c34026b101
note: done using ```sh git grep -l "2010-2024 Google" | xargs sed -i 's/2010-2024 Google/2010-2025 Google/' ```
128 lines
5.3 KiB
C++
128 lines
5.3 KiB
C++
// Copyright 2010-2025 Google LLC
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// Utility helpers for manipulating LinearProgram and other types defined in
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// lp_data.
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#ifndef OR_TOOLS_LP_DATA_LP_DATA_UTILS_H_
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#define OR_TOOLS_LP_DATA_LP_DATA_UTILS_H_
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#include "ortools/glop/parameters.pb.h"
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#include "ortools/lp_data/lp_data.h"
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#include "ortools/lp_data/lp_types.h"
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#include "ortools/lp_data/matrix_scaler.h"
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#include "ortools/lp_data/scattered_vector.h"
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namespace operations_research {
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namespace glop {
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// For all constraints in linear_program, if the constraint has a slack
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// variable, change its value in *values so that the constraints itself is
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// satisfied.
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// Note that this obviously won't always imply that the bounds of the slack
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// variable itself will be satisfied.
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// The code assumes (and DCHECKs) that all constraints with a slack variable
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// have their upper and lower bounds both set to 0. This is ensured by
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// LinearProgram::AddSlackVariablesWhereNecessary().
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void ComputeSlackVariablesValues(const LinearProgram& linear_program,
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DenseRow* values);
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// This is separated from LinearProgram class because of a cyclic dependency
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// when scaling as an LP.
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void Scale(LinearProgram* lp, SparseMatrixScaler* scaler,
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GlopParameters::ScalingAlgorithm scaling_method);
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// A convenience method for above providing a default algorithm for callers that
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// don't specify one.
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void Scale(LinearProgram* lp, SparseMatrixScaler* scaler);
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// Class to facilitate the conversion between an original "unscaled" LP problem
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// and its scaled version. It is easy to get the direction wrong, so it make
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// sense to have a single place where all the scaling formulas are kept.
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class LpScalingHelper {
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public:
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// Clear all scaling coefficients.
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void Clear();
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// Scale the given LP.
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void Scale(LinearProgram* lp);
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void Scale(const GlopParameters& params, LinearProgram* lp);
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void ConfigureFromFactors(absl::Span<const double> row_factors,
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absl::Span<const double> col_factors);
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// Transforms value from unscaled domain to the scaled one.
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Fractional ScaleVariableValue(ColIndex col, Fractional value) const;
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Fractional ScaleReducedCost(ColIndex col, Fractional value) const;
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Fractional ScaleDualValue(RowIndex row, Fractional value) const;
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Fractional ScaleConstraintActivity(RowIndex row, Fractional value) const;
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// Transforms corresponding value from the scaled domain to the original one.
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Fractional UnscaleVariableValue(ColIndex col, Fractional value) const;
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Fractional UnscaleReducedCost(ColIndex col, Fractional value) const;
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Fractional UnscaleDualValue(RowIndex row, Fractional value) const;
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Fractional UnscaleLeftSolveValue(RowIndex row, Fractional value) const;
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Fractional UnscaleConstraintActivity(RowIndex row, Fractional value) const;
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// Unscale a row vector v such that v.B = unit_row. When basis_col is the
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// index of the Column that correspond to the unit position in matrix B.
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void UnscaleUnitRowLeftSolve(ColIndex basis_col,
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ScatteredRow* left_inverse) const;
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// Unscale a col vector v such that B.c = matrix_column_col.
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void UnscaleColumnRightSolve(const RowToColMapping& basis, ColIndex col,
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ScatteredColumn* right_inverse) const;
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// A variable value in the original domain must be multiplied by this factor
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// to be in the scaled domain.
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Fractional VariableScalingFactor(ColIndex col) const;
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// Same as VariableScalingFactor() except that ColIndex greater than the
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// number of columns will be interpreted as "slack" variable whose scaling
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// factor depends on the row.
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Fractional VariableScalingFactorWithSlack(ColIndex col) const;
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// Extra scaling function, to scale objective/bounds.
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void AverageCostScaling(DenseRow* objective);
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void ContainOneBoundScaling(DenseRow* upper_bounds, DenseRow* lower_bounds);
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// Visible for testing. All variable/constraint bounds of the original LP
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// where multiplied by this factor. Nothing else changed.
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Fractional BoundsScalingFactor() const { return bound_scaling_factor_; }
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// Visible for testing. All objective coefficients of the original LP where
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// multiplied by this factor. Nothing else changed.
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Fractional ObjectiveScalingFactor() const {
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return objective_scaling_factor_;
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}
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private:
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Fractional RowUnscalingFactor(RowIndex row) const {
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return matrix_is_scaled_ ? row_unscaling_factors_[row] : 1.0;
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}
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Fractional ColUnscalingFactor(ColIndex col) const {
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return matrix_is_scaled_ ? col_unscaling_factors_[col] : 1.0;
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}
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bool matrix_is_scaled_ = false;
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DenseColumn row_unscaling_factors_;
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DenseRow col_unscaling_factors_;
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Fractional bound_scaling_factor_ = 1.0;
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Fractional objective_scaling_factor_ = 1.0;
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};
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} // namespace glop
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} // namespace operations_research
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#endif // OR_TOOLS_LP_DATA_LP_DATA_UTILS_H_
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