 1284</span> << absl::StrFormat(<span class="stringliteral">"largest=%f, smallest=%f, avg=%f, l2_norm=%f"</span>,</div>
|
|
<div class="line"><a id="l01285" name="l01285"></a><span class="lineno"> 1285</span> stats.combined_bounds_max(),</div>
|
|
<div class="line"><a id="l01286" name="l01286"></a><span class="lineno"> 1286</span> stats.combined_bounds_min(),</div>
|
|
<div class="line"><a id="l01287" name="l01287"></a><span class="lineno"> 1287</span> stats.combined_bounds_avg(),</div>
|
|
<div class="line"><a id="l01288" name="l01288"></a><span class="lineno"> 1288</span> stats.combined_bounds_l2_norm());</div>
|
|
<div class="line"><a id="l01289" name="l01289"></a><span class="lineno"> 1289</span> }</div>
|
|
<div class="line"><a id="l01290" name="l01290"></a><span class="lineno"> 1290</span> <span class="keywordflow">if</span> (!<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a850865b3deabb2a623e130691df99f15">IsLinearProgram</a>(WorkingQp())) {</div>
|
|
<div class="line"><a id="l01291" name="l01291"></a><span class="lineno"> 1291</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << absl::StrFormat(</div>
|
|
<div class="line"><a id="l01292" name="l01292"></a><span class="lineno"> 1292</span> <span class="stringliteral">"There are %i nonzero diagonal coefficients in the objective matrix."</span>,</div>
|
|
<div class="line"><a id="l01293" name="l01293"></a><span class="lineno"> 1293</span> stats.objective_matrix_num_nonzeros());</div>
|
|
<div class="line"><a id="l01294" name="l01294"></a><span class="lineno"> 1294</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Absolute values of nonzero objective matrix elements: "</span></div>
|
|
<div class="line"><a id="l01295" name="l01295"></a><span class="lineno"> 1295</span> << absl::StrFormat(<span class="stringliteral">"largest=%f, smallest=%f, avg=%f"</span>,</div>
|
|
<div class="line"><a id="l01296" name="l01296"></a><span class="lineno"> 1296</span> stats.objective_matrix_abs_max(),</div>
|
|
<div class="line"><a id="l01297" name="l01297"></a><span class="lineno"> 1297</span> stats.objective_matrix_abs_min(),</div>
|
|
<div class="line"><a id="l01298" name="l01298"></a><span class="lineno"> 1298</span> stats.objective_matrix_abs_avg());</div>
|
|
<div class="line"><a id="l01299" name="l01299"></a><span class="lineno"> 1299</span> }</div>
|
|
<div class="line"><a id="l01300" name="l01300"></a><span class="lineno"> 1300</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Absolute values of objective vector elements: "</span></div>
|
|
<div class="line"><a id="l01301" name="l01301"></a><span class="lineno"> 1301</span> << absl::StrFormat(<span class="stringliteral">"largest=%f, smallest=%f, avg=%f, l2_norm=%f"</span>,</div>
|
|
<div class="line"><a id="l01302" name="l01302"></a><span class="lineno"> 1302</span> stats.objective_vector_abs_max(),</div>
|
|
<div class="line"><a id="l01303" name="l01303"></a><span class="lineno"> 1303</span> stats.objective_vector_abs_min(),</div>
|
|
<div class="line"><a id="l01304" name="l01304"></a><span class="lineno"> 1304</span> stats.objective_vector_abs_avg(),</div>
|
|
<div class="line"><a id="l01305" name="l01305"></a><span class="lineno"> 1305</span> stats.objective_vector_l2_norm());</div>
|
|
<div class="line"><a id="l01306" name="l01306"></a><span class="lineno"> 1306</span> </div>
|
|
<div class="line"><a id="l01307" name="l01307"></a><span class="lineno"> 1307</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Gaps between variable upper and lower bounds: "</span></div>
|
|
<div class="line"><a id="l01308" name="l01308"></a><span class="lineno"> 1308</span> << absl::StrFormat(</div>
|
|
<div class="line"><a id="l01309" name="l01309"></a><span class="lineno"> 1309</span> <span class="stringliteral">"#finite=%i of %i, largest=%f, smallest=%f, avg=%f"</span>,</div>
|
|
<div class="line"><a id="l01310" name="l01310"></a><span class="lineno"> 1310</span> stats.variable_bound_gaps_num_finite(),</div>
|
|
<div class="line"><a id="l01311" name="l01311"></a><span class="lineno"> 1311</span> stats.num_variables(), stats.variable_bound_gaps_max(),</div>
|
|
<div class="line"><a id="l01312" name="l01312"></a><span class="lineno"> 1312</span> stats.variable_bound_gaps_min(),</div>
|
|
<div class="line"><a id="l01313" name="l01313"></a><span class="lineno"> 1313</span> stats.variable_bound_gaps_avg());</div>
|
|
<div class="line"><a id="l01314" name="l01314"></a><span class="lineno"> 1314</span>}</div>
|
|
<div class="line"><a id="l01315" name="l01315"></a><span class="lineno"> 1315</span> </div>
|
|
<div class="line"><a id="l01316" name="l01316"></a><span class="lineno"> 1316</span>InnerStepOutcome Solver::TakeMalitskyPockStep() {</div>
|
|
<div class="line"><a id="l01317" name="l01317"></a><span class="lineno"> 1317</span> InnerStepOutcome outcome = InnerStepOutcome::kSuccessful;</div>
|
|
<div class="line"><a id="l01318" name="l01318"></a><span class="lineno"> 1318</span> <span class="keyword">const</span> <span class="keywordtype">double</span> primal_step_size = step_size_ / primal_weight_;</div>
|
|
<div class="line"><a id="l01319" name="l01319"></a><span class="lineno"> 1319</span> NextSolutionAndDelta next_primal_solution =</div>
|
|
<div class="line"><a id="l01320" name="l01320"></a><span class="lineno"> 1320</span> ComputeNextPrimalSolution(primal_step_size);</div>
|
|
<div class="line"><a id="l01321" name="l01321"></a><span class="lineno"> 1321</span> <span class="comment">// The theory by Malitsky and Pock holds for any new_step_size in the interval</span></div>
|
|
<div class="line"><a id="l01322" name="l01322"></a><span class="lineno"> 1322</span> <span class="comment">// [step_size, step_size * sqrt(1 + theta)]. The dilating coefficient</span></div>
|
|
<div class="line"><a id="l01323" name="l01323"></a><span class="lineno"> 1323</span> <span class="comment">// determines where in this interval the new step size lands.</span></div>
|
|
<div class="line"><a id="l01324" name="l01324"></a><span class="lineno"> 1324</span> <span class="keywordtype">double</span> dilating_coeff =</div>
|
|
<div class="line"><a id="l01325" name="l01325"></a><span class="lineno"> 1325</span> 1 + (params_.malitsky_pock_parameters().step_size_interpolation() *</div>
|
|
<div class="line"><a id="l01326" name="l01326"></a><span class="lineno"> 1326</span> (sqrt(1 + ratio_last_two_step_sizes_) - 1));</div>
|
|
<div class="line"><a id="l01327" name="l01327"></a><span class="lineno"> 1327</span> <span class="keywordtype">double</span> new_primal_step_size = primal_step_size * dilating_coeff;</div>
|
|
<div class="line"><a id="l01328" name="l01328"></a><span class="lineno"> 1328</span> <span class="keywordtype">double</span> step_size_downscaling =</div>
|
|
<div class="line"><a id="l01329" name="l01329"></a><span class="lineno"> 1329</span> params_.malitsky_pock_parameters().step_size_downscaling_factor();</div>
|
|
<div class="line"><a id="l01330" name="l01330"></a><span class="lineno"> 1330</span> <span class="keywordtype">double</span> contraction_factor =</div>
|
|
<div class="line"><a id="l01331" name="l01331"></a><span class="lineno"> 1331</span> params_.malitsky_pock_parameters().linesearch_contraction_factor();</div>
|
|
<div class="line"><a id="l01332" name="l01332"></a><span class="lineno"> 1332</span> <span class="keyword">const</span> <span class="keywordtype">double</span> dual_weight = primal_weight_ * primal_weight_;</div>
|
|
<div class="line"><a id="l01333" name="l01333"></a><span class="lineno"> 1333</span> <span class="keywordtype">int</span> inner_iterations = 0;</div>
|
|
<div class="line"><a id="l01334" name="l01334"></a><span class="lineno"> 1334</span> <span class="keywordflow">for</span> (<span class="keywordtype">bool</span> accepted_step = <span class="keyword">false</span>; !accepted_step; ++inner_iterations) {</div>
|
|
<div class="line"><a id="l01335" name="l01335"></a><span class="lineno"> 1335</span> <span class="keywordflow">if</span> (inner_iterations >= 60) {</div>
|
|
<div class="line"><a id="l01336" name="l01336"></a><span class="lineno"> 1336</span> LogInnerIterationLimitHit();</div>
|
|
<div class="line"><a id="l01337" name="l01337"></a><span class="lineno"> 1337</span> ResetAverageToCurrent();</div>
|
|
<div class="line"><a id="l01338" name="l01338"></a><span class="lineno"> 1338</span> outcome = InnerStepOutcome::kForceNumericalTermination;</div>
|
|
<div class="line"><a id="l01339" name="l01339"></a><span class="lineno"> 1339</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01340" name="l01340"></a><span class="lineno"> 1340</span> }</div>
|
|
<div class="line"><a id="l01341" name="l01341"></a><span class="lineno"> 1341</span> <span class="keyword">const</span> <span class="keywordtype">double</span> new_last_two_step_sizes_ratio =</div>
|
|
<div class="line"><a id="l01342" name="l01342"></a><span class="lineno"> 1342</span> new_primal_step_size / primal_step_size;</div>
|
|
<div class="line"><a id="l01343" name="l01343"></a><span class="lineno"> 1343</span> NextSolutionAndDelta next_dual_solution = ComputeNextDualSolution(</div>
|
|
<div class="line"><a id="l01344" name="l01344"></a><span class="lineno"> 1344</span> dual_weight * new_primal_step_size, new_last_two_step_sizes_ratio,</div>
|
|
<div class="line"><a id="l01345" name="l01345"></a><span class="lineno"> 1345</span> next_primal_solution);</div>
|
|
<div class="line"><a id="l01346" name="l01346"></a><span class="lineno"> 1346</span> </div>
|
|
<div class="line"><a id="l01347" name="l01347"></a><span class="lineno"> 1347</span> VectorXd next_dual_product = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a463586ded0a114d3ca4b97a048d37d8a">TransposedMatrixVectorProduct</a>(</div>
|
|
<div class="line"><a id="l01348" name="l01348"></a><span class="lineno"> 1348</span> WorkingQp().constraint_matrix, next_dual_solution.value,</div>
|
|
<div class="line"><a id="l01349" name="l01349"></a><span class="lineno"> 1349</span> sharded_working_qp_.ConstraintMatrixSharder());</div>
|
|
<div class="line"><a id="l01350" name="l01350"></a><span class="lineno"> 1350</span> <span class="keywordtype">double</span> delta_dual_norm =</div>
|
|
<div class="line"><a id="l01351" name="l01351"></a><span class="lineno"> 1351</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ade56a0bd875b06000c45e1730398e5a8">Norm</a>(next_dual_solution.delta, sharded_working_qp_.DualSharder());</div>
|
|
<div class="line"><a id="l01352" name="l01352"></a><span class="lineno"> 1352</span> <span class="keywordtype">double</span> delta_dual_prod_norm =</div>
|
|
<div class="line"><a id="l01353" name="l01353"></a><span class="lineno"> 1353</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a3e28f45b9c1ccdec8d926b4034d3679b">Distance</a>(current_dual_product_, next_dual_product,</div>
|
|
<div class="line"><a id="l01354" name="l01354"></a><span class="lineno"> 1354</span> sharded_working_qp_.PrimalSharder());</div>
|
|
<div class="line"><a id="l01355" name="l01355"></a><span class="lineno"> 1355</span> <span class="keywordflow">if</span> (primal_weight_ * new_primal_step_size * delta_dual_prod_norm <=</div>
|
|
<div class="line"><a id="l01356" name="l01356"></a><span class="lineno"> 1356</span> contraction_factor * delta_dual_norm) {</div>
|
|
<div class="line"><a id="l01357" name="l01357"></a><span class="lineno"> 1357</span> <span class="comment">// Accept new_step_size as a good step.</span></div>
|
|
<div class="line"><a id="l01358" name="l01358"></a><span class="lineno"> 1358</span> step_size_ = new_primal_step_size * primal_weight_;</div>
|
|
<div class="line"><a id="l01359" name="l01359"></a><span class="lineno"> 1359</span> ratio_last_two_step_sizes_ = new_last_two_step_sizes_ratio;</div>
|
|
<div class="line"><a id="l01360" name="l01360"></a><span class="lineno"> 1360</span> <span class="comment">// Malitsky and Pock guarantee uses a nonsymmetric weighted average,</span></div>
|
|
<div class="line"><a id="l01361" name="l01361"></a><span class="lineno"> 1361</span> <span class="comment">// the primal variable average involves the initial point, while the dual</span></div>
|
|
<div class="line"><a id="l01362" name="l01362"></a><span class="lineno"> 1362</span> <span class="comment">// doesn't. See Theorem 2 in https://arxiv.org/pdf/1608.08883.pdf for</span></div>
|
|
<div class="line"><a id="l01363" name="l01363"></a><span class="lineno"> 1363</span> <span class="comment">// details.</span></div>
|
|
<div class="line"><a id="l01364" name="l01364"></a><span class="lineno"> 1364</span> <span class="keywordflow">if</span> (!primal_average_.HasNonzeroWeight()) {</div>
|
|
<div class="line"><a id="l01365" name="l01365"></a><span class="lineno"> 1365</span> primal_average_.Add(</div>
|
|
<div class="line"><a id="l01366" name="l01366"></a><span class="lineno"> 1366</span> current_primal_solution_,</div>
|
|
<div class="line"><a id="l01367" name="l01367"></a><span class="lineno"> 1367</span> <span class="comment">/*weight=*/</span>new_primal_step_size * new_last_two_step_sizes_ratio);</div>
|
|
<div class="line"><a id="l01368" name="l01368"></a><span class="lineno"> 1368</span> }</div>
|
|
<div class="line"><a id="l01369" name="l01369"></a><span class="lineno"> 1369</span> </div>
|
|
<div class="line"><a id="l01370" name="l01370"></a><span class="lineno"> 1370</span> current_primal_solution_ = std::move(next_primal_solution.value);</div>
|
|
<div class="line"><a id="l01371" name="l01371"></a><span class="lineno"> 1371</span> current_dual_solution_ = std::move(next_dual_solution.value);</div>
|
|
<div class="line"><a id="l01372" name="l01372"></a><span class="lineno"> 1372</span> current_dual_product_ = std::move(next_dual_product);</div>
|
|
<div class="line"><a id="l01373" name="l01373"></a><span class="lineno"> 1373</span> primal_average_.Add(current_primal_solution_,</div>
|
|
<div class="line"><a id="l01374" name="l01374"></a><span class="lineno"> 1374</span> <span class="comment">/*weight=*/</span>new_primal_step_size);</div>
|
|
<div class="line"><a id="l01375" name="l01375"></a><span class="lineno"> 1375</span> dual_average_.Add(current_dual_solution_,</div>
|
|
<div class="line"><a id="l01376" name="l01376"></a><span class="lineno"> 1376</span> <span class="comment">/*weight=*/</span>new_primal_step_size);</div>
|
|
<div class="line"><a id="l01377" name="l01377"></a><span class="lineno"> 1377</span> <span class="keyword">const</span> <span class="keywordtype">double</span> movement =</div>
|
|
<div class="line"><a id="l01378" name="l01378"></a><span class="lineno"> 1378</span> ComputeMovement(next_primal_solution.delta, next_dual_solution.delta);</div>
|
|
<div class="line"><a id="l01379" name="l01379"></a><span class="lineno"> 1379</span> <span class="keywordflow">if</span> (movement == 0.0) {</div>
|
|
<div class="line"><a id="l01380" name="l01380"></a><span class="lineno"> 1380</span> LogNumericalTermination();</div>
|
|
<div class="line"><a id="l01381" name="l01381"></a><span class="lineno"> 1381</span> ResetAverageToCurrent();</div>
|
|
<div class="line"><a id="l01382" name="l01382"></a><span class="lineno"> 1382</span> outcome = InnerStepOutcome::kForceNumericalTermination;</div>
|
|
<div class="line"><a id="l01383" name="l01383"></a><span class="lineno"> 1383</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (movement > kDivergentMovement) {</div>
|
|
<div class="line"><a id="l01384" name="l01384"></a><span class="lineno"> 1384</span> LogNumericalTermination();</div>
|
|
<div class="line"><a id="l01385" name="l01385"></a><span class="lineno"> 1385</span> outcome = InnerStepOutcome::kForceNumericalTermination;</div>
|
|
<div class="line"><a id="l01386" name="l01386"></a><span class="lineno"> 1386</span> }</div>
|
|
<div class="line"><a id="l01387" name="l01387"></a><span class="lineno"> 1387</span> current_primal_delta_ = std::move(next_primal_solution.delta);</div>
|
|
<div class="line"><a id="l01388" name="l01388"></a><span class="lineno"> 1388</span> current_dual_delta_ = std::move(next_dual_solution.delta);</div>
|
|
<div class="line"><a id="l01389" name="l01389"></a><span class="lineno"> 1389</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01390" name="l01390"></a><span class="lineno"> 1390</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01391" name="l01391"></a><span class="lineno"> 1391</span> new_primal_step_size = step_size_downscaling * new_primal_step_size;</div>
|
|
<div class="line"><a id="l01392" name="l01392"></a><span class="lineno"> 1392</span> }</div>
|
|
<div class="line"><a id="l01393" name="l01393"></a><span class="lineno"> 1393</span> }</div>
|
|
<div class="line"><a id="l01394" name="l01394"></a><span class="lineno"> 1394</span> <span class="comment">// inner_iterations isn't incremented for the accepted step.</span></div>
|
|
<div class="line"><a id="l01395" name="l01395"></a><span class="lineno"> 1395</span> num_rejected_steps_ += inner_iterations;</div>
|
|
<div class="line"><a id="l01396" name="l01396"></a><span class="lineno"> 1396</span> <span class="keywordflow">return</span> outcome;</div>
|
|
<div class="line"><a id="l01397" name="l01397"></a><span class="lineno"> 1397</span>}</div>
|
|
<div class="line"><a id="l01398" name="l01398"></a><span class="lineno"> 1398</span> </div>
|
|
<div class="line"><a id="l01399" name="l01399"></a><span class="lineno"> 1399</span>InnerStepOutcome Solver::TakeAdaptiveStep() {</div>
|
|
<div class="line"><a id="l01400" name="l01400"></a><span class="lineno"> 1400</span> <span class="keywordtype">bool</span> force_numerical_termination = <span class="keyword">false</span>;</div>
|
|
<div class="line"><a id="l01401" name="l01401"></a><span class="lineno"> 1401</span> <span class="keywordflow">for</span> (<span class="keywordtype">bool</span> accepted_step = <span class="keyword">false</span>; !accepted_step;) {</div>
|
|
<div class="line"><a id="l01402" name="l01402"></a><span class="lineno"> 1402</span> <span class="keyword">const</span> <span class="keywordtype">double</span> primal_step_size = step_size_ / primal_weight_;</div>
|
|
<div class="line"><a id="l01403" name="l01403"></a><span class="lineno"> 1403</span> <span class="keyword">const</span> <span class="keywordtype">double</span> dual_step_size = step_size_ * primal_weight_;</div>
|
|
<div class="line"><a id="l01404" name="l01404"></a><span class="lineno"> 1404</span> NextSolutionAndDelta next_primal_solution =</div>
|
|
<div class="line"><a id="l01405" name="l01405"></a><span class="lineno"> 1405</span> ComputeNextPrimalSolution(primal_step_size);</div>
|
|
<div class="line"><a id="l01406" name="l01406"></a><span class="lineno"> 1406</span> NextSolutionAndDelta next_dual_solution = ComputeNextDualSolution(</div>
|
|
<div class="line"><a id="l01407" name="l01407"></a><span class="lineno"> 1407</span> dual_step_size, <span class="comment">/*extrapolation_factor=*/</span>1.0, next_primal_solution);</div>
|
|
<div class="line"><a id="l01408" name="l01408"></a><span class="lineno"> 1408</span> <span class="keyword">const</span> <span class="keywordtype">double</span> movement =</div>
|
|
<div class="line"><a id="l01409" name="l01409"></a><span class="lineno"> 1409</span> ComputeMovement(next_primal_solution.delta, next_dual_solution.delta);</div>
|
|
<div class="line"><a id="l01410" name="l01410"></a><span class="lineno"> 1410</span> <span class="keywordflow">if</span> (movement == 0.0) {</div>
|
|
<div class="line"><a id="l01411" name="l01411"></a><span class="lineno"> 1411</span> LogNumericalTermination();</div>
|
|
<div class="line"><a id="l01412" name="l01412"></a><span class="lineno"> 1412</span> ResetAverageToCurrent();</div>
|
|
<div class="line"><a id="l01413" name="l01413"></a><span class="lineno"> 1413</span> force_numerical_termination = <span class="keyword">true</span>;</div>
|
|
<div class="line"><a id="l01414" name="l01414"></a><span class="lineno"> 1414</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01415" name="l01415"></a><span class="lineno"> 1415</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (movement > kDivergentMovement) {</div>
|
|
<div class="line"><a id="l01416" name="l01416"></a><span class="lineno"> 1416</span> LogNumericalTermination();</div>
|
|
<div class="line"><a id="l01417" name="l01417"></a><span class="lineno"> 1417</span> force_numerical_termination = <span class="keyword">true</span>;</div>
|
|
<div class="line"><a id="l01418" name="l01418"></a><span class="lineno"> 1418</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01419" name="l01419"></a><span class="lineno"> 1419</span> }</div>
|
|
<div class="line"><a id="l01420" name="l01420"></a><span class="lineno"> 1420</span> VectorXd next_dual_product = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a463586ded0a114d3ca4b97a048d37d8a">TransposedMatrixVectorProduct</a>(</div>
|
|
<div class="line"><a id="l01421" name="l01421"></a><span class="lineno"> 1421</span> WorkingQp().constraint_matrix, next_dual_solution.value,</div>
|
|
<div class="line"><a id="l01422" name="l01422"></a><span class="lineno"> 1422</span> sharded_working_qp_.ConstraintMatrixSharder());</div>
|
|
<div class="line"><a id="l01423" name="l01423"></a><span class="lineno"> 1423</span> <span class="keyword">const</span> <span class="keywordtype">double</span> nonlinearity =</div>
|
|
<div class="line"><a id="l01424" name="l01424"></a><span class="lineno"> 1424</span> ComputeNonlinearity(next_primal_solution.delta, next_dual_product);</div>
|
|
<div class="line"><a id="l01425" name="l01425"></a><span class="lineno"> 1425</span> </div>
|
|
<div class="line"><a id="l01426" name="l01426"></a><span class="lineno"> 1426</span> <span class="comment">// See equation (5) in https://arxiv.org/pdf/2106.04756.pdf.</span></div>
|
|
<div class="line"><a id="l01427" name="l01427"></a><span class="lineno"> 1427</span> <span class="keyword">const</span> <span class="keywordtype">double</span> step_size_limit =</div>
|
|
<div class="line"><a id="l01428" name="l01428"></a><span class="lineno"> 1428</span> nonlinearity > 0 ? movement / nonlinearity</div>
|
|
<div class="line"><a id="l01429" name="l01429"></a><span class="lineno"> 1429</span> : std::numeric_limits<double>::infinity();</div>
|
|
<div class="line"><a id="l01430" name="l01430"></a><span class="lineno"> 1430</span> </div>
|
|
<div class="line"><a id="l01431" name="l01431"></a><span class="lineno"> 1431</span> <span class="keywordflow">if</span> (step_size_ <= step_size_limit) {</div>
|
|
<div class="line"><a id="l01432" name="l01432"></a><span class="lineno"> 1432</span> current_primal_solution_ = std::move(next_primal_solution.value);</div>
|
|
<div class="line"><a id="l01433" name="l01433"></a><span class="lineno"> 1433</span> current_dual_solution_ = std::move(next_dual_solution.value);</div>
|
|
<div class="line"><a id="l01434" name="l01434"></a><span class="lineno"> 1434</span> current_dual_product_ = std::move(next_dual_product);</div>
|
|
<div class="line"><a id="l01435" name="l01435"></a><span class="lineno"> 1435</span> current_primal_delta_ = std::move(next_primal_solution.delta);</div>
|
|
<div class="line"><a id="l01436" name="l01436"></a><span class="lineno"> 1436</span> current_dual_delta_ = std::move(next_dual_solution.delta);</div>
|
|
<div class="line"><a id="l01437" name="l01437"></a><span class="lineno"> 1437</span> primal_average_.Add(current_primal_solution_, <span class="comment">/*weight=*/</span>step_size_);</div>
|
|
<div class="line"><a id="l01438" name="l01438"></a><span class="lineno"> 1438</span> dual_average_.Add(current_dual_solution_, <span class="comment">/*weight=*/</span>step_size_);</div>
|
|
<div class="line"><a id="l01439" name="l01439"></a><span class="lineno"> 1439</span> accepted_step = <span class="keyword">true</span>;</div>
|
|
<div class="line"><a id="l01440" name="l01440"></a><span class="lineno"> 1440</span> }</div>
|
|
<div class="line"><a id="l01441" name="l01441"></a><span class="lineno"> 1441</span> <span class="keyword">const</span> <span class="keywordtype">double</span> total_steps_attempted =</div>
|
|
<div class="line"><a id="l01442" name="l01442"></a><span class="lineno"> 1442</span> num_rejected_steps_ + iterations_completed_ + 1;</div>
|
|
<div class="line"><a id="l01443" name="l01443"></a><span class="lineno"> 1443</span> <span class="comment">// Our step sizes are a factor 1 - (total_steps_attempted + 1)^(-</span></div>
|
|
<div class="line"><a id="l01444" name="l01444"></a><span class="lineno"> 1444</span> <span class="comment">// step_size_reduction_exponent) smaller than they could be as a margin to</span></div>
|
|
<div class="line"><a id="l01445" name="l01445"></a><span class="lineno"> 1445</span> <span class="comment">// reduce rejected steps.</span></div>
|
|
<div class="line"><a id="l01446" name="l01446"></a><span class="lineno"> 1446</span> <span class="keyword">const</span> <span class="keywordtype">double</span> first_term =</div>
|
|
<div class="line"><a id="l01447" name="l01447"></a><span class="lineno"> 1447</span> (1 - std::pow(total_steps_attempted + 1.0,</div>
|
|
<div class="line"><a id="l01448" name="l01448"></a><span class="lineno"> 1448</span> -params_.adaptive_linesearch_parameters()</div>
|
|
<div class="line"><a id="l01449" name="l01449"></a><span class="lineno"> 1449</span> .step_size_reduction_exponent())) *</div>
|
|
<div class="line"><a id="l01450" name="l01450"></a><span class="lineno"> 1450</span> step_size_limit;</div>
|
|
<div class="line"><a id="l01451" name="l01451"></a><span class="lineno"> 1451</span> <span class="keyword">const</span> <span class="keywordtype">double</span> second_term =</div>
|
|
<div class="line"><a id="l01452" name="l01452"></a><span class="lineno"> 1452</span> (1 + std::pow(total_steps_attempted + 1.0,</div>
|
|
<div class="line"><a id="l01453" name="l01453"></a><span class="lineno"> 1453</span> -params_.adaptive_linesearch_parameters()</div>
|
|
<div class="line"><a id="l01454" name="l01454"></a><span class="lineno"> 1454</span> .step_size_growth_exponent())) *</div>
|
|
<div class="line"><a id="l01455" name="l01455"></a><span class="lineno"> 1455</span> step_size_;</div>
|
|
<div class="line"><a id="l01456" name="l01456"></a><span class="lineno"> 1456</span> <span class="comment">// From the first term when we have to reject a step, the step_size</span></div>
|
|
<div class="line"><a id="l01457" name="l01457"></a><span class="lineno"> 1457</span> <span class="comment">// decreases by a factor of at least 1 - (total_steps_attempted + 1)^(-</span></div>
|
|
<div class="line"><a id="l01458" name="l01458"></a><span class="lineno"> 1458</span> <span class="comment">// step_size_reduction_exponent). From the second term we increase the</span></div>
|
|
<div class="line"><a id="l01459" name="l01459"></a><span class="lineno"> 1459</span> <span class="comment">// step_size by a factor of at most 1 + (total_steps_attempted +</span></div>
|
|
<div class="line"><a id="l01460" name="l01460"></a><span class="lineno"> 1460</span> <span class="comment">// 1)^(-step_size_growth_exponent) Therefore if more than order</span></div>
|
|
<div class="line"><a id="l01461" name="l01461"></a><span class="lineno"> 1461</span> <span class="comment">// (total_steps_attempted + 1)^(step_size_reduction_exponent</span></div>
|
|
<div class="line"><a id="l01462" name="l01462"></a><span class="lineno"> 1462</span> <span class="comment">// - step_size_growth_exponent) fraction of the time we have a rejected</span></div>
|
|
<div class="line"><a id="l01463" name="l01463"></a><span class="lineno"> 1463</span> <span class="comment">// step, we overall decrease the step_size. When the step_size is</span></div>
|
|
<div class="line"><a id="l01464" name="l01464"></a><span class="lineno"> 1464</span> <span class="comment">// sufficiently small we stop having rejected steps.</span></div>
|
|
<div class="line"><a id="l01465" name="l01465"></a><span class="lineno"> 1465</span> step_size_ = <a class="code hl_variable" href="alldiff__cst_8cc.html#ad10edae0a852d72fb76afb1c77735045">std::min</a>(first_term, second_term);</div>
|
|
<div class="line"><a id="l01466" name="l01466"></a><span class="lineno"> 1466</span> <span class="keywordflow">if</span> (!accepted_step) {</div>
|
|
<div class="line"><a id="l01467" name="l01467"></a><span class="lineno"> 1467</span> ++num_rejected_steps_;</div>
|
|
<div class="line"><a id="l01468" name="l01468"></a><span class="lineno"> 1468</span> }</div>
|
|
<div class="line"><a id="l01469" name="l01469"></a><span class="lineno"> 1469</span> }</div>
|
|
<div class="line"><a id="l01470" name="l01470"></a><span class="lineno"> 1470</span> <span class="keywordflow">if</span> (force_numerical_termination) {</div>
|
|
<div class="line"><a id="l01471" name="l01471"></a><span class="lineno"> 1471</span> <span class="keywordflow">return</span> InnerStepOutcome::kForceNumericalTermination;</div>
|
|
<div class="line"><a id="l01472" name="l01472"></a><span class="lineno"> 1472</span> }</div>
|
|
<div class="line"><a id="l01473" name="l01473"></a><span class="lineno"> 1473</span> <span class="keywordflow">return</span> InnerStepOutcome::kSuccessful;</div>
|
|
<div class="line"><a id="l01474" name="l01474"></a><span class="lineno"> 1474</span>}</div>
|
|
<div class="line"><a id="l01475" name="l01475"></a><span class="lineno"> 1475</span> </div>
|
|
<div class="line"><a id="l01476" name="l01476"></a><span class="lineno"> 1476</span>InnerStepOutcome Solver::TakeConstantSizeStep() {</div>
|
|
<div class="line"><a id="l01477" name="l01477"></a><span class="lineno"> 1477</span> <span class="keyword">const</span> <span class="keywordtype">double</span> primal_step_size = step_size_ / primal_weight_;</div>
|
|
<div class="line"><a id="l01478" name="l01478"></a><span class="lineno"> 1478</span> <span class="keyword">const</span> <span class="keywordtype">double</span> dual_step_size = step_size_ * primal_weight_;</div>
|
|
<div class="line"><a id="l01479" name="l01479"></a><span class="lineno"> 1479</span> NextSolutionAndDelta next_primal_solution =</div>
|
|
<div class="line"><a id="l01480" name="l01480"></a><span class="lineno"> 1480</span> ComputeNextPrimalSolution(primal_step_size);</div>
|
|
<div class="line"><a id="l01481" name="l01481"></a><span class="lineno"> 1481</span> NextSolutionAndDelta next_dual_solution = ComputeNextDualSolution(</div>
|
|
<div class="line"><a id="l01482" name="l01482"></a><span class="lineno"> 1482</span> dual_step_size, <span class="comment">/*extrapolation_factor=*/</span>1.0, next_primal_solution);</div>
|
|
<div class="line"><a id="l01483" name="l01483"></a><span class="lineno"> 1483</span> <span class="keyword">const</span> <span class="keywordtype">double</span> movement =</div>
|
|
<div class="line"><a id="l01484" name="l01484"></a><span class="lineno"> 1484</span> ComputeMovement(next_primal_solution.delta, next_dual_solution.delta);</div>
|
|
<div class="line"><a id="l01485" name="l01485"></a><span class="lineno"> 1485</span> <span class="keywordflow">if</span> (movement == 0.0) {</div>
|
|
<div class="line"><a id="l01486" name="l01486"></a><span class="lineno"> 1486</span> LogNumericalTermination();</div>
|
|
<div class="line"><a id="l01487" name="l01487"></a><span class="lineno"> 1487</span> ResetAverageToCurrent();</div>
|
|
<div class="line"><a id="l01488" name="l01488"></a><span class="lineno"> 1488</span> <span class="keywordflow">return</span> InnerStepOutcome::kForceNumericalTermination;</div>
|
|
<div class="line"><a id="l01489" name="l01489"></a><span class="lineno"> 1489</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (movement > kDivergentMovement) {</div>
|
|
<div class="line"><a id="l01490" name="l01490"></a><span class="lineno"> 1490</span> LogNumericalTermination();</div>
|
|
<div class="line"><a id="l01491" name="l01491"></a><span class="lineno"> 1491</span> <span class="keywordflow">return</span> InnerStepOutcome::kForceNumericalTermination;</div>
|
|
<div class="line"><a id="l01492" name="l01492"></a><span class="lineno"> 1492</span> }</div>
|
|
<div class="line"><a id="l01493" name="l01493"></a><span class="lineno"> 1493</span> VectorXd next_dual_product = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a463586ded0a114d3ca4b97a048d37d8a">TransposedMatrixVectorProduct</a>(</div>
|
|
<div class="line"><a id="l01494" name="l01494"></a><span class="lineno"> 1494</span> WorkingQp().constraint_matrix, next_dual_solution.value,</div>
|
|
<div class="line"><a id="l01495" name="l01495"></a><span class="lineno"> 1495</span> sharded_working_qp_.ConstraintMatrixSharder());</div>
|
|
<div class="line"><a id="l01496" name="l01496"></a><span class="lineno"> 1496</span> current_primal_solution_ = std::move(next_primal_solution.value);</div>
|
|
<div class="line"><a id="l01497" name="l01497"></a><span class="lineno"> 1497</span> current_dual_solution_ = std::move(next_dual_solution.value);</div>
|
|
<div class="line"><a id="l01498" name="l01498"></a><span class="lineno"> 1498</span> current_dual_product_ = std::move(next_dual_product);</div>
|
|
<div class="line"><a id="l01499" name="l01499"></a><span class="lineno"> 1499</span> current_primal_delta_ = std::move(next_primal_solution.delta);</div>
|
|
<div class="line"><a id="l01500" name="l01500"></a><span class="lineno"> 1500</span> current_dual_delta_ = std::move(next_dual_solution.delta);</div>
|
|
<div class="line"><a id="l01501" name="l01501"></a><span class="lineno"> 1501</span> primal_average_.Add(current_primal_solution_, <span class="comment">/*weight=*/</span>step_size_);</div>
|
|
<div class="line"><a id="l01502" name="l01502"></a><span class="lineno"> 1502</span> dual_average_.Add(current_dual_solution_, <span class="comment">/*weight=*/</span>step_size_);</div>
|
|
<div class="line"><a id="l01503" name="l01503"></a><span class="lineno"> 1503</span> <span class="keywordflow">return</span> InnerStepOutcome::kSuccessful;</div>
|
|
<div class="line"><a id="l01504" name="l01504"></a><span class="lineno"> 1504</span>}</div>
|
|
<div class="line"><a id="l01505" name="l01505"></a><span class="lineno"> 1505</span> </div>
|
|
<div class="line"><a id="l01506" name="l01506"></a><span class="lineno"> 1506</span>glop::GlopParameters Solver::PreprocessorParameters(</div>
|
|
<div class="line"><a id="l01507" name="l01507"></a><span class="lineno"> 1507</span> <span class="keyword">const</span> PrimalDualHybridGradientParams& params) {</div>
|
|
<div class="line"><a id="l01508" name="l01508"></a><span class="lineno"> 1508</span> glop::GlopParameters glop_params;</div>
|
|
<div class="line"><a id="l01509" name="l01509"></a><span class="lineno"> 1509</span> <span class="comment">// TODO(user): Test if dualization helps or hurts performance.</span></div>
|
|
<div class="line"><a id="l01510" name="l01510"></a><span class="lineno"> 1510</span> glop_params.set_solve_dual_problem(glop::GlopParameters::NEVER_DO);</div>
|
|
<div class="line"><a id="l01511" name="l01511"></a><span class="lineno"> 1511</span> <span class="comment">// Experiments show that this preprocessing step can hurt because it relaxes</span></div>
|
|
<div class="line"><a id="l01512" name="l01512"></a><span class="lineno"> 1512</span> <span class="comment">// variable bounds.</span></div>
|
|
<div class="line"><a id="l01513" name="l01513"></a><span class="lineno"> 1513</span> glop_params.set_use_implied_free_preprocessor(<span class="keyword">false</span>);</div>
|
|
<div class="line"><a id="l01514" name="l01514"></a><span class="lineno"> 1514</span> <span class="comment">// We do our own scaling.</span></div>
|
|
<div class="line"><a id="l01515" name="l01515"></a><span class="lineno"> 1515</span> glop_params.set_use_scaling(<span class="keyword">false</span>);</div>
|
|
<div class="line"><a id="l01516" name="l01516"></a><span class="lineno"> 1516</span> <span class="keywordflow">if</span> (params.presolve_options().has_glop_parameters()) {</div>
|
|
<div class="line"><a id="l01517" name="l01517"></a><span class="lineno"> 1517</span> glop_params.MergeFrom(params.presolve_options().glop_parameters());</div>
|
|
<div class="line"><a id="l01518" name="l01518"></a><span class="lineno"> 1518</span> }</div>
|
|
<div class="line"><a id="l01519" name="l01519"></a><span class="lineno"> 1519</span> <span class="keywordflow">return</span> glop_params;</div>
|
|
<div class="line"><a id="l01520" name="l01520"></a><span class="lineno"> 1520</span>}</div>
|
|
<div class="line"><a id="l01521" name="l01521"></a><span class="lineno"> 1521</span> </div>
|
|
<div class="line"><a id="l01522" name="l01522"></a><span class="lineno"> 1522</span><span class="keyword">namespace </span>{</div>
|
|
<div class="line"><a id="l01523" name="l01523"></a><span class="lineno"> 1523</span> </div>
|
|
<div class="line"><a id="l01524" name="l01524"></a><span class="lineno"> 1524</span>SolverResult ErrorSolverResult(<span class="keyword">const</span> <a class="code hl_enumeration" href="namespaceoperations__research_1_1math__opt.html#ad02e69a0531469b463df907c7b2ad194">TerminationReason</a> reason,</div>
|
|
<div class="line"><a id="l01525" name="l01525"></a><span class="lineno"> 1525</span> <span class="keyword">const</span> std::string& <a class="code hl_variable" href="trace_8cc.html#a36bd74109f547f7f8198faf5a12d2879">message</a>) {</div>
|
|
<div class="line"><a id="l01526" name="l01526"></a><span class="lineno"> 1526</span> SolveLog error_log;</div>
|
|
<div class="line"><a id="l01527" name="l01527"></a><span class="lineno"> 1527</span> error_log.set_termination_reason(reason);</div>
|
|
<div class="line"><a id="l01528" name="l01528"></a><span class="lineno"> 1528</span> error_log.set_termination_string(<a class="code hl_variable" href="trace_8cc.html#a36bd74109f547f7f8198faf5a12d2879">message</a>);</div>
|
|
<div class="line"><a id="l01529" name="l01529"></a><span class="lineno"> 1529</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>) << <span class="stringliteral">"The solver did not run because of invalid input: "</span></div>
|
|
<div class="line"><a id="l01530" name="l01530"></a><span class="lineno"> 1530</span> << <a class="code hl_variable" href="trace_8cc.html#a36bd74109f547f7f8198faf5a12d2879">message</a>;</div>
|
|
<div class="line"><a id="l01531" name="l01531"></a><span class="lineno"> 1531</span> <span class="keywordflow">return</span> SolverResult{.solve_log = error_log};</div>
|
|
<div class="line"><a id="l01532" name="l01532"></a><span class="lineno"> 1532</span>}</div>
|
|
<div class="line"><a id="l01533" name="l01533"></a><span class="lineno"> 1533</span> </div>
|
|
<div class="line"><a id="l01534" name="l01534"></a><span class="lineno"> 1534</span><a class="code hl_enumeration" href="namespaceoperations__research_1_1math__opt.html#ad02e69a0531469b463df907c7b2ad194">TerminationReason</a> GlopStatusToTerminationReason(</div>
|
|
<div class="line"><a id="l01535" name="l01535"></a><span class="lineno"> 1535</span> <span class="keyword">const</span> <a class="code hl_enumeration" href="namespaceoperations__research_1_1glop.html#a884f3b645d22471e5ed3320e182cd493">glop::ProblemStatus</a> glop_status) {</div>
|
|
<div class="line"><a id="l01536" name="l01536"></a><span class="lineno"> 1536</span> <span class="keywordflow">switch</span> (glop_status) {</div>
|
|
<div class="line"><a id="l01537" name="l01537"></a><span class="lineno"> 1537</span> <span class="keywordflow">case</span> glop::ProblemStatus::OPTIMAL:</div>
|
|
<div class="line"><a id="l01538" name="l01538"></a><span class="lineno"> 1538</span> <span class="keywordflow">return</span> TERMINATION_REASON_OPTIMAL;</div>
|
|
<div class="line"><a id="l01539" name="l01539"></a><span class="lineno"> 1539</span> <span class="keywordflow">case</span> glop::ProblemStatus::INVALID_PROBLEM:</div>
|
|
<div class="line"><a id="l01540" name="l01540"></a><span class="lineno"> 1540</span> <span class="keywordflow">return</span> TERMINATION_REASON_INVALID_PROBLEM;</div>
|
|
<div class="line"><a id="l01541" name="l01541"></a><span class="lineno"> 1541</span> <span class="keywordflow">case</span> glop::ProblemStatus::ABNORMAL:</div>
|
|
<div class="line"><a id="l01542" name="l01542"></a><span class="lineno"> 1542</span> <span class="keywordflow">case</span> glop::ProblemStatus::IMPRECISE:</div>
|
|
<div class="line"><a id="l01543" name="l01543"></a><span class="lineno"> 1543</span> <span class="keywordflow">return</span> TERMINATION_REASON_NUMERICAL_ERROR;</div>
|
|
<div class="line"><a id="l01544" name="l01544"></a><span class="lineno"> 1544</span> <span class="keywordflow">case</span> glop::ProblemStatus::PRIMAL_INFEASIBLE:</div>
|
|
<div class="line"><a id="l01545" name="l01545"></a><span class="lineno"> 1545</span> <span class="keywordflow">case</span> glop::ProblemStatus::DUAL_INFEASIBLE:</div>
|
|
<div class="line"><a id="l01546" name="l01546"></a><span class="lineno"> 1546</span> <span class="keywordflow">case</span> glop::ProblemStatus::INFEASIBLE_OR_UNBOUNDED:</div>
|
|
<div class="line"><a id="l01547" name="l01547"></a><span class="lineno"> 1547</span> <span class="keywordflow">case</span> glop::ProblemStatus::DUAL_UNBOUNDED:</div>
|
|
<div class="line"><a id="l01548" name="l01548"></a><span class="lineno"> 1548</span> <span class="keywordflow">case</span> glop::ProblemStatus::PRIMAL_UNBOUNDED:</div>
|
|
<div class="line"><a id="l01549" name="l01549"></a><span class="lineno"> 1549</span> <span class="keywordflow">return</span> TERMINATION_REASON_PRIMAL_OR_DUAL_INFEASIBLE;</div>
|
|
<div class="line"><a id="l01550" name="l01550"></a><span class="lineno"> 1550</span> <span class="keywordflow">default</span>:</div>
|
|
<div class="line"><a id="l01551" name="l01551"></a><span class="lineno"> 1551</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>) << <span class="stringliteral">"Unexpected preprocessor status "</span> << glop_status;</div>
|
|
<div class="line"><a id="l01552" name="l01552"></a><span class="lineno"> 1552</span> <span class="keywordflow">return</span> TERMINATION_REASON_OTHER;</div>
|
|
<div class="line"><a id="l01553" name="l01553"></a><span class="lineno"> 1553</span> }</div>
|
|
<div class="line"><a id="l01554" name="l01554"></a><span class="lineno"> 1554</span>}</div>
|
|
<div class="line"><a id="l01555" name="l01555"></a><span class="lineno"> 1555</span> </div>
|
|
<div class="line"><a id="l01556" name="l01556"></a><span class="lineno"> 1556</span>} <span class="comment">// namespace</span></div>
|
|
<div class="line"><a id="l01557" name="l01557"></a><span class="lineno"> 1557</span> </div>
|
|
<div class="line"><a id="l01558" name="l01558"></a><span class="lineno"> 1558</span>absl::optional<TerminationReason> Solver::ApplyPresolveIfEnabled(</div>
|
|
<div class="line"><a id="l01559" name="l01559"></a><span class="lineno"> 1559</span> absl::optional<PrimalAndDualSolution>* <span class="keyword">const</span> initial_solution) {</div>
|
|
<div class="line"><a id="l01560" name="l01560"></a><span class="lineno"> 1560</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> presolve_enabled = params_.presolve_options().use_glop();</div>
|
|
<div class="line"><a id="l01561" name="l01561"></a><span class="lineno"> 1561</span> <span class="keywordflow">if</span> (!presolve_enabled) {</div>
|
|
<div class="line"><a id="l01562" name="l01562"></a><span class="lineno"> 1562</span> <span class="keywordflow">return</span> absl::nullopt;</div>
|
|
<div class="line"><a id="l01563" name="l01563"></a><span class="lineno"> 1563</span> }</div>
|
|
<div class="line"><a id="l01564" name="l01564"></a><span class="lineno"> 1564</span> <span class="keywordflow">if</span> (!<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a850865b3deabb2a623e130691df99f15">IsLinearProgram</a>(WorkingQp())) {</div>
|
|
<div class="line"><a id="l01565" name="l01565"></a><span class="lineno"> 1565</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>)</div>
|
|
<div class="line"><a id="l01566" name="l01566"></a><span class="lineno"> 1566</span> << <span class="stringliteral">"Skipping presolve, which is only supported for linear programs"</span>;</div>
|
|
<div class="line"><a id="l01567" name="l01567"></a><span class="lineno"> 1567</span> <span class="keywordflow">return</span> absl::nullopt;</div>
|
|
<div class="line"><a id="l01568" name="l01568"></a><span class="lineno"> 1568</span> }</div>
|
|
<div class="line"><a id="l01569" name="l01569"></a><span class="lineno"> 1569</span> absl::StatusOr<MPModelProto> <a class="code hl_variable" href="gurobi__interface_8cc.html#a0728f23c9a47655d38e0bf1a2f200bcf">model</a> = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a7f791925d78c8eb11002320336d0410d">QpToMpModelProto</a>(WorkingQp());</div>
|
|
<div class="line"><a id="l01570" name="l01570"></a><span class="lineno"> 1570</span> <span class="keywordflow">if</span> (!<a class="code hl_variable" href="gurobi__interface_8cc.html#a0728f23c9a47655d38e0bf1a2f200bcf">model</a>.ok()) {</div>
|
|
<div class="line"><a id="l01571" name="l01571"></a><span class="lineno"> 1571</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>)</div>
|
|
<div class="line"><a id="l01572" name="l01572"></a><span class="lineno"> 1572</span> << <span class="stringliteral">"Skipping presolve because of error converting to MPModelProto: "</span></div>
|
|
<div class="line"><a id="l01573" name="l01573"></a><span class="lineno"> 1573</span> << <a class="code hl_variable" href="gurobi__interface_8cc.html#a0728f23c9a47655d38e0bf1a2f200bcf">model</a>.status();</div>
|
|
<div class="line"><a id="l01574" name="l01574"></a><span class="lineno"> 1574</span> <span class="keywordflow">return</span> absl::nullopt;</div>
|
|
<div class="line"><a id="l01575" name="l01575"></a><span class="lineno"> 1575</span> }</div>
|
|
<div class="line"><a id="l01576" name="l01576"></a><span class="lineno"> 1576</span> <span class="keywordflow">if</span> (initial_solution->has_value()) {</div>
|
|
<div class="line"><a id="l01577" name="l01577"></a><span class="lineno"> 1577</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>) << <span class="stringliteral">"Ignoring initial solution. Initial solutions "</span></div>
|
|
<div class="line"><a id="l01578" name="l01578"></a><span class="lineno"> 1578</span> <span class="stringliteral">"are ignored when presolve is on."</span>;</div>
|
|
<div class="line"><a id="l01579" name="l01579"></a><span class="lineno"> 1579</span> initial_solution->reset();</div>
|
|
<div class="line"><a id="l01580" name="l01580"></a><span class="lineno"> 1580</span> }</div>
|
|
<div class="line"><a id="l01581" name="l01581"></a><span class="lineno"> 1581</span> glop::LinearProgram glop_lp;</div>
|
|
<div class="line"><a id="l01582" name="l01582"></a><span class="lineno"> 1582</span> <a class="code hl_function" href="namespaceoperations__research_1_1glop.html#a4066bdd6e74f798c189fa8e830fcd37b">glop::MPModelProtoToLinearProgram</a>(*<a class="code hl_variable" href="gurobi__interface_8cc.html#a0728f23c9a47655d38e0bf1a2f200bcf">model</a>, &glop_lp);</div>
|
|
<div class="line"><a id="l01583" name="l01583"></a><span class="lineno"> 1583</span> <span class="comment">// Save RAM</span></div>
|
|
<div class="line"><a id="l01584" name="l01584"></a><span class="lineno"> 1584</span> <a class="code hl_variable" href="gurobi__interface_8cc.html#a0728f23c9a47655d38e0bf1a2f200bcf">model</a>->Clear();</div>
|
|
<div class="line"><a id="l01585" name="l01585"></a><span class="lineno"> 1585</span> presolve_info_.emplace(std::move(sharded_working_qp_), params_);</div>
|
|
<div class="line"><a id="l01586" name="l01586"></a><span class="lineno"> 1586</span> <span class="comment">// To simplify our code we ignore the return value indicating whether</span></div>
|
|
<div class="line"><a id="l01587" name="l01587"></a><span class="lineno"> 1587</span> <span class="comment">// postprocessing is required. We simply call RecoverSolution()</span></div>
|
|
<div class="line"><a id="l01588" name="l01588"></a><span class="lineno"> 1588</span> <span class="comment">// unconditionally, which may do nothing.</span></div>
|
|
<div class="line"><a id="l01589" name="l01589"></a><span class="lineno"> 1589</span> presolve_info_->preprocessor.Run(&glop_lp);</div>
|
|
<div class="line"><a id="l01590" name="l01590"></a><span class="lineno"> 1590</span> presolve_info_->presolved_problem_was_maximization =</div>
|
|
<div class="line"><a id="l01591" name="l01591"></a><span class="lineno"> 1591</span> glop_lp.IsMaximizationProblem();</div>
|
|
<div class="line"><a id="l01592" name="l01592"></a><span class="lineno"> 1592</span> <span class="comment">// MpModelProto doesn't support scaling factors so any scaling factor was</span></div>
|
|
<div class="line"><a id="l01593" name="l01593"></a><span class="lineno"> 1593</span> <span class="comment">// eliminated when we converted to MpModelProto. Nothing afterwards should set</span></div>
|
|
<div class="line"><a id="l01594" name="l01594"></a><span class="lineno"> 1594</span> <span class="comment">// scaling factor.</span></div>
|
|
<div class="line"><a id="l01595" name="l01595"></a><span class="lineno"> 1595</span> <a class="code hl_define" href="base_2logging_8h.html#a7c0ce053b28d53aa4eaf3eb7fb71663b">CHECK_EQ</a>(glop_lp.objective_scaling_factor(), 1.0);</div>
|
|
<div class="line"><a id="l01596" name="l01596"></a><span class="lineno"> 1596</span> MPModelProto output;</div>
|
|
<div class="line"><a id="l01597" name="l01597"></a><span class="lineno"> 1597</span> <a class="code hl_function" href="namespaceoperations__research_1_1glop.html#a8750840afdd5774223821fd504df04c5">glop::LinearProgramToMPModelProto</a>(glop_lp, &output);</div>
|
|
<div class="line"><a id="l01598" name="l01598"></a><span class="lineno"> 1598</span> <span class="comment">// This will only fail if given an invalid LP, which shouldn't happen.</span></div>
|
|
<div class="line"><a id="l01599" name="l01599"></a><span class="lineno"> 1599</span> absl::StatusOr<QuadraticProgram> presolved_qp =</div>
|
|
<div class="line"><a id="l01600" name="l01600"></a><span class="lineno"> 1600</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a007a657dbfa4e0e820e0d9af4d8d27a2">QpFromMpModelProto</a>(output, <span class="comment">/*relax_integer_variables=*/</span><span class="keyword">false</span>);</div>
|
|
<div class="line"><a id="l01601" name="l01601"></a><span class="lineno"> 1601</span> <a class="code hl_define" href="base_2logging_8h.html#a9f96ed9f06763f0821fdbb4d29031d8d">CHECK_OK</a>(presolved_qp.status());</div>
|
|
<div class="line"><a id="l01602" name="l01602"></a><span class="lineno"> 1602</span> sharded_working_qp_ = ShardedQuadraticProgram(</div>
|
|
<div class="line"><a id="l01603" name="l01603"></a><span class="lineno"> 1603</span> std::move(*presolved_qp), params_.num_threads(), num_shards_);</div>
|
|
<div class="line"><a id="l01604" name="l01604"></a><span class="lineno"> 1604</span> primal_average_ =</div>
|
|
<div class="line"><a id="l01605" name="l01605"></a><span class="lineno"> 1605</span> ShardedWeightedAverage(&sharded_working_qp_.PrimalSharder());</div>
|
|
<div class="line"><a id="l01606" name="l01606"></a><span class="lineno"> 1606</span> dual_average_ = ShardedWeightedAverage(&sharded_working_qp_.DualSharder());</div>
|
|
<div class="line"><a id="l01607" name="l01607"></a><span class="lineno"> 1607</span> <span class="comment">// A status of INIT means the preprocessor created a (usually) smaller</span></div>
|
|
<div class="line"><a id="l01608" name="l01608"></a><span class="lineno"> 1608</span> <span class="comment">// problem that needs solving. Other statuses mean the preprocessor solved</span></div>
|
|
<div class="line"><a id="l01609" name="l01609"></a><span class="lineno"> 1609</span> <span class="comment">// the problem completely.</span></div>
|
|
<div class="line"><a id="l01610" name="l01610"></a><span class="lineno"> 1610</span> <span class="keywordflow">if</span> (presolve_info_->preprocessor.status() != glop::ProblemStatus::INIT) {</div>
|
|
<div class="line"><a id="l01611" name="l01611"></a><span class="lineno"> 1611</span> col_scaling_vec_.setOnes(sharded_working_qp_.PrimalSize());</div>
|
|
<div class="line"><a id="l01612" name="l01612"></a><span class="lineno"> 1612</span> row_scaling_vec_.setOnes(sharded_working_qp_.DualSize());</div>
|
|
<div class="line"><a id="l01613" name="l01613"></a><span class="lineno"> 1613</span> <span class="keywordflow">return</span> GlopStatusToTerminationReason(presolve_info_->preprocessor.status());</div>
|
|
<div class="line"><a id="l01614" name="l01614"></a><span class="lineno"> 1614</span> }</div>
|
|
<div class="line"><a id="l01615" name="l01615"></a><span class="lineno"> 1615</span> <span class="keywordflow">return</span> absl::nullopt;</div>
|
|
<div class="line"><a id="l01616" name="l01616"></a><span class="lineno"> 1616</span>}</div>
|
|
<div class="line"><a id="l01617" name="l01617"></a><span class="lineno"> 1617</span> </div>
|
|
<div class="line"><a id="l01618" name="l01618"></a><span class="lineno"> 1618</span>PrimalAndDualSolution Solver::RecoverOriginalSolution(</div>
|
|
<div class="line"><a id="l01619" name="l01619"></a><span class="lineno"> 1619</span> PrimalAndDualSolution working_solution)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l01620" name="l01620"></a><span class="lineno"> 1620</span> glop::ProblemSolution glop_solution(glop::RowIndex{0}, glop::ColIndex{0});</div>
|
|
<div class="line"><a id="l01621" name="l01621"></a><span class="lineno"> 1621</span> <span class="keywordflow">if</span> (presolve_info_.has_value()) {</div>
|
|
<div class="line"><a id="l01622" name="l01622"></a><span class="lineno"> 1622</span> <span class="comment">// We compute statuses relative to the working problem so we can detect when</span></div>
|
|
<div class="line"><a id="l01623" name="l01623"></a><span class="lineno"> 1623</span> <span class="comment">// variables are at their bounds without floating-point roundoff induced by</span></div>
|
|
<div class="line"><a id="l01624" name="l01624"></a><span class="lineno"> 1624</span> <span class="comment">// scaling.</span></div>
|
|
<div class="line"><a id="l01625" name="l01625"></a><span class="lineno"> 1625</span> glop_solution = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp_1_1internal.html#a3fe24e4dddd7460838c010f64fd3569a">internal::ComputeStatuses</a>(WorkingQp(), working_solution);</div>
|
|
<div class="line"><a id="l01626" name="l01626"></a><span class="lineno"> 1626</span> }</div>
|
|
<div class="line"><a id="l01627" name="l01627"></a><span class="lineno"> 1627</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a920005e41b36a7a0c7f4ad148ad7069d">CoefficientWiseProductInPlace</a>(col_scaling_vec_,</div>
|
|
<div class="line"><a id="l01628" name="l01628"></a><span class="lineno"> 1628</span> sharded_working_qp_.PrimalSharder(),</div>
|
|
<div class="line"><a id="l01629" name="l01629"></a><span class="lineno"> 1629</span> working_solution.primal_solution);</div>
|
|
<div class="line"><a id="l01630" name="l01630"></a><span class="lineno"> 1630</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a920005e41b36a7a0c7f4ad148ad7069d">CoefficientWiseProductInPlace</a>(row_scaling_vec_,</div>
|
|
<div class="line"><a id="l01631" name="l01631"></a><span class="lineno"> 1631</span> sharded_working_qp_.DualSharder(),</div>
|
|
<div class="line"><a id="l01632" name="l01632"></a><span class="lineno"> 1632</span> working_solution.dual_solution);</div>
|
|
<div class="line"><a id="l01633" name="l01633"></a><span class="lineno"> 1633</span> <span class="keywordflow">if</span> (presolve_info_.has_value()) {</div>
|
|
<div class="line"><a id="l01634" name="l01634"></a><span class="lineno"> 1634</span> glop_solution.primal_values =</div>
|
|
<div class="line"><a id="l01635" name="l01635"></a><span class="lineno"> 1635</span> <a class="code hl_typedef" href="namespaceoperations__research_1_1glop.html#a4dc8ae0f97c4110f3cc770715b2bbd12">glop::DenseRow</a>(working_solution.primal_solution.begin(),</div>
|
|
<div class="line"><a id="l01636" name="l01636"></a><span class="lineno"> 1636</span> working_solution.primal_solution.end());</div>
|
|
<div class="line"><a id="l01637" name="l01637"></a><span class="lineno"> 1637</span> glop_solution.dual_values =</div>
|
|
<div class="line"><a id="l01638" name="l01638"></a><span class="lineno"> 1638</span> <a class="code hl_typedef" href="namespaceoperations__research_1_1glop.html#ae5fa9e57c2d31b294195ed8a9e17bfac">glop::DenseColumn</a>(working_solution.dual_solution.begin(),</div>
|
|
<div class="line"><a id="l01639" name="l01639"></a><span class="lineno"> 1639</span> working_solution.dual_solution.end());</div>
|
|
<div class="line"><a id="l01640" name="l01640"></a><span class="lineno"> 1640</span> <span class="comment">// We got the working QP by calling LinearProgramToMPModelProto() and</span></div>
|
|
<div class="line"><a id="l01641" name="l01641"></a><span class="lineno"> 1641</span> <span class="comment">// QpFromMpModelProto(). We need to negate the duals if the LP resulting</span></div>
|
|
<div class="line"><a id="l01642" name="l01642"></a><span class="lineno"> 1642</span> <span class="comment">// from presolve was a max problem.</span></div>
|
|
<div class="line"><a id="l01643" name="l01643"></a><span class="lineno"> 1643</span> <span class="keywordflow">if</span> (presolve_info_->presolved_problem_was_maximization) {</div>
|
|
<div class="line"><a id="l01644" name="l01644"></a><span class="lineno"> 1644</span> <span class="keywordflow">for</span> (glop::RowIndex i{0}; i < glop_solution.dual_values.size(); ++i) {</div>
|
|
<div class="line"><a id="l01645" name="l01645"></a><span class="lineno"> 1645</span> glop_solution.dual_values[i] *= -1;</div>
|
|
<div class="line"><a id="l01646" name="l01646"></a><span class="lineno"> 1646</span> }</div>
|
|
<div class="line"><a id="l01647" name="l01647"></a><span class="lineno"> 1647</span> }</div>
|
|
<div class="line"><a id="l01648" name="l01648"></a><span class="lineno"> 1648</span> presolve_info_->preprocessor.RecoverSolution(&glop_solution);</div>
|
|
<div class="line"><a id="l01649" name="l01649"></a><span class="lineno"> 1649</span> PrimalAndDualSolution solution;</div>
|
|
<div class="line"><a id="l01650" name="l01650"></a><span class="lineno"> 1650</span> solution.primal_solution =</div>
|
|
<div class="line"><a id="l01651" name="l01651"></a><span class="lineno"> 1651</span> Eigen::Map<Eigen::VectorXd>(glop_solution.primal_values.data(),</div>
|
|
<div class="line"><a id="l01652" name="l01652"></a><span class="lineno"> 1652</span> glop_solution.primal_values.size().value());</div>
|
|
<div class="line"><a id="l01653" name="l01653"></a><span class="lineno"> 1653</span> solution.dual_solution =</div>
|
|
<div class="line"><a id="l01654" name="l01654"></a><span class="lineno"> 1654</span> Eigen::Map<Eigen::VectorXd>(glop_solution.dual_values.data(),</div>
|
|
<div class="line"><a id="l01655" name="l01655"></a><span class="lineno"> 1655</span> glop_solution.dual_values.size().value());</div>
|
|
<div class="line"><a id="l01656" name="l01656"></a><span class="lineno"> 1656</span> <span class="comment">// We called QpToMpModelProto() and MPModelProtoToLinearProgram() to convert</span></div>
|
|
<div class="line"><a id="l01657" name="l01657"></a><span class="lineno"> 1657</span> <span class="comment">// our original QP into input for glop's preprocessor. The former multiplies</span></div>
|
|
<div class="line"><a id="l01658" name="l01658"></a><span class="lineno"> 1658</span> <span class="comment">// the objective vector by the objective_scaling_factor, which multiplies</span></div>
|
|
<div class="line"><a id="l01659" name="l01659"></a><span class="lineno"> 1659</span> <span class="comment">// the duals by that factor as well. To undo this we divide by the</span></div>
|
|
<div class="line"><a id="l01660" name="l01660"></a><span class="lineno"> 1660</span> <span class="comment">// objective_scaling_factor.</span></div>
|
|
<div class="line"><a id="l01661" name="l01661"></a><span class="lineno"> 1661</span> solution.dual_solution /=</div>
|
|
<div class="line"><a id="l01662" name="l01662"></a><span class="lineno"> 1662</span> presolve_info_->sharded_original_qp.Qp().objective_scaling_factor;</div>
|
|
<div class="line"><a id="l01663" name="l01663"></a><span class="lineno"> 1663</span> <span class="comment">// Glop's preprocessor sometimes violates the primal bounds constraints. To</span></div>
|
|
<div class="line"><a id="l01664" name="l01664"></a><span class="lineno"> 1664</span> <span class="comment">// be safe we project both primal and dual.</span></div>
|
|
<div class="line"><a id="l01665" name="l01665"></a><span class="lineno"> 1665</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#acb7f29f435d6c9fc53148ee403c7049e">ProjectToPrimalVariableBounds</a>(presolve_info_->sharded_original_qp,</div>
|
|
<div class="line"><a id="l01666" name="l01666"></a><span class="lineno"> 1666</span> solution.primal_solution);</div>
|
|
<div class="line"><a id="l01667" name="l01667"></a><span class="lineno"> 1667</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a898c0c776a5736cf1931036d0d370724">ProjectToDualVariableBounds</a>(presolve_info_->sharded_original_qp,</div>
|
|
<div class="line"><a id="l01668" name="l01668"></a><span class="lineno"> 1668</span> solution.dual_solution);</div>
|
|
<div class="line"><a id="l01669" name="l01669"></a><span class="lineno"> 1669</span> <span class="keywordflow">return</span> solution;</div>
|
|
<div class="line"><a id="l01670" name="l01670"></a><span class="lineno"> 1670</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01671" name="l01671"></a><span class="lineno"> 1671</span> <span class="keywordflow">return</span> working_solution;</div>
|
|
<div class="line"><a id="l01672" name="l01672"></a><span class="lineno"> 1672</span> }</div>
|
|
<div class="line"><a id="l01673" name="l01673"></a><span class="lineno"> 1673</span>}</div>
|
|
<div class="line"><a id="l01674" name="l01674"></a><span class="lineno"> 1674</span> </div>
|
|
<div class="line"><a id="l01675" name="l01675"></a><span class="lineno"> 1675</span>SolverResult <a class="code hl_function" href="namespaceoperations__research_1_1math__opt.html#a5767ee23f380e72488d3c7ebf2d742b1">Solver::Solve</a>(</div>
|
|
<div class="line"><a id="l01676" name="l01676"></a><span class="lineno"> 1676</span> absl::optional<PrimalAndDualSolution> initial_solution,</div>
|
|
<div class="line"><a id="l01677" name="l01677"></a><span class="lineno"> 1677</span> <span class="keyword">const</span> std::atomic<bool>* interrupt_solve,</div>
|
|
<div class="line"><a id="l01678" name="l01678"></a><span class="lineno"> 1678</span> IterationStatsCallback iteration_stats_callback) {</div>
|
|
<div class="line"><a id="l01679" name="l01679"></a><span class="lineno"> 1679</span> SolveLog solve_log;</div>
|
|
<div class="line"><a id="l01680" name="l01680"></a><span class="lineno"> 1680</span> <span class="keywordflow">if</span> (sharded_working_qp_.Qp().problem_name.has_value()) {</div>
|
|
<div class="line"><a id="l01681" name="l01681"></a><span class="lineno"> 1681</span> solve_log.set_instance_name(*sharded_working_qp_.Qp().problem_name);</div>
|
|
<div class="line"><a id="l01682" name="l01682"></a><span class="lineno"> 1682</span> }</div>
|
|
<div class="line"><a id="l01683" name="l01683"></a><span class="lineno"> 1683</span> *solve_log.mutable_params() = params_;</div>
|
|
<div class="line"><a id="l01684" name="l01684"></a><span class="lineno"> 1684</span> *solve_log.mutable_original_problem_stats() = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(</div>
|
|
<div class="line"><a id="l01685" name="l01685"></a><span class="lineno"> 1685</span> sharded_working_qp_, params_.infinite_constraint_bound_threshold());</div>
|
|
<div class="line"><a id="l01686" name="l01686"></a><span class="lineno"> 1686</span> original_bound_norms_ =</div>
|
|
<div class="line"><a id="l01687" name="l01687"></a><span class="lineno"> 1687</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ac69c2a44159905c520139def17af7e4f">BoundNormsFromProblemStats</a>(solve_log.original_problem_stats());</div>
|
|
<div class="line"><a id="l01688" name="l01688"></a><span class="lineno"> 1688</span> <span class="keyword">const</span> std::string preprocessing_string = absl::StrCat(</div>
|
|
<div class="line"><a id="l01689" name="l01689"></a><span class="lineno"> 1689</span> params_.presolve_options().use_glop() ? <span class="stringliteral">"presolving and "</span> : <span class="stringliteral">""</span>,</div>
|
|
<div class="line"><a id="l01690" name="l01690"></a><span class="lineno"> 1690</span> <span class="stringliteral">"rescaling:"</span>);</div>
|
|
<div class="line"><a id="l01691" name="l01691"></a><span class="lineno"> 1691</span> <span class="keywordflow">if</span> (params_.verbosity_level() >= 1) {</div>
|
|
<div class="line"><a id="l01692" name="l01692"></a><span class="lineno"> 1692</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Problem stats before "</span> << preprocessing_string;</div>
|
|
<div class="line"><a id="l01693" name="l01693"></a><span class="lineno"> 1693</span> LogQuadraticProgramStats(solve_log.original_problem_stats());</div>
|
|
<div class="line"><a id="l01694" name="l01694"></a><span class="lineno"> 1694</span> }</div>
|
|
<div class="line"><a id="l01695" name="l01695"></a><span class="lineno"> 1695</span> timer_.<a class="code hl_function" href="class_wall_timer.html#a07aaf1227e4d645f15e0a964f54ef291">Start</a>();</div>
|
|
<div class="line"><a id="l01696" name="l01696"></a><span class="lineno"> 1696</span> iteration_stats_callback_ = std::move(iteration_stats_callback);</div>
|
|
<div class="line"><a id="l01697" name="l01697"></a><span class="lineno"> 1697</span> absl::optional<TerminationReason> maybe_terminate =</div>
|
|
<div class="line"><a id="l01698" name="l01698"></a><span class="lineno"> 1698</span> ApplyPresolveIfEnabled(&initial_solution);</div>
|
|
<div class="line"><a id="l01699" name="l01699"></a><span class="lineno"> 1699</span> <span class="keywordflow">if</span> (maybe_terminate.has_value()) {</div>
|
|
<div class="line"><a id="l01700" name="l01700"></a><span class="lineno"> 1700</span> <span class="comment">// Glop also feeds zero primal and dual solutions when the preprocessor</span></div>
|
|
<div class="line"><a id="l01701" name="l01701"></a><span class="lineno"> 1701</span> <span class="comment">// has a non-INIT status. When the preprocessor status is optimal the</span></div>
|
|
<div class="line"><a id="l01702" name="l01702"></a><span class="lineno"> 1702</span> <span class="comment">// vectors have length 0. When the status is something else the lengths</span></div>
|
|
<div class="line"><a id="l01703" name="l01703"></a><span class="lineno"> 1703</span> <span class="comment">// may be non-zero, but that's OK since we don't promise to produce a</span></div>
|
|
<div class="line"><a id="l01704" name="l01704"></a><span class="lineno"> 1704</span> <span class="comment">// meaningful solution in that case.</span></div>
|
|
<div class="line"><a id="l01705" name="l01705"></a><span class="lineno"> 1705</span> <span class="keyword">const</span> <span class="keywordtype">int</span> working_dual_size = sharded_working_qp_.DualSize();</div>
|
|
<div class="line"><a id="l01706" name="l01706"></a><span class="lineno"> 1706</span> <span class="keyword">const</span> <span class="keywordtype">int</span> working_primal_size = sharded_working_qp_.PrimalSize();</div>
|
|
<div class="line"><a id="l01707" name="l01707"></a><span class="lineno"> 1707</span> IterationStats iteration_stats;</div>
|
|
<div class="line"><a id="l01708" name="l01708"></a><span class="lineno"> 1708</span> iteration_stats.set_cumulative_time_sec(timer_.<a class="code hl_function" href="class_wall_timer.html#aec56fe080959ecebec3feaed9dafde84">Get</a>());</div>
|
|
<div class="line"><a id="l01709" name="l01709"></a><span class="lineno"> 1709</span> solve_log.set_preprocessing_time_sec(iteration_stats.cumulative_time_sec());</div>
|
|
<div class="line"><a id="l01710" name="l01710"></a><span class="lineno"> 1710</span> VectorXd working_primal = <a class="code hl_function" href="namespaceoperations__research.html#a5a9881f8a07b166ef2cbde572cea27b6">VectorXd::Zero</a>(working_primal_size);</div>
|
|
<div class="line"><a id="l01711" name="l01711"></a><span class="lineno"> 1711</span> VectorXd working_dual = <a class="code hl_function" href="namespaceoperations__research.html#a5a9881f8a07b166ef2cbde572cea27b6">VectorXd::Zero</a>(working_dual_size);</div>
|
|
<div class="line"><a id="l01712" name="l01712"></a><span class="lineno"> 1712</span> PrimalAndDualSolution original = RecoverOriginalSolution(</div>
|
|
<div class="line"><a id="l01713" name="l01713"></a><span class="lineno"> 1713</span> {.primal_solution = working_primal, .dual_solution = working_dual});</div>
|
|
<div class="line"><a id="l01714" name="l01714"></a><span class="lineno"> 1714</span> AddConvergenceAndInfeasibilityInformation(</div>
|
|
<div class="line"><a id="l01715" name="l01715"></a><span class="lineno"> 1715</span> original.primal_solution, original.dual_solution,</div>
|
|
<div class="line"><a id="l01716" name="l01716"></a><span class="lineno"> 1716</span> presolve_info_->sharded_original_qp,</div>
|
|
<div class="line"><a id="l01717" name="l01717"></a><span class="lineno"> 1717</span> presolve_info_->trivial_col_scaling_vec,</div>
|
|
<div class="line"><a id="l01718" name="l01718"></a><span class="lineno"> 1718</span> presolve_info_->trivial_row_scaling_vec, POINT_TYPE_PRESOLVER_SOLUTION,</div>
|
|
<div class="line"><a id="l01719" name="l01719"></a><span class="lineno"> 1719</span> iteration_stats);</div>
|
|
<div class="line"><a id="l01720" name="l01720"></a><span class="lineno"> 1720</span> absl::optional<TerminationReasonAndPointType> earned_termination =</div>
|
|
<div class="line"><a id="l01721" name="l01721"></a><span class="lineno"> 1721</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a7d2e7889c98661aba130697a142fdf4b">CheckTerminationCriteria</a>(params_.termination_criteria(),</div>
|
|
<div class="line"><a id="l01722" name="l01722"></a><span class="lineno"> 1722</span> iteration_stats, original_bound_norms_,</div>
|
|
<div class="line"><a id="l01723" name="l01723"></a><span class="lineno"> 1723</span> <span class="comment">/*force_numerical_termination=*/</span><span class="keyword">false</span>);</div>
|
|
<div class="line"><a id="l01724" name="l01724"></a><span class="lineno"> 1724</span> <a class="code hl_enumeration" href="namespaceoperations__research_1_1math__opt.html#ad02e69a0531469b463df907c7b2ad194">TerminationReason</a> final_termination_reason;</div>
|
|
<div class="line"><a id="l01725" name="l01725"></a><span class="lineno"> 1725</span> <span class="keywordflow">if</span> (earned_termination.has_value() &&</div>
|
|
<div class="line"><a id="l01726" name="l01726"></a><span class="lineno"> 1726</span> (earned_termination->reason == TERMINATION_REASON_OPTIMAL ||</div>
|
|
<div class="line"><a id="l01727" name="l01727"></a><span class="lineno"> 1727</span> earned_termination->reason == TERMINATION_REASON_PRIMAL_INFEASIBLE ||</div>
|
|
<div class="line"><a id="l01728" name="l01728"></a><span class="lineno"> 1728</span> earned_termination->reason == TERMINATION_REASON_DUAL_INFEASIBLE)) {</div>
|
|
<div class="line"><a id="l01729" name="l01729"></a><span class="lineno"> 1729</span> final_termination_reason = earned_termination->reason;</div>
|
|
<div class="line"><a id="l01730" name="l01730"></a><span class="lineno"> 1730</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01731" name="l01731"></a><span class="lineno"> 1731</span> <span class="keywordflow">if</span> (*maybe_terminate == TERMINATION_REASON_OPTIMAL) {</div>
|
|
<div class="line"><a id="l01732" name="l01732"></a><span class="lineno"> 1732</span> final_termination_reason = TERMINATION_REASON_NUMERICAL_ERROR;</div>
|
|
<div class="line"><a id="l01733" name="l01733"></a><span class="lineno"> 1733</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a>) << <span class="stringliteral">"Presolve claimed to solve the LP optimally but the "</span></div>
|
|
<div class="line"><a id="l01734" name="l01734"></a><span class="lineno"> 1734</span> <span class="stringliteral">"solution doesn't satisfy the optimality criteria."</span>;</div>
|
|
<div class="line"><a id="l01735" name="l01735"></a><span class="lineno"> 1735</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01736" name="l01736"></a><span class="lineno"> 1736</span> final_termination_reason = *maybe_terminate;</div>
|
|
<div class="line"><a id="l01737" name="l01737"></a><span class="lineno"> 1737</span> }</div>
|
|
<div class="line"><a id="l01738" name="l01738"></a><span class="lineno"> 1738</span> }</div>
|
|
<div class="line"><a id="l01739" name="l01739"></a><span class="lineno"> 1739</span> <span class="keywordflow">return</span> ConstructSolverResult(</div>
|
|
<div class="line"><a id="l01740" name="l01740"></a><span class="lineno"> 1740</span> std::move(working_primal), std::move(working_dual),</div>
|
|
<div class="line"><a id="l01741" name="l01741"></a><span class="lineno"> 1741</span> std::move(iteration_stats), final_termination_reason,</div>
|
|
<div class="line"><a id="l01742" name="l01742"></a><span class="lineno"> 1742</span> POINT_TYPE_PRESOLVER_SOLUTION, std::move(solve_log));</div>
|
|
<div class="line"><a id="l01743" name="l01743"></a><span class="lineno"> 1743</span> }</div>
|
|
<div class="line"><a id="l01744" name="l01744"></a><span class="lineno"> 1744</span> </div>
|
|
<div class="line"><a id="l01745" name="l01745"></a><span class="lineno"> 1745</span> <span class="comment">// The current solution is updated by ComputeAndApplyRescaling.</span></div>
|
|
<div class="line"><a id="l01746" name="l01746"></a><span class="lineno"> 1746</span> <span class="keywordflow">if</span> (initial_solution.has_value()) {</div>
|
|
<div class="line"><a id="l01747" name="l01747"></a><span class="lineno"> 1747</span> current_primal_solution_ = std::move(initial_solution->primal_solution);</div>
|
|
<div class="line"><a id="l01748" name="l01748"></a><span class="lineno"> 1748</span> current_dual_solution_ = std::move(initial_solution->dual_solution);</div>
|
|
<div class="line"><a id="l01749" name="l01749"></a><span class="lineno"> 1749</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01750" name="l01750"></a><span class="lineno"> 1750</span> current_primal_solution_.setZero(sharded_working_qp_.PrimalSize());</div>
|
|
<div class="line"><a id="l01751" name="l01751"></a><span class="lineno"> 1751</span> current_dual_solution_.setZero(sharded_working_qp_.DualSize());</div>
|
|
<div class="line"><a id="l01752" name="l01752"></a><span class="lineno"> 1752</span> }</div>
|
|
<div class="line"><a id="l01753" name="l01753"></a><span class="lineno"> 1753</span> <span class="comment">// The following projections are necessary since all our checks assume that</span></div>
|
|
<div class="line"><a id="l01754" name="l01754"></a><span class="lineno"> 1754</span> <span class="comment">// the primal and dual variable bounds are satisfied.</span></div>
|
|
<div class="line"><a id="l01755" name="l01755"></a><span class="lineno"> 1755</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#acb7f29f435d6c9fc53148ee403c7049e">ProjectToPrimalVariableBounds</a>(sharded_working_qp_, current_primal_solution_);</div>
|
|
<div class="line"><a id="l01756" name="l01756"></a><span class="lineno"> 1756</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a898c0c776a5736cf1931036d0d370724">ProjectToDualVariableBounds</a>(sharded_working_qp_, current_dual_solution_);</div>
|
|
<div class="line"><a id="l01757" name="l01757"></a><span class="lineno"> 1757</span> </div>
|
|
<div class="line"><a id="l01758" name="l01758"></a><span class="lineno"> 1758</span> ComputeAndApplyRescaling();</div>
|
|
<div class="line"><a id="l01759" name="l01759"></a><span class="lineno"> 1759</span> *solve_log.mutable_preprocessed_problem_stats() = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(</div>
|
|
<div class="line"><a id="l01760" name="l01760"></a><span class="lineno"> 1760</span> sharded_working_qp_, params_.infinite_constraint_bound_threshold());</div>
|
|
<div class="line"><a id="l01761" name="l01761"></a><span class="lineno"> 1761</span> <span class="keywordflow">if</span> (params_.verbosity_level() >= 1) {</div>
|
|
<div class="line"><a id="l01762" name="l01762"></a><span class="lineno"> 1762</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a>) << <span class="stringliteral">"Problem stats after "</span> << preprocessing_string;</div>
|
|
<div class="line"><a id="l01763" name="l01763"></a><span class="lineno"> 1763</span> LogQuadraticProgramStats(solve_log.preprocessed_problem_stats());</div>
|
|
<div class="line"><a id="l01764" name="l01764"></a><span class="lineno"> 1764</span> }</div>
|
|
<div class="line"><a id="l01765" name="l01765"></a><span class="lineno"> 1765</span> </div>
|
|
<div class="line"><a id="l01766" name="l01766"></a><span class="lineno"> 1766</span> <span class="keywordflow">if</span> (params_.linesearch_rule() ==</div>
|
|
<div class="line"><a id="l01767" name="l01767"></a><span class="lineno"> 1767</span> PrimalDualHybridGradientParams::CONSTANT_STEP_SIZE_RULE) {</div>
|
|
<div class="line"><a id="l01768" name="l01768"></a><span class="lineno"> 1768</span> std::mt19937 random(1);</div>
|
|
<div class="line"><a id="l01769" name="l01769"></a><span class="lineno"> 1769</span> <span class="keywordtype">double</span> inverse_step_size;</div>
|
|
<div class="line"><a id="l01770" name="l01770"></a><span class="lineno"> 1770</span> <span class="keyword">const</span> <span class="keyword">auto</span> lipschitz_result =</div>
|
|
<div class="line"><a id="l01771" name="l01771"></a><span class="lineno"> 1771</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a880902cb3a98b7205fa57be9e16a82c7">EstimateMaximumSingularValueOfConstraintMatrix</a>(</div>
|
|
<div class="line"><a id="l01772" name="l01772"></a><span class="lineno"> 1772</span> sharded_working_qp_, absl::nullopt, absl::nullopt,</div>
|
|
<div class="line"><a id="l01773" name="l01773"></a><span class="lineno"> 1773</span> <span class="comment">/*desired_relative_error=*/</span>0.2, <span class="comment">/*failure_probability=*/</span>0.0005,</div>
|
|
<div class="line"><a id="l01774" name="l01774"></a><span class="lineno"> 1774</span> random);</div>
|
|
<div class="line"><a id="l01775" name="l01775"></a><span class="lineno"> 1775</span> <span class="comment">// With high probability, the estimate of the lipschitz term is within</span></div>
|
|
<div class="line"><a id="l01776" name="l01776"></a><span class="lineno"> 1776</span> <span class="comment">// +/- estimated_relative_error * lipschitz_term.</span></div>
|
|
<div class="line"><a id="l01777" name="l01777"></a><span class="lineno"> 1777</span> <span class="keyword">const</span> <span class="keywordtype">double</span> lipschitz_term_upper_bound =</div>
|
|
<div class="line"><a id="l01778" name="l01778"></a><span class="lineno"> 1778</span> lipschitz_result.singular_value /</div>
|
|
<div class="line"><a id="l01779" name="l01779"></a><span class="lineno"> 1779</span> (1.0 - lipschitz_result.estimated_relative_error);</div>
|
|
<div class="line"><a id="l01780" name="l01780"></a><span class="lineno"> 1780</span> inverse_step_size = lipschitz_term_upper_bound;</div>
|
|
<div class="line"><a id="l01781" name="l01781"></a><span class="lineno"> 1781</span> step_size_ = inverse_step_size > 0.0 ? 1.0 / inverse_step_size : 1.0;</div>
|
|
<div class="line"><a id="l01782" name="l01782"></a><span class="lineno"> 1782</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l01783" name="l01783"></a><span class="lineno"> 1783</span> <span class="comment">// This initial step size is designed to err on the side of being too big.</span></div>
|
|
<div class="line"><a id="l01784" name="l01784"></a><span class="lineno"> 1784</span> <span class="comment">// This is because</span></div>
|
|
<div class="line"><a id="l01785" name="l01785"></a><span class="lineno"> 1785</span> <span class="comment">// (i) too-big steps are rejected and hence don't hurt us other than</span></div>
|
|
<div class="line"><a id="l01786" name="l01786"></a><span class="lineno"> 1786</span> <span class="comment">// wasting</span></div>
|
|
<div class="line"><a id="l01787" name="l01787"></a><span class="lineno"> 1787</span> <span class="comment">// an iteration and</span></div>
|
|
<div class="line"><a id="l01788" name="l01788"></a><span class="lineno"> 1788</span> <span class="comment">// (ii) the step size adjustment algorithm shrinks the step size as far as</span></div>
|
|
<div class="line"><a id="l01789" name="l01789"></a><span class="lineno"> 1789</span> <span class="comment">// needed in a single iteration but raises it slowly.</span></div>
|
|
<div class="line"><a id="l01790" name="l01790"></a><span class="lineno"> 1790</span> <span class="comment">// The tiny constant is there to keep the step size finite in the case of a</span></div>
|
|
<div class="line"><a id="l01791" name="l01791"></a><span class="lineno"> 1791</span> <span class="comment">// trivial LP with no constraints.</span></div>
|
|
<div class="line"><a id="l01792" name="l01792"></a><span class="lineno"> 1792</span> step_size_ =</div>
|
|
<div class="line"><a id="l01793" name="l01793"></a><span class="lineno"> 1793</span> 1.0 /</div>
|
|
<div class="line"><a id="l01794" name="l01794"></a><span class="lineno"> 1794</span> <a class="code hl_variable" href="alldiff__cst_8cc.html#a26e6db9bcc64b584051ecc28171ed11f">std::max</a>(</div>
|
|
<div class="line"><a id="l01795" name="l01795"></a><span class="lineno"> 1795</span> 1.0e-20,</div>
|
|
<div class="line"><a id="l01796" name="l01796"></a><span class="lineno"> 1796</span> solve_log.preprocessed_problem_stats().constraint_matrix_abs_max());</div>
|
|
<div class="line"><a id="l01797" name="l01797"></a><span class="lineno"> 1797</span> }</div>
|
|
<div class="line"><a id="l01798" name="l01798"></a><span class="lineno"> 1798</span> step_size_ *= params_.initial_step_size_scaling();</div>
|
|
<div class="line"><a id="l01799" name="l01799"></a><span class="lineno"> 1799</span> </div>
|
|
<div class="line"><a id="l01800" name="l01800"></a><span class="lineno"> 1800</span> primal_weight_ = InitialPrimalWeight(</div>
|
|
<div class="line"><a id="l01801" name="l01801"></a><span class="lineno"> 1801</span> solve_log.preprocessed_problem_stats().objective_vector_l2_norm(),</div>
|
|
<div class="line"><a id="l01802" name="l01802"></a><span class="lineno"> 1802</span> solve_log.preprocessed_problem_stats().combined_bounds_l2_norm());</div>
|
|
<div class="line"><a id="l01803" name="l01803"></a><span class="lineno"> 1803</span> last_primal_start_point_ = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#aaa4a3bad4a7c95a6d68387ba8ae8c104">CloneVector</a>(current_primal_solution_,</div>
|
|
<div class="line"><a id="l01804" name="l01804"></a><span class="lineno"> 1804</span> sharded_working_qp_.PrimalSharder());</div>
|
|
<div class="line"><a id="l01805" name="l01805"></a><span class="lineno"> 1805</span> last_dual_start_point_ =</div>
|
|
<div class="line"><a id="l01806" name="l01806"></a><span class="lineno"> 1806</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#aaa4a3bad4a7c95a6d68387ba8ae8c104">CloneVector</a>(current_dual_solution_, sharded_working_qp_.DualSharder());</div>
|
|
<div class="line"><a id="l01807" name="l01807"></a><span class="lineno"> 1807</span> <span class="comment">// Note: Any cached values computed here also need to be recomputed after a</span></div>
|
|
<div class="line"><a id="l01808" name="l01808"></a><span class="lineno"> 1808</span> <span class="comment">// restart.</span></div>
|
|
<div class="line"><a id="l01809" name="l01809"></a><span class="lineno"> 1809</span> ratio_last_two_step_sizes_ = 1;</div>
|
|
<div class="line"><a id="l01810" name="l01810"></a><span class="lineno"> 1810</span> current_dual_product_ = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a463586ded0a114d3ca4b97a048d37d8a">TransposedMatrixVectorProduct</a>(</div>
|
|
<div class="line"><a id="l01811" name="l01811"></a><span class="lineno"> 1811</span> WorkingQp().constraint_matrix, current_dual_solution_,</div>
|
|
<div class="line"><a id="l01812" name="l01812"></a><span class="lineno"> 1812</span> sharded_working_qp_.ConstraintMatrixSharder());</div>
|
|
<div class="line"><a id="l01813" name="l01813"></a><span class="lineno"> 1813</span> </div>
|
|
<div class="line"><a id="l01814" name="l01814"></a><span class="lineno"> 1814</span> <span class="comment">// This is set to true if we can't proceed any more because of numerical</span></div>
|
|
<div class="line"><a id="l01815" name="l01815"></a><span class="lineno"> 1815</span> <span class="comment">// issues. We may or may not have found the optimal solution.</span></div>
|
|
<div class="line"><a id="l01816" name="l01816"></a><span class="lineno"> 1816</span> <span class="keywordtype">bool</span> force_numerical_termination = <span class="keyword">false</span>;</div>
|
|
<div class="line"><a id="l01817" name="l01817"></a><span class="lineno"> 1817</span> </div>
|
|
<div class="line"><a id="l01818" name="l01818"></a><span class="lineno"> 1818</span> num_rejected_steps_ = 0;</div>
|
|
<div class="line"><a id="l01819" name="l01819"></a><span class="lineno"> 1819</span> </div>
|
|
<div class="line"><a id="l01820" name="l01820"></a><span class="lineno"> 1820</span> solve_log.set_preprocessing_time_sec(timer_.<a class="code hl_function" href="class_wall_timer.html#aec56fe080959ecebec3feaed9dafde84">Get</a>());</div>
|
|
<div class="line"><a id="l01821" name="l01821"></a><span class="lineno"> 1821</span> </div>
|
|
<div class="line"><a id="l01822" name="l01822"></a><span class="lineno"> 1822</span> <span class="keywordflow">for</span> (iterations_completed_ = 0;; ++iterations_completed_) {</div>
|
|
<div class="line"><a id="l01823" name="l01823"></a><span class="lineno"> 1823</span> <span class="comment">// This code performs the logic of the major iterations and termination</span></div>
|
|
<div class="line"><a id="l01824" name="l01824"></a><span class="lineno"> 1824</span> <span class="comment">// checks. It may modify the current solution and primal weight (e.g., when</span></div>
|
|
<div class="line"><a id="l01825" name="l01825"></a><span class="lineno"> 1825</span> <span class="comment">// performing a restart).</span></div>
|
|
<div class="line"><a id="l01826" name="l01826"></a><span class="lineno"> 1826</span> <span class="keyword">const</span> absl::optional<SolverResult> maybe_result =</div>
|
|
<div class="line"><a id="l01827" name="l01827"></a><span class="lineno"> 1827</span> MajorIterationAndTerminationCheck(force_numerical_termination,</div>
|
|
<div class="line"><a id="l01828" name="l01828"></a><span class="lineno"> 1828</span> solve_log);</div>
|
|
<div class="line"><a id="l01829" name="l01829"></a><span class="lineno"> 1829</span> <span class="keywordflow">if</span> (maybe_result.has_value()) {</div>
|
|
<div class="line"><a id="l01830" name="l01830"></a><span class="lineno"> 1830</span> <span class="keywordflow">return</span> maybe_result.value();</div>
|
|
<div class="line"><a id="l01831" name="l01831"></a><span class="lineno"> 1831</span> }</div>
|
|
<div class="line"><a id="l01832" name="l01832"></a><span class="lineno"> 1832</span> <span class="comment">// Check for interrupt on every iteration.</span></div>
|
|
<div class="line"><a id="l01833" name="l01833"></a><span class="lineno"> 1833</span> <span class="keywordflow">if</span> (interrupt_solve != <span class="keyword">nullptr</span> && interrupt_solve->load() == <span class="keyword">true</span>) {</div>
|
|
<div class="line"><a id="l01834" name="l01834"></a><span class="lineno"> 1834</span> <span class="keywordflow">return</span> ConstructSolverResult(</div>
|
|
<div class="line"><a id="l01835" name="l01835"></a><span class="lineno"> 1835</span> PrimalAverage(), DualAverage(),</div>
|
|
<div class="line"><a id="l01836" name="l01836"></a><span class="lineno"> 1836</span> CreateSimpleIterationStats(RESTART_CHOICE_NO_RESTART),</div>
|
|
<div class="line"><a id="l01837" name="l01837"></a><span class="lineno"> 1837</span> TERMINATION_REASON_INTERRUPTED_BY_USER, POINT_TYPE_NONE, solve_log);</div>
|
|
<div class="line"><a id="l01838" name="l01838"></a><span class="lineno"> 1838</span> }</div>
|
|
<div class="line"><a id="l01839" name="l01839"></a><span class="lineno"> 1839</span> </div>
|
|
<div class="line"><a id="l01840" name="l01840"></a><span class="lineno"> 1840</span> <span class="comment">// TODO(user): If we use a step rule that could reject many steps in a</span></div>
|
|
<div class="line"><a id="l01841" name="l01841"></a><span class="lineno"> 1841</span> <span class="comment">// row, we should add a termination check within this loop also. For the</span></div>
|
|
<div class="line"><a id="l01842" name="l01842"></a><span class="lineno"> 1842</span> <span class="comment">// Malitsky and Pock rule, we perform a termination check and declare</span></div>
|
|
<div class="line"><a id="l01843" name="l01843"></a><span class="lineno"> 1843</span> <span class="comment">// NUMERICAL_ERROR whenever we hit 60 inner iterations.</span></div>
|
|
<div class="line"><a id="l01844" name="l01844"></a><span class="lineno"> 1844</span> InnerStepOutcome outcome;</div>
|
|
<div class="line"><a id="l01845" name="l01845"></a><span class="lineno"> 1845</span> <span class="keywordflow">switch</span> (params_.linesearch_rule()) {</div>
|
|
<div class="line"><a id="l01846" name="l01846"></a><span class="lineno"> 1846</span> <span class="keywordflow">case</span> PrimalDualHybridGradientParams::MALITSKY_POCK_LINESEARCH_RULE:</div>
|
|
<div class="line"><a id="l01847" name="l01847"></a><span class="lineno"> 1847</span> outcome = TakeMalitskyPockStep();</div>
|
|
<div class="line"><a id="l01848" name="l01848"></a><span class="lineno"> 1848</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01849" name="l01849"></a><span class="lineno"> 1849</span> <span class="keywordflow">case</span> PrimalDualHybridGradientParams::ADAPTIVE_LINESEARCH_RULE:</div>
|
|
<div class="line"><a id="l01850" name="l01850"></a><span class="lineno"> 1850</span> outcome = TakeAdaptiveStep();</div>
|
|
<div class="line"><a id="l01851" name="l01851"></a><span class="lineno"> 1851</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01852" name="l01852"></a><span class="lineno"> 1852</span> <span class="keywordflow">case</span> PrimalDualHybridGradientParams::CONSTANT_STEP_SIZE_RULE:</div>
|
|
<div class="line"><a id="l01853" name="l01853"></a><span class="lineno"> 1853</span> outcome = TakeConstantSizeStep();</div>
|
|
<div class="line"><a id="l01854" name="l01854"></a><span class="lineno"> 1854</span> <span class="keywordflow">break</span>;</div>
|
|
<div class="line"><a id="l01855" name="l01855"></a><span class="lineno"> 1855</span> <span class="keywordflow">default</span>:</div>
|
|
<div class="line"><a id="l01856" name="l01856"></a><span class="lineno"> 1856</span> <a class="code hl_define" href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a>(<a class="code hl_variable" href="log__severity_8h.html#acdd38e3c9f22f127d7776920e3079eda">FATAL</a>) << <span class="stringliteral">"Unrecognized linesearch rule "</span></div>
|
|
<div class="line"><a id="l01857" name="l01857"></a><span class="lineno"> 1857</span> << params_.linesearch_rule();</div>
|
|
<div class="line"><a id="l01858" name="l01858"></a><span class="lineno"> 1858</span> }</div>
|
|
<div class="line"><a id="l01859" name="l01859"></a><span class="lineno"> 1859</span> <span class="keywordflow">if</span> (outcome == InnerStepOutcome::kForceNumericalTermination) {</div>
|
|
<div class="line"><a id="l01860" name="l01860"></a><span class="lineno"> 1860</span> force_numerical_termination = <span class="keyword">true</span>;</div>
|
|
<div class="line"><a id="l01861" name="l01861"></a><span class="lineno"> 1861</span> }</div>
|
|
<div class="line"><a id="l01862" name="l01862"></a><span class="lineno"> 1862</span> } <span class="comment">// loop over iterations</span></div>
|
|
<div class="line"><a id="l01863" name="l01863"></a><span class="lineno"> 1863</span>}</div>
|
|
<div class="line"><a id="l01864" name="l01864"></a><span class="lineno"> 1864</span> </div>
|
|
<div class="line"><a id="l01865" name="l01865"></a><span class="lineno"> 1865</span>} <span class="comment">// namespace</span></div>
|
|
<div class="line"><a id="l01866" name="l01866"></a><span class="lineno"> 1866</span> </div>
|
|
<div class="line"><a id="l01867" name="l01867"></a><span class="lineno"><a class="line" href="namespaceoperations__research_1_1pdlp.html#ac43b4c39d3be5e0bced44530d50f8208"> 1867</a></span><a class="code hl_struct" href="structoperations__research_1_1pdlp_1_1_solver_result.html">SolverResult</a> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ae070a9361e9af01b7a77f1b0b3aaf3a5">PrimalDualHybridGradient</a>(</div>
|
|
<div class="line"><a id="l01868" name="l01868"></a><span class="lineno"> 1868</span> <a class="code hl_struct" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html">QuadraticProgram</a> qp, <span class="keyword">const</span> PrimalDualHybridGradientParams& params,</div>
|
|
<div class="line"><a id="l01869" name="l01869"></a><span class="lineno"> 1869</span> <span class="keyword">const</span> std::atomic<bool>* interrupt_solve,</div>
|
|
<div class="line"><a id="l01870" name="l01870"></a><span class="lineno"> 1870</span> IterationStatsCallback iteration_stats_callback) {</div>
|
|
<div class="line"><a id="l01871" name="l01871"></a><span class="lineno"> 1871</span> <span class="keywordflow">return</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ae070a9361e9af01b7a77f1b0b3aaf3a5">PrimalDualHybridGradient</a>(std::move(qp), params, absl::nullopt,</div>
|
|
<div class="line"><a id="l01872" name="l01872"></a><span class="lineno"> 1872</span> interrupt_solve,</div>
|
|
<div class="line"><a id="l01873" name="l01873"></a><span class="lineno"> 1873</span> std::move(iteration_stats_callback));</div>
|
|
<div class="line"><a id="l01874" name="l01874"></a><span class="lineno"> 1874</span>}</div>
|
|
<div class="line"><a id="l01875" name="l01875"></a><span class="lineno"> 1875</span> </div>
|
|
<div class="line"><a id="l01876" name="l01876"></a><span class="lineno"><a class="line" href="namespaceoperations__research_1_1pdlp.html#ae070a9361e9af01b7a77f1b0b3aaf3a5"> 1876</a></span><a class="code hl_struct" href="structoperations__research_1_1pdlp_1_1_solver_result.html">SolverResult</a> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ae070a9361e9af01b7a77f1b0b3aaf3a5">PrimalDualHybridGradient</a>(</div>
|
|
<div class="line"><a id="l01877" name="l01877"></a><span class="lineno"> 1877</span> <a class="code hl_struct" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html">QuadraticProgram</a> qp, <span class="keyword">const</span> PrimalDualHybridGradientParams& params,</div>
|
|
<div class="line"><a id="l01878" name="l01878"></a><span class="lineno"> 1878</span> absl::optional<PrimalAndDualSolution> initial_solution,</div>
|
|
<div class="line"><a id="l01879" name="l01879"></a><span class="lineno"> 1879</span> <span class="keyword">const</span> std::atomic<bool>* interrupt_solve,</div>
|
|
<div class="line"><a id="l01880" name="l01880"></a><span class="lineno"> 1880</span> IterationStatsCallback iteration_stats_callback) {</div>
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<div class="line"><a id="l01881" name="l01881"></a><span class="lineno"> 1881</span> <span class="keyword">const</span> absl::Status params_status =</div>
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<div class="line"><a id="l01882" name="l01882"></a><span class="lineno"> 1882</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afe67d69670c32d98eea0f73c8a311e51">ValidatePrimalDualHybridGradientParams</a>(params);</div>
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<div class="line"><a id="l01883" name="l01883"></a><span class="lineno"> 1883</span> <span class="keywordflow">if</span> (!params_status.ok()) {</div>
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<div class="line"><a id="l01884" name="l01884"></a><span class="lineno"> 1884</span> <span class="keywordflow">return</span> ErrorSolverResult(TERMINATION_REASON_INVALID_PARAMETER,</div>
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<div class="line"><a id="l01885" name="l01885"></a><span class="lineno"> 1885</span> params_status.ToString());</div>
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<div class="line"><a id="l01886" name="l01886"></a><span class="lineno"> 1886</span> }</div>
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<div class="line"><a id="l01887" name="l01887"></a><span class="lineno"> 1887</span> <span class="keywordflow">if</span> (!qp.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#ae7a462ef3035095eff6c883ae0078d02">constraint_matrix</a>.isCompressed()) {</div>
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<div class="line"><a id="l01888" name="l01888"></a><span class="lineno"> 1888</span> <span class="keywordflow">return</span> ErrorSolverResult(TERMINATION_REASON_INVALID_PROBLEM,</div>
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<div class="line"><a id="l01889" name="l01889"></a><span class="lineno"> 1889</span> <span class="stringliteral">"constraint_matrix must be in compressed format. "</span></div>
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<div class="line"><a id="l01890" name="l01890"></a><span class="lineno"> 1890</span> <span class="stringliteral">"Call constraint_matrix.makeCompressed()"</span>);</div>
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<div class="line"><a id="l01891" name="l01891"></a><span class="lineno"> 1891</span> }</div>
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<div class="line"><a id="l01892" name="l01892"></a><span class="lineno"> 1892</span> <span class="keyword">const</span> absl::Status dimensions_status = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a26dcf89d9520f56d883f7100dfd36146">ValidateQuadraticProgramDimensions</a>(qp);</div>
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<div class="line"><a id="l01893" name="l01893"></a><span class="lineno"> 1893</span> <span class="keywordflow">if</span> (!dimensions_status.ok()) {</div>
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<div class="line"><a id="l01894" name="l01894"></a><span class="lineno"> 1894</span> <span class="keywordflow">return</span> ErrorSolverResult(TERMINATION_REASON_INVALID_PROBLEM,</div>
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<div class="line"><a id="l01895" name="l01895"></a><span class="lineno"> 1895</span> dimensions_status.ToString());</div>
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<div class="line"><a id="l01896" name="l01896"></a><span class="lineno"> 1896</span> }</div>
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<div class="line"><a id="l01897" name="l01897"></a><span class="lineno"> 1897</span> <span class="keywordflow">if</span> (!<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a77dbe245ed9fb597ad836b27ac989f26">HasValidBounds</a>(qp)) {</div>
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<div class="line"><a id="l01898" name="l01898"></a><span class="lineno"> 1898</span> <span class="keywordflow">return</span> ErrorSolverResult(TERMINATION_REASON_INVALID_PROBLEM,</div>
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<div class="line"><a id="l01899" name="l01899"></a><span class="lineno"> 1899</span> <span class="stringliteral">"The input problem has inconsistent bounds."</span>);</div>
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<div class="line"><a id="l01900" name="l01900"></a><span class="lineno"> 1900</span> }</div>
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<div class="line"><a id="l01901" name="l01901"></a><span class="lineno"> 1901</span> <span class="keywordflow">if</span> (qp.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a18b5b62c2150cdcf678427d52b05a949">objective_scaling_factor</a> == 0) {</div>
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<div class="line"><a id="l01902" name="l01902"></a><span class="lineno"> 1902</span> <span class="keywordflow">return</span> ErrorSolverResult(TERMINATION_REASON_INVALID_PROBLEM,</div>
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<div class="line"><a id="l01903" name="l01903"></a><span class="lineno"> 1903</span> <span class="stringliteral">"The objective scaling factor cannot be zero."</span>);</div>
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<div class="line"><a id="l01904" name="l01904"></a><span class="lineno"> 1904</span> }</div>
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<div class="line"><a id="l01905" name="l01905"></a><span class="lineno"> 1905</span> Solver solver(std::move(qp), params);</div>
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<div class="line"><a id="l01906" name="l01906"></a><span class="lineno"> 1906</span> <span class="keywordflow">return</span> solver.Solve(std::move(initial_solution), interrupt_solve,</div>
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<div class="line"><a id="l01907" name="l01907"></a><span class="lineno"> 1907</span> std::move(iteration_stats_callback));</div>
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<div class="line"><a id="l01908" name="l01908"></a><span class="lineno"> 1908</span>}</div>
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<div class="line"><a id="l01909" name="l01909"></a><span class="lineno"> 1909</span> </div>
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<div class="line"><a id="l01910" name="l01910"></a><span class="lineno"><a class="line" href="namespaceoperations__research_1_1pdlp_1_1internal.html"> 1910</a></span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceinternal.html">internal</a> {</div>
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<div class="line"><a id="l01911" name="l01911"></a><span class="lineno"> 1911</span> </div>
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<div class="line"><a id="l01912" name="l01912"></a><span class="lineno"><a class="line" href="namespaceoperations__research_1_1pdlp_1_1internal.html#a3fe24e4dddd7460838c010f64fd3569a"> 1912</a></span><a class="code hl_struct" href="structoperations__research_1_1glop_1_1_problem_solution.html">glop::ProblemSolution</a> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp_1_1internal.html#a3fe24e4dddd7460838c010f64fd3569a">ComputeStatuses</a>(<span class="keyword">const</span> <a class="code hl_struct" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html">QuadraticProgram</a>& qp,</div>
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<div class="line"><a id="l01913" name="l01913"></a><span class="lineno"> 1913</span> <span class="keyword">const</span> <a class="code hl_struct" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html">PrimalAndDualSolution</a>& solution) {</div>
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<div class="line"><a id="l01914" name="l01914"></a><span class="lineno"> 1914</span> <a class="code hl_struct" href="structoperations__research_1_1glop_1_1_problem_solution.html">glop::ProblemSolution</a> glop_solution(</div>
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<div class="line"><a id="l01915" name="l01915"></a><span class="lineno"> 1915</span> glop::RowIndex(solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#a7240a2ad18b5d152e29675e53bcd117d">dual_solution</a>.size()),</div>
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<div class="line"><a id="l01916" name="l01916"></a><span class="lineno"> 1916</span> glop::ColIndex(solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#aba446ee2bbf0217015660220ecf8d935">primal_solution</a>.size()));</div>
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<div class="line"><a id="l01917" name="l01917"></a><span class="lineno"> 1917</span> <span class="comment">// This doesn't matter much as glop's preprocessor doesn't use this much.</span></div>
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<div class="line"><a id="l01918" name="l01918"></a><span class="lineno"> 1918</span> <span class="comment">// We pick IMPRECISE since we are often calling this code early in the solve.</span></div>
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<div class="line"><a id="l01919" name="l01919"></a><span class="lineno"> 1919</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#a15eb0790f4f62ad63676f55e4ba7d2bb">status</a> = glop::ProblemStatus::IMPRECISE;</div>
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<div class="line"><a id="l01920" name="l01920"></a><span class="lineno"> 1920</span> <span class="keywordflow">for</span> (glop::RowIndex i{0}; i.value() < solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#a7240a2ad18b5d152e29675e53bcd117d">dual_solution</a>.size(); ++i) {</div>
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<div class="line"><a id="l01921" name="l01921"></a><span class="lineno"> 1921</span> <span class="keywordflow">if</span> (qp.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a61349a88b7e83784a92be3d231cfa638">constraint_lower_bounds</a>[i.value()] ==</div>
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<div class="line"><a id="l01922" name="l01922"></a><span class="lineno"> 1922</span> qp.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#af2acc3fce9196f0cd70ed7505923234c">constraint_upper_bounds</a>[i.value()]) {</div>
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<div class="line"><a id="l01923" name="l01923"></a><span class="lineno"> 1923</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#ae7a0a13dedf8ae7920036bfff1ad9c3b">constraint_statuses</a>[i] =</div>
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<div class="line"><a id="l01924" name="l01924"></a><span class="lineno"> 1924</span> glop::ConstraintStatus::FIXED_VALUE;</div>
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<div class="line"><a id="l01925" name="l01925"></a><span class="lineno"> 1925</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#a7240a2ad18b5d152e29675e53bcd117d">dual_solution</a>[i.value()] > 0) {</div>
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<div class="line"><a id="l01926" name="l01926"></a><span class="lineno"> 1926</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#ae7a0a13dedf8ae7920036bfff1ad9c3b">constraint_statuses</a>[i] =</div>
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<div class="line"><a id="l01927" name="l01927"></a><span class="lineno"> 1927</span> glop::ConstraintStatus::AT_LOWER_BOUND;</div>
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<div class="line"><a id="l01928" name="l01928"></a><span class="lineno"> 1928</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#a7240a2ad18b5d152e29675e53bcd117d">dual_solution</a>[i.value()] < 0) {</div>
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<div class="line"><a id="l01929" name="l01929"></a><span class="lineno"> 1929</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#ae7a0a13dedf8ae7920036bfff1ad9c3b">constraint_statuses</a>[i] =</div>
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<div class="line"><a id="l01930" name="l01930"></a><span class="lineno"> 1930</span> glop::ConstraintStatus::AT_UPPER_BOUND;</div>
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<div class="line"><a id="l01931" name="l01931"></a><span class="lineno"> 1931</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l01932" name="l01932"></a><span class="lineno"> 1932</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#ae7a0a13dedf8ae7920036bfff1ad9c3b">constraint_statuses</a>[i] = glop::ConstraintStatus::BASIC;</div>
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<div class="line"><a id="l01933" name="l01933"></a><span class="lineno"> 1933</span> }</div>
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<div class="line"><a id="l01934" name="l01934"></a><span class="lineno"> 1934</span> }</div>
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<div class="line"><a id="l01935" name="l01935"></a><span class="lineno"> 1935</span> </div>
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<div class="line"><a id="l01936" name="l01936"></a><span class="lineno"> 1936</span> <span class="keywordflow">for</span> (glop::ColIndex i{0}; i.value() < solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#aba446ee2bbf0217015660220ecf8d935">primal_solution</a>.size(); ++i) {</div>
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<div class="line"><a id="l01937" name="l01937"></a><span class="lineno"> 1937</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> at_lb = solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#aba446ee2bbf0217015660220ecf8d935">primal_solution</a>[i.value()] <=</div>
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<div class="line"><a id="l01938" name="l01938"></a><span class="lineno"> 1938</span> qp.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a0f72e7b49f91d0b980f5a54a18c06964">variable_lower_bounds</a>[i.value()];</div>
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<div class="line"><a id="l01939" name="l01939"></a><span class="lineno"> 1939</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> at_ub = solution.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#aba446ee2bbf0217015660220ecf8d935">primal_solution</a>[i.value()] >=</div>
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<div class="line"><a id="l01940" name="l01940"></a><span class="lineno"> 1940</span> qp.<a class="code hl_variable" href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a097d329b7af662bea9b5a8e310a22726">variable_upper_bounds</a>[i.value()];</div>
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<div class="line"><a id="l01941" name="l01941"></a><span class="lineno"> 1941</span> <span class="comment">// Note that ShardedWeightedAverage is designed so that variables at their</span></div>
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<div class="line"><a id="l01942" name="l01942"></a><span class="lineno"> 1942</span> <span class="comment">// bounds will be exactly at their bounds even with floating-point roundoff.</span></div>
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<div class="line"><a id="l01943" name="l01943"></a><span class="lineno"> 1943</span> <span class="keywordflow">if</span> (at_lb) {</div>
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<div class="line"><a id="l01944" name="l01944"></a><span class="lineno"> 1944</span> <span class="keywordflow">if</span> (at_ub) {</div>
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<div class="line"><a id="l01945" name="l01945"></a><span class="lineno"> 1945</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#a887a20330f1f58adbe564ef0fcf74e8c">variable_statuses</a>[i] = glop::VariableStatus::FIXED_VALUE;</div>
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<div class="line"><a id="l01946" name="l01946"></a><span class="lineno"> 1946</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l01947" name="l01947"></a><span class="lineno"> 1947</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#a887a20330f1f58adbe564ef0fcf74e8c">variable_statuses</a>[i] =</div>
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<div class="line"><a id="l01948" name="l01948"></a><span class="lineno"> 1948</span> glop::VariableStatus::AT_LOWER_BOUND;</div>
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<div class="line"><a id="l01949" name="l01949"></a><span class="lineno"> 1949</span> }</div>
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<div class="line"><a id="l01950" name="l01950"></a><span class="lineno"> 1950</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l01951" name="l01951"></a><span class="lineno"> 1951</span> <span class="keywordflow">if</span> (at_ub) {</div>
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<div class="line"><a id="l01952" name="l01952"></a><span class="lineno"> 1952</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#a887a20330f1f58adbe564ef0fcf74e8c">variable_statuses</a>[i] =</div>
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<div class="line"><a id="l01953" name="l01953"></a><span class="lineno"> 1953</span> glop::VariableStatus::AT_UPPER_BOUND;</div>
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<div class="line"><a id="l01954" name="l01954"></a><span class="lineno"> 1954</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l01955" name="l01955"></a><span class="lineno"> 1955</span> glop_solution.<a class="code hl_variable" href="structoperations__research_1_1glop_1_1_problem_solution.html#a887a20330f1f58adbe564ef0fcf74e8c">variable_statuses</a>[i] = glop::VariableStatus::BASIC;</div>
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<div class="line"><a id="l01956" name="l01956"></a><span class="lineno"> 1956</span> }</div>
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<div class="line"><a id="l01957" name="l01957"></a><span class="lineno"> 1957</span> }</div>
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<div class="line"><a id="l01958" name="l01958"></a><span class="lineno"> 1958</span> }</div>
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<div class="line"><a id="l01959" name="l01959"></a><span class="lineno"> 1959</span> <span class="keywordflow">return</span> glop_solution;</div>
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<div class="line"><a id="l01960" name="l01960"></a><span class="lineno"> 1960</span>}</div>
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<div class="line"><a id="l01961" name="l01961"></a><span class="lineno"> 1961</span> </div>
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<div class="line"><a id="l01962" name="l01962"></a><span class="lineno"> 1962</span>} <span class="comment">// namespace internal</span></div>
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<div class="line"><a id="l01963" name="l01963"></a><span class="lineno"> 1963</span> </div>
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<div class="line"><a id="l01964" name="l01964"></a><span class="lineno"> 1964</span>} <span class="comment">// namespace operations_research::pdlp</span></div>
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<div class="ttc" id="aalldiff__cst_8cc_html_a26e6db9bcc64b584051ecc28171ed11f"><div class="ttname"><a href="alldiff__cst_8cc.html#a26e6db9bcc64b584051ecc28171ed11f">max</a></div><div class="ttdeci">int64_t max</div><div class="ttdef"><b>Definition:</b> <a href="alldiff__cst_8cc_source.html#l00140">alldiff_cst.cc:140</a></div></div>
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<div class="ttc" id="aalldiff__cst_8cc_html_ad10edae0a852d72fb76afb1c77735045"><div class="ttname"><a href="alldiff__cst_8cc.html#ad10edae0a852d72fb76afb1c77735045">min</a></div><div class="ttdeci">int64_t min</div><div class="ttdef"><b>Definition:</b> <a href="alldiff__cst_8cc_source.html#l00139">alldiff_cst.cc:139</a></div></div>
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<div class="ttc" id="abase_2logging_8h_html"><div class="ttname"><a href="base_2logging_8h.html">logging.h</a></div></div>
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<div class="ttc" id="abase_2logging_8h_html_a09f7d88282cf92c9f231270ac113e5c6"><div class="ttname"><a href="base_2logging_8h.html#a09f7d88282cf92c9f231270ac113e5c6">LOG_IF</a></div><div class="ttdeci">#define LOG_IF(severity, condition)</div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8h_source.html#l00479">base/logging.h:479</a></div></div>
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<div class="ttc" id="abase_2logging_8h_html_a7c0ce053b28d53aa4eaf3eb7fb71663b"><div class="ttname"><a href="base_2logging_8h.html#a7c0ce053b28d53aa4eaf3eb7fb71663b">CHECK_EQ</a></div><div class="ttdeci">#define CHECK_EQ(val1, val2)</div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8h_source.html#l00703">base/logging.h:703</a></div></div>
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<div class="ttc" id="abase_2logging_8h_html_a9f96ed9f06763f0821fdbb4d29031d8d"><div class="ttname"><a href="base_2logging_8h.html#a9f96ed9f06763f0821fdbb4d29031d8d">CHECK_OK</a></div><div class="ttdeci">#define CHECK_OK(x)</div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8h_source.html#l00044">base/logging.h:44</a></div></div>
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<div class="ttc" id="abase_2logging_8h_html_accad43a85d781d53381cd53a9894b6ae"><div class="ttname"><a href="base_2logging_8h.html#accad43a85d781d53381cd53a9894b6ae">LOG</a></div><div class="ttdeci">#define LOG(severity)</div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8h_source.html#l00420">base/logging.h:420</a></div></div>
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<div class="ttc" id="abase_2logging_8h_html_ae17f8119c108cf3070bad3449c7e0006"><div class="ttname"><a href="base_2logging_8h.html#ae17f8119c108cf3070bad3449c7e0006">DCHECK</a></div><div class="ttdeci">#define DCHECK(condition)</div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8h_source.html#l00890">base/logging.h:890</a></div></div>
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<div class="ttc" id="aclass_wall_timer_html"><div class="ttname"><a href="class_wall_timer.html">WallTimer</a></div><div class="ttdef"><b>Definition:</b> <a href="timer_8h_source.html#l00023">timer.h:23</a></div></div>
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<div class="ttc" id="aclass_wall_timer_html_a07aaf1227e4d645f15e0a964f54ef291"><div class="ttname"><a href="class_wall_timer.html#a07aaf1227e4d645f15e0a964f54ef291">WallTimer::Start</a></div><div class="ttdeci">void Start()</div><div class="ttdef"><b>Definition:</b> <a href="timer_8h_source.html#l00031">timer.h:31</a></div></div>
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<div class="ttc" id="aclass_wall_timer_html_aec56fe080959ecebec3feaed9dafde84"><div class="ttname"><a href="class_wall_timer.html#aec56fe080959ecebec3feaed9dafde84">WallTimer::Get</a></div><div class="ttdeci">double Get() const</div><div class="ttdef"><b>Definition:</b> <a href="timer_8h_source.html#l00045">timer.h:45</a></div></div>
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<div class="ttc" id="aclassgoogle_1_1_log_message_html"><div class="ttname"><a href="classgoogle_1_1_log_message.html">google::LogMessage</a></div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8h_source.html#l01018">base/logging.h:1018</a></div></div>
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<div class="ttc" id="aclassgoogle_1_1_log_message_html_a48387141df3f5afb48b012cc28ac244c"><div class="ttname"><a href="classgoogle_1_1_log_message.html#a48387141df3f5afb48b012cc28ac244c">google::LogMessage::stream</a></div><div class="ttdeci">std::ostream & stream()</div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8cc_source.html#l01197">base/logging.cc:1197</a></div></div>
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<div class="ttc" id="aclassgoogle_1_1_log_message_html_a99fb83031ce9923c84392b4e92f956b5af8756cb52a8aac6329e43782e08f69e5"><div class="ttname"><a href="classgoogle_1_1_log_message.html#a99fb83031ce9923c84392b4e92f956b5af8756cb52a8aac6329e43782e08f69e5">google::LogMessage::kNoLogPrefix</a></div><div class="ttdeci">@ kNoLogPrefix</div><div class="ttdef"><b>Definition:</b> <a href="base_2logging_8h_source.html#l01027">base/logging.h:1026</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1_solver_html_abac10873a1af49f1dce33a34f3afaa56"><div class="ttname"><a href="classoperations__research_1_1_solver.html#abac10873a1af49f1dce33a34f3afaa56">operations_research::Solver::Solver</a></div><div class="ttdeci">Solver(const std::string &name)</div><div class="ttdoc">Solver API.</div><div class="ttdef"><b>Definition:</b> <a href="constraint__solver_8cc_source.html#l01421">constraint_solver.cc:1421</a></div></div>
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<div class="ttc" id="agurobi__interface_8cc_html_a0728f23c9a47655d38e0bf1a2f200bcf"><div class="ttname"><a href="gurobi__interface_8cc.html#a0728f23c9a47655d38e0bf1a2f200bcf">model</a></div><div class="ttdeci">GRBmodel * model</div><div class="ttdef"><b>Definition:</b> <a href="gurobi__interface_8cc_source.html#l00274">gurobi_interface.cc:274</a></div></div>
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<div class="ttc" id="aiteration__stats_8h_html"><div class="ttname"><a href="iteration__stats_8h.html">iteration_stats.h</a></div></div>
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<div class="ttc" id="alog__severity_8h_html_a50e5762f38854b37ee3e2851bc1bb0e7"><div class="ttname"><a href="log__severity_8h.html#a50e5762f38854b37ee3e2851bc1bb0e7">WARNING</a></div><div class="ttdeci">const int WARNING</div><div class="ttdef"><b>Definition:</b> <a href="log__severity_8h_source.html#l00031">log_severity.h:31</a></div></div>
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<div class="ttc" id="alog__severity_8h_html_ab4a2cbab234914b320b7fae11b6e8cb9"><div class="ttname"><a href="log__severity_8h.html#ab4a2cbab234914b320b7fae11b6e8cb9">INFO</a></div><div class="ttdeci">const int INFO</div><div class="ttdef"><b>Definition:</b> <a href="log__severity_8h_source.html#l00031">log_severity.h:31</a></div></div>
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<div class="ttc" id="alog__severity_8h_html_acdd38e3c9f22f127d7776920e3079eda"><div class="ttname"><a href="log__severity_8h.html#acdd38e3c9f22f127d7776920e3079eda">FATAL</a></div><div class="ttdeci">const int FATAL</div><div class="ttdef"><b>Definition:</b> <a href="log__severity_8h_source.html#l00032">log_severity.h:32</a></div></div>
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<div class="ttc" id="alp__data_2proto__utils_8h_html"><div class="ttname"><a href="lp__data_2proto__utils_8h.html">proto_utils.h</a></div></div>
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<div class="ttc" id="alp__data_8h_html"><div class="ttname"><a href="lp__data_8h.html">lp_data.h</a></div></div>
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<div class="ttc" id="alp__types_8h_html"><div class="ttname"><a href="lp__types_8h.html">lp_types.h</a></div></div>
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<div class="ttc" id="amathutil_8h_html"><div class="ttname"><a href="mathutil_8h.html">mathutil.h</a></div></div>
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<div class="ttc" id="anamespacegoogle_html_af3c2db675e75f2074724f754d3cf7885"><div class="ttname"><a href="namespacegoogle.html#af3c2db675e75f2074724f754d3cf7885">google::GLOG_INFO</a></div><div class="ttdeci">const int GLOG_INFO</div><div class="ttdef"><b>Definition:</b> <a href="log__severity_8h_source.html#l00025">log_severity.h:25</a></div></div>
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<div class="ttc" id="anamespaceinternal_html"><div class="ttname"><a href="namespaceinternal.html">internal</a></div><div class="ttdef"><b>Definition:</b> <a href="connected__components_8h_source.html#l00138">connected_components.h:138</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1glop_html_a1dcd08b0f6c19cd4a302bb5a3a6ea06e"><div class="ttname"><a href="namespaceoperations__research_1_1glop.html#a1dcd08b0f6c19cd4a302bb5a3a6ea06e">operations_research::glop::Square</a></div><div class="ttdeci">Fractional Square(Fractional f)</div><div class="ttdef"><b>Definition:</b> <a href="lp__data_2lp__utils_8h_source.html#l00036">lp_data/lp_utils.h:36</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1glop_html_a2d53948bf5e999d006e781105aa8bc77"><div class="ttname"><a href="namespaceoperations__research_1_1glop.html#a2d53948bf5e999d006e781105aa8bc77">operations_research::glop::SquaredNorm</a></div><div class="ttdeci">Fractional SquaredNorm(const SparseColumn &v)</div><div class="ttdef"><b>Definition:</b> <a href="lp__data_2lp__utils_8cc_source.html#l00030">lp_data/lp_utils.cc:30</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1glop_html_a4066bdd6e74f798c189fa8e830fcd37b"><div class="ttname"><a href="namespaceoperations__research_1_1glop.html#a4066bdd6e74f798c189fa8e830fcd37b">operations_research::glop::MPModelProtoToLinearProgram</a></div><div class="ttdeci">void MPModelProtoToLinearProgram(const MPModelProto &input, LinearProgram *output)</div><div class="ttdef"><b>Definition:</b> <a href="proto__utils_8cc_source.html#l00051">proto_utils.cc:51</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1glop_html_a4dc8ae0f97c4110f3cc770715b2bbd12"><div class="ttname"><a href="namespaceoperations__research_1_1glop.html#a4dc8ae0f97c4110f3cc770715b2bbd12">operations_research::glop::DenseRow</a></div><div class="ttdeci">StrictITIVector< ColIndex, Fractional > DenseRow</div><div class="ttdef"><b>Definition:</b> <a href="lp__types_8h_source.html#l00303">lp_types.h:303</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1glop_html_a8750840afdd5774223821fd504df04c5"><div class="ttname"><a href="namespaceoperations__research_1_1glop.html#a8750840afdd5774223821fd504df04c5">operations_research::glop::LinearProgramToMPModelProto</a></div><div class="ttdeci">void LinearProgramToMPModelProto(const LinearProgram &input, MPModelProto *output)</div><div class="ttdef"><b>Definition:</b> <a href="proto__utils_8cc_source.html#l00020">proto_utils.cc:20</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1glop_html_a884f3b645d22471e5ed3320e182cd493"><div class="ttname"><a href="namespaceoperations__research_1_1glop.html#a884f3b645d22471e5ed3320e182cd493">operations_research::glop::ProblemStatus</a></div><div class="ttdeci">ProblemStatus</div><div class="ttdef"><b>Definition:</b> <a href="lp__types_8h_source.html#l00102">lp_types.h:102</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1glop_html_ae5fa9e57c2d31b294195ed8a9e17bfac"><div class="ttname"><a href="namespaceoperations__research_1_1glop.html#ae5fa9e57c2d31b294195ed8a9e17bfac">operations_research::glop::DenseColumn</a></div><div class="ttdeci">StrictITIVector< RowIndex, Fractional > DenseColumn</div><div class="ttdef"><b>Definition:</b> <a href="lp__types_8h_source.html#l00332">lp_types.h:332</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1math__opt_html_a5767ee23f380e72488d3c7ebf2d742b1"><div class="ttname"><a href="namespaceoperations__research_1_1math__opt.html#a5767ee23f380e72488d3c7ebf2d742b1">operations_research::math_opt::Solve</a></div><div class="ttdeci">absl::StatusOr< SolveResult > Solve(const Model &model, const SolverType solver_type, const SolveArguments &solve_args, const SolverInitArguments &init_args)</div><div class="ttdef"><b>Definition:</b> <a href="solve_8cc_source.html#l00094">solve.cc:94</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1math__opt_html_ad02e69a0531469b463df907c7b2ad194"><div class="ttname"><a href="namespaceoperations__research_1_1math__opt.html#ad02e69a0531469b463df907c7b2ad194">operations_research::math_opt::TerminationReason</a></div><div class="ttdeci">TerminationReason</div><div class="ttdef"><b>Definition:</b> <a href="solve__result_8h_source.html#l00150">solve_result.h:150</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_1_1internal_html_a3fe24e4dddd7460838c010f64fd3569a"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp_1_1internal.html#a3fe24e4dddd7460838c010f64fd3569a">operations_research::pdlp::internal::ComputeStatuses</a></div><div class="ttdeci">glop::ProblemSolution ComputeStatuses(const QuadraticProgram &qp, const PrimalAndDualSolution &solution)</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l01912">primal_dual_hybrid_gradient.cc:1912</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html">operations_research::pdlp</a></div><div class="ttdef"><b>Definition:</b> <a href="iteration__stats_8cc_source.html#l00040">iteration_stats.cc:40</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a007a657dbfa4e0e820e0d9af4d8d27a2"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a007a657dbfa4e0e820e0d9af4d8d27a2">operations_research::pdlp::QpFromMpModelProto</a></div><div class="ttdeci">absl::StatusOr< QuadraticProgram > QpFromMpModelProto(const MPModelProto &proto, bool relax_integer_variables, bool include_names)</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8cc_source.html#l00094">quadratic_program.cc:94</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a26dcf89d9520f56d883f7100dfd36146"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a26dcf89d9520f56d883f7100dfd36146">operations_research::pdlp::ValidateQuadraticProgramDimensions</a></div><div class="ttdeci">absl::Status ValidateQuadraticProgramDimensions(const QuadraticProgram &qp)</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8cc_source.html#l00039">quadratic_program.cc:39</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a32389515e696df20cec86493cf9852e6"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a32389515e696df20cec86493cf9852e6">operations_research::pdlp::SquaredDistance</a></div><div class="ttdeci">double SquaredDistance(const VectorXd &vector1, const VectorXd &vector2, const Sharder &sharder)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00224">sharder.cc:224</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a3e28f45b9c1ccdec8d926b4034d3679b"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a3e28f45b9c1ccdec8d926b4034d3679b">operations_research::pdlp::Distance</a></div><div class="ttdeci">double Distance(const VectorXd &vector1, const VectorXd &vector2, const Sharder &sharder)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00231">sharder.cc:231</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a463586ded0a114d3ca4b97a048d37d8a"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a463586ded0a114d3ca4b97a048d37d8a">operations_research::pdlp::TransposedMatrixVectorProduct</a></div><div class="ttdeci">VectorXd TransposedMatrixVectorProduct(const Eigen::SparseMatrix< double, Eigen::ColMajor, int64_t > &matrix, const VectorXd &vector, const Sharder &sharder)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00151">sharder.cc:151</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a64bfea523f69cba6f7be8ac302c18f2f"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a64bfea523f69cba6f7be8ac302c18f2f">operations_research::pdlp::ReducedCosts</a></div><div class="ttdeci">VectorXd ReducedCosts(const ShardedQuadraticProgram &sharded_qp, const VectorXd &primal_solution, const VectorXd &dual_solution, bool use_zero_primal_objective)</div><div class="ttdef"><b>Definition:</b> <a href="iteration__stats_8cc_source.html#l00474">iteration_stats.cc:474</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a74af5ceb7b6e37fbfca92e2c59b99e3e"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a74af5ceb7b6e37fbfca92e2c59b99e3e">operations_research::pdlp::ComputeInfeasibilityInformation</a></div><div class="ttdeci">InfeasibilityInformation ComputeInfeasibilityInformation(const ShardedQuadraticProgram &scaled_sharded_qp, const Eigen::VectorXd &col_scaling_vec, const Eigen::VectorXd &row_scaling_vec, const Eigen::VectorXd &scaled_primal_ray, const Eigen::VectorXd &scaled_dual_ray, PointType candidate_type)</div><div class="ttdef"><b>Definition:</b> <a href="iteration__stats_8cc_source.html#l00372">iteration_stats.cc:372</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a77dbe245ed9fb597ad836b27ac989f26"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a77dbe245ed9fb597ad836b27ac989f26">operations_research::pdlp::HasValidBounds</a></div><div class="ttdeci">bool HasValidBounds(const QuadraticProgram &qp)</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8cc_source.html#l00084">quadratic_program.cc:84</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a7b93e1d980b7d8112423361ac15a0c28"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a7b93e1d980b7d8112423361ac15a0c28">operations_research::pdlp::GetConvergenceInformation</a></div><div class="ttdeci">absl::optional< ConvergenceInformation > GetConvergenceInformation(const IterationStats &stats, PointType candidate_type)</div><div class="ttdef"><b>Definition:</b> <a href="iteration__stats_8cc_source.html#l00507">iteration_stats.cc:507</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a7d2e7889c98661aba130697a142fdf4b"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a7d2e7889c98661aba130697a142fdf4b">operations_research::pdlp::CheckTerminationCriteria</a></div><div class="ttdeci">absl::optional< TerminationReasonAndPointType > CheckTerminationCriteria(const TerminationCriteria &criteria, const IterationStats &stats, const QuadraticProgramBoundNorms &bound_norms, const bool force_numerical_termination)</div><div class="ttdef"><b>Definition:</b> <a href="termination_8cc_source.html#l00090">termination.cc:90</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a7f791925d78c8eb11002320336d0410d"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a7f791925d78c8eb11002320336d0410d">operations_research::pdlp::QpToMpModelProto</a></div><div class="ttdeci">absl::StatusOr< MPModelProto > QpToMpModelProto(const QuadraticProgram &qp)</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8cc_source.html#l00237">quadratic_program.cc:237</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a850865b3deabb2a623e130691df99f15"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a850865b3deabb2a623e130691df99f15">operations_research::pdlp::IsLinearProgram</a></div><div class="ttdeci">bool IsLinearProgram(const QuadraticProgram &qp)</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00150">quadratic_program.h:150</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a85cd9828e35e9f00a622d0376bc81325"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a85cd9828e35e9f00a622d0376bc81325">operations_research::pdlp::SetRandomProjections</a></div><div class="ttdeci">void SetRandomProjections(const ShardedQuadraticProgram &sharded_qp, const Eigen::VectorXd &primal_solution, const Eigen::VectorXd &dual_solution, const std::vector< int > &random_projection_seeds, PointMetadata &metadata)</div><div class="ttdef"><b>Definition:</b> <a href="iteration__stats_8cc_source.html#l00538">iteration_stats.cc:538</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a880902cb3a98b7205fa57be9e16a82c7"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a880902cb3a98b7205fa57be9e16a82c7">operations_research::pdlp::EstimateMaximumSingularValueOfConstraintMatrix</a></div><div class="ttdeci">SingularValueAndIterations EstimateMaximumSingularValueOfConstraintMatrix(const ShardedQuadraticProgram &sharded_qp, const absl::optional< VectorXd > &primal_solution, const absl::optional< VectorXd > &dual_solution, const double desired_relative_error, const double failure_probability, std::mt19937 &mt_generator)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00706">sharded_optimization_utils.cc:706</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a898c0c776a5736cf1931036d0d370724"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a898c0c776a5736cf1931036d0d370724">operations_research::pdlp::ProjectToDualVariableBounds</a></div><div class="ttdeci">void ProjectToDualVariableBounds(const ShardedQuadraticProgram &sharded_qp, VectorXd &dual)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00762">sharded_optimization_utils.cc:762</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a8e307fb8ac2854dd493d52760dd3aa30"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a8e307fb8ac2854dd493d52760dd3aa30">operations_research::pdlp::ComputeRelativeResiduals</a></div><div class="ttdeci">RelativeConvergenceInformation ComputeRelativeResiduals(const double eps_optimal_absolute, const double eps_optimal_relative, const QuadraticProgramBoundNorms &norms, const ConvergenceInformation &stats)</div><div class="ttdef"><b>Definition:</b> <a href="termination_8cc_source.html#l00147">termination.cc:147</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a920005e41b36a7a0c7f4ad148ad7069d"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a920005e41b36a7a0c7f4ad148ad7069d">operations_research::pdlp::CoefficientWiseProductInPlace</a></div><div class="ttdeci">void CoefficientWiseProductInPlace(const VectorXd &scale, const Sharder &sharder, VectorXd &dest)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00182">sharder.cc:182</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a92c8ca6bf2bb288c322e1d8fbd6ea2bc"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a92c8ca6bf2bb288c322e1d8fbd6ea2bc">operations_research::pdlp::CoefficientWiseQuotientInPlace</a></div><div class="ttdeci">void CoefficientWiseQuotientInPlace(const VectorXd &scale, const Sharder &sharder, VectorXd &dest)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00190">sharder.cc:190</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_aaa4a3bad4a7c95a6d68387ba8ae8c104"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#aaa4a3bad4a7c95a6d68387ba8ae8c104">operations_research::pdlp::CloneVector</a></div><div class="ttdeci">VectorXd CloneVector(const VectorXd &vec, const Sharder &sharder)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00175">sharder.cc:175</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_aac68304831a1bc81557fb03623a619d6"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#aac68304831a1bc81557fb03623a619d6">operations_research::pdlp::ApplyRescaling</a></div><div class="ttdeci">ScalingVectors ApplyRescaling(const RescalingOptions &rescaling_options, ShardedQuadraticProgram &sharded_qp)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00439">sharded_optimization_utils.cc:439</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_ab315578d37cb2f5e1111b0176254cb84"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">operations_research::pdlp::ComputeStats</a></div><div class="ttdeci">QuadraticProgramStats ComputeStats(const ShardedQuadraticProgram &qp, const double infinite_constraint_bound_threshold)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00303">sharded_optimization_utils.cc:303</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_ac69c2a44159905c520139def17af7e4f"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#ac69c2a44159905c520139def17af7e4f">operations_research::pdlp::BoundNormsFromProblemStats</a></div><div class="ttdeci">QuadraticProgramBoundNorms BoundNormsFromProblemStats(const QuadraticProgramStats &stats)</div><div class="ttdef"><b>Definition:</b> <a href="termination_8cc_source.html#l00138">termination.cc:138</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_ac77694ebaac0adfa0fce8422782c48c8"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#ac77694ebaac0adfa0fce8422782c48c8">operations_research::pdlp::ComputeConvergenceInformation</a></div><div class="ttdeci">ConvergenceInformation ComputeConvergenceInformation(const ShardedQuadraticProgram &scaled_sharded_qp, const Eigen::VectorXd &col_scaling_vec, const Eigen::VectorXd &row_scaling_vec, const Eigen::VectorXd &scaled_primal_solution, const Eigen::VectorXd &scaled_dual_solution, PointType candidate_type)</div><div class="ttdef"><b>Definition:</b> <a href="iteration__stats_8cc_source.html#l00308">iteration_stats.cc:308</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_acb7f29f435d6c9fc53148ee403c7049e"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#acb7f29f435d6c9fc53148ee403c7049e">operations_research::pdlp::ProjectToPrimalVariableBounds</a></div><div class="ttdeci">void ProjectToPrimalVariableBounds(const ShardedQuadraticProgram &sharded_qp, VectorXd &primal)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00751">sharded_optimization_utils.cc:751</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_adb77e7cede2fecf6bccfa93226b49c0b"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#adb77e7cede2fecf6bccfa93226b49c0b">operations_research::pdlp::ComputeLocalizedLagrangianBounds</a></div><div class="ttdeci">LocalizedLagrangianBounds ComputeLocalizedLagrangianBounds(const ShardedQuadraticProgram &sharded_qp, const VectorXd &primal_solution, const VectorXd &dual_solution, const PrimalDualNorm primal_dual_norm, const double primal_weight, const double radius, const VectorXd *primal_product, const VectorXd *dual_product, const bool use_diagonal_qp_trust_region_solver, const double diagonal_qp_trust_region_solver_tolerance)</div><div class="ttdef"><b>Definition:</b> <a href="trust__region_8cc_source.html#l00963">trust_region.cc:963</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_ade56a0bd875b06000c45e1730398e5a8"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#ade56a0bd875b06000c45e1730398e5a8">operations_research::pdlp::Norm</a></div><div class="ttdeci">double Norm(const VectorXd &vector, const Sharder &sharder)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00220">sharder.cc:220</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_ae070a9361e9af01b7a77f1b0b3aaf3a5"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#ae070a9361e9af01b7a77f1b0b3aaf3a5">operations_research::pdlp::PrimalDualHybridGradient</a></div><div class="ttdeci">SolverResult PrimalDualHybridGradient(QuadraticProgram qp, const PrimalDualHybridGradientParams &params, absl::optional< PrimalAndDualSolution > initial_solution, const std::atomic< bool > *interrupt_solve, IterationStatsCallback iteration_stats_callback)</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l01876">primal_dual_hybrid_gradient.cc:1876</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_afca8f74da7e8301c8aee45f33c93896c"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#afca8f74da7e8301c8aee45f33c93896c">operations_research::pdlp::AssignVector</a></div><div class="ttdeci">void AssignVector(const VectorXd &vec, const Sharder &sharder, VectorXd &dest)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00169">sharder.cc:169</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_afdd1506c32f697aeb13c4b9a9f05ba03"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#afdd1506c32f697aeb13c4b9a9f05ba03">operations_research::pdlp::BoundGap</a></div><div class="ttdeci">double BoundGap(const LocalizedLagrangianBounds &bounds)</div><div class="ttdef"><b>Definition:</b> <a href="trust__region_8h_source.html#l00113">trust_region.h:113</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_afe67d69670c32d98eea0f73c8a311e51"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#afe67d69670c32d98eea0f73c8a311e51">operations_research::pdlp::ValidatePrimalDualHybridGradientParams</a></div><div class="ttdeci">absl::Status ValidatePrimalDualHybridGradientParams(const PrimalDualHybridGradientParams &params)</div><div class="ttdef"><b>Definition:</b> <a href="solvers__proto__validation_8cc_source.html#l00083">solvers_proto_validation.cc:83</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_html_a23fc0ff92a3f47fe0bd2ad3eac3c9b57"><div class="ttname"><a href="namespaceoperations__research.html#a23fc0ff92a3f47fe0bd2ad3eac3c9b57">operations_research::ToString</a></div><div class="ttdeci">const absl::string_view ToString(MPSolver::OptimizationProblemType optimization_problem_type)</div><div class="ttdef"><b>Definition:</b> <a href="linear__solver_8cc_source.html#l00581">linear_solver.cc:581</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_html_a5a9881f8a07b166ef2cbde572cea27b6"><div class="ttname"><a href="namespaceoperations__research.html#a5a9881f8a07b166ef2cbde572cea27b6">operations_research::Zero</a></div><div class="ttdeci">int64_t Zero()</div><div class="ttdoc">NOLINT.</div><div class="ttdef"><b>Definition:</b> <a href="constraint__solver_8h_source.html#l03161">constraint_solver.h:3161</a></div></div>
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<div class="ttc" id="anamespacestd_html"><div class="ttname"><a href="namespacestd.html">std</a></div><div class="ttdoc">STL namespace.</div></div>
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<div class="ttc" id="apreprocessor_8h_html"><div class="ttname"><a href="preprocessor_8h.html">preprocessor.h</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_a00a26e2a8ca2ce9d8f5492d1722f7be7"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#a00a26e2a8ca2ce9d8f5492d1722f7be7">presolved_problem_was_maximization</a></div><div class="ttdeci">bool presolved_problem_was_maximization</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00268">primal_dual_hybrid_gradient.cc:268</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_a1339b97193af37ff85bd41146dba5290"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#a1339b97193af37ff85bd41146dba5290">preprocessor</a></div><div class="ttdeci">glop::MainLpPreprocessor preprocessor</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00266">primal_dual_hybrid_gradient.cc:266</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_a730b1ea892f1f794d9bd5f16027acb63"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#a730b1ea892f1f794d9bd5f16027acb63">value</a></div><div class="ttdeci">VectorXd value</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00245">primal_dual_hybrid_gradient.cc:245</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_a8684c349890b354a93a8b977029fc58d"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#a8684c349890b354a93a8b977029fc58d">trivial_row_scaling_vec</a></div><div class="ttdeci">const VectorXd trivial_row_scaling_vec</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00269">primal_dual_hybrid_gradient.cc:269</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_ab4d0766b8b9bbc7fdfddbafe8dda4c97"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#ab4d0766b8b9bbc7fdfddbafe8dda4c97">sharded_original_qp</a></div><div class="ttdeci">ShardedQuadraticProgram sharded_original_qp</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00267">primal_dual_hybrid_gradient.cc:267</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_ac7a159e390ca0bbb5d8aba647055448b"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#ac7a159e390ca0bbb5d8aba647055448b">trivial_col_scaling_vec</a></div><div class="ttdeci">const VectorXd trivial_col_scaling_vec</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00269">primal_dual_hybrid_gradient.cc:269</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_ae3146d59eb9e49c48bd3ea7b3e60ab65"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#ae3146d59eb9e49c48bd3ea7b3e60ab65">distance_moved_last_restart_period</a></div><div class="ttdeci">double distance_moved_last_restart_period</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00251">primal_dual_hybrid_gradient.cc:251</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_ae99d92906640f8755574c1f93f39d320"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#ae99d92906640f8755574c1f93f39d320">delta</a></div><div class="ttdeci">VectorXd delta</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00247">primal_dual_hybrid_gradient.cc:247</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_af6ec6f87520da8de4c5522f7bd04dfe4"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#af6ec6f87520da8de4c5522f7bd04dfe4">length_of_last_restart_period</a></div><div class="ttdeci">int length_of_last_restart_period</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00252">primal_dual_hybrid_gradient.cc:252</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8cc_html_afc562f1013a6986df081403ff83fdeac"><div class="ttname"><a href="primal__dual__hybrid__gradient_8cc.html#afc562f1013a6986df081403ff83fdeac">preprocessor_parameters</a></div><div class="ttdeci">glop::GlopParameters preprocessor_parameters</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8cc_source.html#l00265">primal_dual_hybrid_gradient.cc:265</a></div></div>
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<div class="ttc" id="aprimal__dual__hybrid__gradient_8h_html"><div class="ttname"><a href="primal__dual__hybrid__gradient_8h.html">primal_dual_hybrid_gradient.h</a></div></div>
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<div class="ttc" id="aquadratic__program_8h_html"><div class="ttname"><a href="quadratic__program_8h.html">quadratic_program.h</a></div></div>
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<div class="ttc" id="asharded__optimization__utils_8h_html"><div class="ttname"><a href="sharded__optimization__utils_8h.html">sharded_optimization_utils.h</a></div></div>
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<div class="ttc" id="asharded__quadratic__program_8h_html"><div class="ttname"><a href="sharded__quadratic__program_8h.html">sharded_quadratic_program.h</a></div></div>
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<div class="ttc" id="asharder_8h_html"><div class="ttname"><a href="sharder_8h.html">sharder.h</a></div></div>
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<div class="ttc" id="asolvers__proto__validation_8h_html"><div class="ttname"><a href="solvers__proto__validation_8h.html">solvers_proto_validation.h</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1glop_1_1_problem_solution_html"><div class="ttname"><a href="structoperations__research_1_1glop_1_1_problem_solution.html">operations_research::glop::ProblemSolution</a></div><div class="ttdef"><b>Definition:</b> <a href="lp__data_8h_source.html#l00660">lp_data.h:660</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1glop_1_1_problem_solution_html_a15eb0790f4f62ad63676f55e4ba7d2bb"><div class="ttname"><a href="structoperations__research_1_1glop_1_1_problem_solution.html#a15eb0790f4f62ad63676f55e4ba7d2bb">operations_research::glop::ProblemSolution::status</a></div><div class="ttdeci">ProblemStatus status</div><div class="ttdef"><b>Definition:</b> <a href="lp__data_8h_source.html#l00668">lp_data.h:668</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1glop_1_1_problem_solution_html_a887a20330f1f58adbe564ef0fcf74e8c"><div class="ttname"><a href="structoperations__research_1_1glop_1_1_problem_solution.html#a887a20330f1f58adbe564ef0fcf74e8c">operations_research::glop::ProblemSolution::variable_statuses</a></div><div class="ttdeci">VariableStatusRow variable_statuses</div><div class="ttdef"><b>Definition:</b> <a href="lp__data_8h_source.html#l00688">lp_data.h:688</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1glop_1_1_problem_solution_html_ae7a0a13dedf8ae7920036bfff1ad9c3b"><div class="ttname"><a href="structoperations__research_1_1glop_1_1_problem_solution.html#ae7a0a13dedf8ae7920036bfff1ad9c3b">operations_research::glop::ProblemSolution::constraint_statuses</a></div><div class="ttdeci">ConstraintStatusColumn constraint_statuses</div><div class="ttdef"><b>Definition:</b> <a href="lp__data_8h_source.html#l00689">lp_data.h:689</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_primal_and_dual_solution_html"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html">operations_research::pdlp::PrimalAndDualSolution</a></div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8h_source.html#l00032">primal_dual_hybrid_gradient.h:32</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_primal_and_dual_solution_html_a7240a2ad18b5d152e29675e53bcd117d"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#a7240a2ad18b5d152e29675e53bcd117d">operations_research::pdlp::PrimalAndDualSolution::dual_solution</a></div><div class="ttdeci">Eigen::VectorXd dual_solution</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8h_source.html#l00034">primal_dual_hybrid_gradient.h:34</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_primal_and_dual_solution_html_aba446ee2bbf0217015660220ecf8d935"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_primal_and_dual_solution.html#aba446ee2bbf0217015660220ecf8d935">operations_research::pdlp::PrimalAndDualSolution::primal_solution</a></div><div class="ttdeci">Eigen::VectorXd primal_solution</div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8h_source.html#l00033">primal_dual_hybrid_gradient.h:33</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_quadratic_program_html"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_quadratic_program.html">operations_research::pdlp::QuadraticProgram</a></div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00054">quadratic_program.h:54</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_quadratic_program_html_a097d329b7af662bea9b5a8e310a22726"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a097d329b7af662bea9b5a8e310a22726">operations_research::pdlp::QuadraticProgram::variable_upper_bounds</a></div><div class="ttdeci">Eigen::VectorXd variable_upper_bounds</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00133">quadratic_program.h:133</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_quadratic_program_html_a0f72e7b49f91d0b980f5a54a18c06964"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a0f72e7b49f91d0b980f5a54a18c06964">operations_research::pdlp::QuadraticProgram::variable_lower_bounds</a></div><div class="ttdeci">Eigen::VectorXd variable_lower_bounds</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00133">quadratic_program.h:133</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_quadratic_program_html_a18b5b62c2150cdcf678427d52b05a949"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a18b5b62c2150cdcf678427d52b05a949">operations_research::pdlp::QuadraticProgram::objective_scaling_factor</a></div><div class="ttdeci">double objective_scaling_factor</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00143">quadratic_program.h:143</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_quadratic_program_html_a61349a88b7e83784a92be3d231cfa638"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#a61349a88b7e83784a92be3d231cfa638">operations_research::pdlp::QuadraticProgram::constraint_lower_bounds</a></div><div class="ttdeci">Eigen::VectorXd constraint_lower_bounds</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00132">quadratic_program.h:132</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_quadratic_program_html_ae7a462ef3035095eff6c883ae0078d02"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#ae7a462ef3035095eff6c883ae0078d02">operations_research::pdlp::QuadraticProgram::constraint_matrix</a></div><div class="ttdeci">Eigen::SparseMatrix< double, Eigen::ColMajor, int64_t > constraint_matrix</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00131">quadratic_program.h:131</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_quadratic_program_html_af2acc3fce9196f0cd70ed7505923234c"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_quadratic_program.html#af2acc3fce9196f0cd70ed7505923234c">operations_research::pdlp::QuadraticProgram::constraint_upper_bounds</a></div><div class="ttdeci">Eigen::VectorXd constraint_upper_bounds</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8h_source.html#l00132">quadratic_program.h:132</a></div></div>
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<div class="ttc" id="astructoperations__research_1_1pdlp_1_1_solver_result_html"><div class="ttname"><a href="structoperations__research_1_1pdlp_1_1_solver_result.html">operations_research::pdlp::SolverResult</a></div><div class="ttdef"><b>Definition:</b> <a href="primal__dual__hybrid__gradient_8h_source.html#l00061">primal_dual_hybrid_gradient.h:61</a></div></div>
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<div class="ttc" id="atermination_8h_html"><div class="ttname"><a href="termination_8h.html">termination.h</a></div></div>
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<div class="ttc" id="atimer_8h_html"><div class="ttname"><a href="timer_8h.html">timer.h</a></div></div>
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<div class="ttc" id="atrace_8cc_html_a36bd74109f547f7f8198faf5a12d2879"><div class="ttname"><a href="trace_8cc.html#a36bd74109f547f7f8198faf5a12d2879">message</a></div><div class="ttdeci">std::string message</div><div class="ttdef"><b>Definition:</b> <a href="trace_8cc_source.html#l00398">trace.cc:398</a></div></div>
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