always-cautious/ortools-clone
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add missing basic examples

Browse Source 
C++:
 - [Up] linear_programming
 - [Up] integer_programming
 - constraint_programming_CP / rabbits_pheasants_cp
 - knapsack
 - max_flow / min_cost_flow
 - tsp / vrp
note: previous "fuzzy" tsp has been renamed random_tsp.

.Net:
 - vrp
This commit is contained in:
Corentin Le Molgat
2018-09-26 11:02:04 +02:00
parent a2978f293d
commit 027f5cc3f8
15 changed files with 1133 additions and 271 deletions
Download Patch File Download Diff File Expand all files Collapse all files

105
examples/cpp/integer_programming.cc
View File

@@ -1,4 +1,4 @@
// Copyright 2010-2017 Google
// Copyright 2010-2018 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
@@ -11,79 +11,82 @@
// See the License for the specific language governing permissions and
// limitations under the License.
//
// Integer programming example that shows how to use the API.
#include "ortools/base/commandlineflags.h"
#include "ortools/base/logging.h"
#include "ortools/linear_solver/linear_solver.h"
namespace operations_research {
void RunIntegerProgrammingExample(
MPSolver::OptimizationProblemType optimization_problem_type) {
MPSolver solver("IntegerProgrammingExample", optimization_problem_type);
const double infinity = solver.infinity();
// x1 and x2 are integer non-negative variables.
MPVariable* const x1 = solver.MakeIntVar(0.0, infinity, "x1");
MPVariable* const x2 = solver.MakeIntVar(0.0, infinity, "x2");
void RunIntegerProgrammingExample(
MPSolver::OptimizationProblemType optimization_problem_type) {
MPSolver solver("IntegerProgrammingExample", optimization_problem_type);
const double infinity = solver.infinity();
// x and y are integer non-negative variables.
MPVariable* const x = solver.MakeIntVar(0.0, infinity, "x");
MPVariable* const y = solver.MakeIntVar(0.0, infinity, "y");
// Minimize x1 + 2 * x2.
MPObjective* const objective = solver.MutableObjective();
objective->SetCoefficient(x1, 1);
objective->SetCoefficient(x2, 2);
// Maximize x + 10 * y.
MPObjective* const objective = solver.MutableObjective();
objective->SetCoefficient(x, 1);
objective->SetCoefficient(y, 10);
objective->SetMaximization();
// 2 * x2 + 3 * x1 >= 17.
MPConstraint* const c0 = solver.MakeRowConstraint(17, infinity);
c0->SetCoefficient(x1, 3);
c0->SetCoefficient(x2, 2);
// x + 7 * y <= 17.5.
MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 17.5);
c0->SetCoefficient(x, 1);
c0->SetCoefficient(y, 7);
const MPSolver::ResultStatus result_status = solver.Solve();
// x <= 3.5
MPConstraint* const c1 = solver.MakeRowConstraint(-infinity, 3.5);
c1->SetCoefficient(x, 1);
c1->SetCoefficient(y, 0);
// Check that the problem has an optimal solution.
if (result_status != MPSolver::OPTIMAL) {
LOG(FATAL) << "The problem does not have an optimal solution!";
LOG(INFO) << "Number of variables = " << solver.NumVariables();
LOG(INFO) << "Number of constraints = " << solver.NumConstraints();
const MPSolver::ResultStatus result_status = solver.Solve();
// Check that the problem has an optimal solution.
if (result_status != MPSolver::OPTIMAL) {
LOG(FATAL) << "The problem does not have an optimal solution!";
}
LOG(INFO) << "Solution:";
LOG(INFO) << "x = " << x->solution_value();
LOG(INFO) << "y = " << y->solution_value();
LOG(INFO) << "Optimal objective value = " << objective->Value();
LOG(INFO) << "";
LOG(INFO) << "Advanced usage:";
LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
LOG(INFO) << "Problem solved in " << solver.iterations() << " iterations";
LOG(INFO) << "Problem solved in " << solver.nodes() << " branch-and-bound nodes";
}
LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
// The objective value of the solution.
LOG(INFO) << "Optimal objective value = " << objective->Value();
// The value of each variable in the solution.
LOG(INFO) << "x1 = " << x1->solution_value();
LOG(INFO) << "x2 = " << x2->solution_value();
LOG(INFO) << "Advanced usage:";
LOG(INFO) << "Problem solved in " << solver.nodes()
<< " branch-and-bound nodes";
}
void RunAllExamples() {
#if defined(USE_GLPK)
LOG(INFO) << "---- Integer programming example with GLPK ----";
RunIntegerProgrammingExample(MPSolver::GLPK_MIXED_INTEGER_PROGRAMMING);
#endif
void RunAllExamples() {
#if defined(USE_CBC)
LOG(INFO) << "---- Integer programming example with CBC ----";
RunIntegerProgrammingExample(MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
LOG(INFO) << "---- Integer programming example with CBC ----";
RunIntegerProgrammingExample(MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
#endif
#if defined(USE_GLPK)
LOG(INFO) << "---- Integer programming example with GLPK ----";
RunIntegerProgrammingExample(MPSolver::GLPK_MIXED_INTEGER_PROGRAMMING);
#endif
#if defined(USE_SCIP)
LOG(INFO) << "---- Integer programming example with SCIP ----";
RunIntegerProgrammingExample(MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING);
LOG(INFO) << "---- Integer programming example with SCIP ----";
RunIntegerProgrammingExample(MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING);
#endif
#if defined(USE_GUROBI)
LOG(INFO) << "---- Integer programming example with Gurobi ----";
RunIntegerProgrammingExample(MPSolver::GUROBI_MIXED_INTEGER_PROGRAMMING);
LOG(INFO) << "---- Integer programming example with Gurobi ----";
RunIntegerProgrammingExample(MPSolver::GUROBI_MIXED_INTEGER_PROGRAMMING);
#endif // USE_GUROBI
#if defined(USE_CPLEX)
LOG(INFO) << "---- Integer programming example with CPLEX ----";
RunIntegerProgrammingExample(MPSolver::CPLEX_MIXED_INTEGER_PROGRAMMING);
LOG(INFO) << "---- Integer programming example with CPLEX ----";
RunIntegerProgrammingExample(MPSolver::CPLEX_MIXED_INTEGER_PROGRAMMING);
#endif // USE_CPLEX
}
}
} // namespace operations_research
int main(int argc, char** argv) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
google::InitGoogleLogging(argv[0]);
FLAGS_logtostderr = 1;
operations_research::RunAllExamples();
return 0;
}