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lp_data_utils.cc
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lp_data_utils.cc
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// Copyright 2010-2024 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
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "ortools/lp_data/lp_data_utils.h"
#include "absl/log/check.h"
#include "ortools/glop/parameters.pb.h"
#include "ortools/lp_data/lp_data.h"
#include "ortools/lp_data/lp_types.h"
#include "ortools/lp_data/matrix_scaler.h"
#include "ortools/lp_data/scattered_vector.h"
#include "ortools/lp_data/sparse_column.h"
namespace operations_research {
namespace glop {
void ComputeSlackVariablesValues(const LinearProgram& linear_program,
DenseRow* values) {
DCHECK(values);
DCHECK_EQ(linear_program.num_variables(), values->size());
// If there are no slack variable, we can give up.
if (linear_program.GetFirstSlackVariable() == kInvalidCol) return;
const auto& transposed_matrix = linear_program.GetTransposeSparseMatrix();
for (RowIndex row(0); row < linear_program.num_constraints(); row++) {
const ColIndex slack_variable = linear_program.GetSlackVariable(row);
if (slack_variable == kInvalidCol) continue;
DCHECK_EQ(0.0, linear_program.constraint_lower_bounds()[row]);
DCHECK_EQ(0.0, linear_program.constraint_upper_bounds()[row]);
const RowIndex transposed_slack = ColToRowIndex(slack_variable);
Fractional activation = 0.0;
// Row in the initial matrix (column in the transposed).
const SparseColumn& sparse_row =
transposed_matrix.column(RowToColIndex(row));
for (const auto& entry : sparse_row) {
if (transposed_slack == entry.index()) continue;
activation +=
(*values)[RowToColIndex(entry.index())] * entry.coefficient();
}
(*values)[slack_variable] = -activation;
}
}
// This is separated from the LinearProgram class because of a cyclic dependency
// when scaling as an LP.
void Scale(LinearProgram* lp, SparseMatrixScaler* scaler) {
// Create GlopParameters proto to get default scaling algorithm.
GlopParameters params;
Scale(lp, scaler, params.scaling_method());
}
// This is separated from LinearProgram class because of a cyclic dependency
// when scaling as an LP.
void Scale(LinearProgram* lp, SparseMatrixScaler* scaler,
GlopParameters::ScalingAlgorithm scaling_method) {
scaler->Init(&lp->matrix_);
scaler->Scale(
scaling_method); // Compute R and C, and replace the matrix A by R.A.C
scaler->ScaleRowVector(false,
&lp->objective_coefficients_); // oc = oc.C
scaler->ScaleRowVector(true,
&lp->variable_upper_bounds_); // cl = cl.C^-1
scaler->ScaleRowVector(true,
&lp->variable_lower_bounds_); // cu = cu.C^-1
scaler->ScaleColumnVector(false, &lp->constraint_upper_bounds_); // rl = R.rl
scaler->ScaleColumnVector(false, &lp->constraint_lower_bounds_); // ru = R.ru
lp->transpose_matrix_is_consistent_ = false;
}
void LpScalingHelper::Scale(LinearProgram* lp) { Scale(GlopParameters(), lp); }
void LpScalingHelper::Scale(const GlopParameters& params, LinearProgram* lp) {
scaler_.Clear();
::operations_research::glop::Scale(lp, &scaler_, params.scaling_method());
bound_scaling_factor_ = 1.0 / lp->ScaleBounds();
objective_scaling_factor_ = 1.0 / lp->ScaleObjective(params.cost_scaling());
}
void LpScalingHelper::Clear() {
scaler_.Clear();
bound_scaling_factor_ = 1.0;
objective_scaling_factor_ = 1.0;
}
Fractional LpScalingHelper::VariableScalingFactor(ColIndex col) const {
// During scaling a col was multiplied by ColScalingFactor() and the variable
// bounds divided by it.
return scaler_.ColUnscalingFactor(col) * bound_scaling_factor_;
}
Fractional LpScalingHelper::ScaleVariableValue(ColIndex col,
Fractional value) const {
return value * scaler_.ColUnscalingFactor(col) * bound_scaling_factor_;
}
Fractional LpScalingHelper::ScaleReducedCost(ColIndex col,
Fractional value) const {
// The reduced cost move like the objective and the col scale.
return value / scaler_.ColUnscalingFactor(col) * objective_scaling_factor_;
}
Fractional LpScalingHelper::ScaleDualValue(RowIndex row,
Fractional value) const {
// The dual value move like the objective and the inverse of the row scale.
return value * (scaler_.RowUnscalingFactor(row) * objective_scaling_factor_);
}
Fractional LpScalingHelper::ScaleConstraintActivity(RowIndex row,
Fractional value) const {
// The activity move with the row_scale and the bound_scaling_factor.
return value / scaler_.RowUnscalingFactor(row) * bound_scaling_factor_;
}
Fractional LpScalingHelper::UnscaleVariableValue(ColIndex col,
Fractional value) const {
// Just the opposite of ScaleVariableValue().
return value / (scaler_.ColUnscalingFactor(col) * bound_scaling_factor_);
}
Fractional LpScalingHelper::UnscaleReducedCost(ColIndex col,
Fractional value) const {
// The reduced cost move like the objective and the col scale.
return value * scaler_.ColUnscalingFactor(col) / objective_scaling_factor_;
}
Fractional LpScalingHelper::UnscaleDualValue(RowIndex row,
Fractional value) const {
// The dual value move like the objective and the inverse of the row scale.
return value / (scaler_.RowUnscalingFactor(row) * objective_scaling_factor_);
}
Fractional LpScalingHelper::UnscaleConstraintActivity(RowIndex row,
Fractional value) const {
// The activity move with the row_scale and the bound_scaling_factor.
return value * scaler_.RowUnscalingFactor(row) / bound_scaling_factor_;
}
void LpScalingHelper::UnscaleUnitRowLeftSolve(
ColIndex basis_col, ScatteredRow* left_inverse) const {
const Fractional global_factor = scaler_.ColUnscalingFactor(basis_col);
// We have left_inverse * [RowScale * B * ColScale] = unit_row.
if (left_inverse->non_zeros.empty()) {
const ColIndex num_rows = left_inverse->values.size();
for (ColIndex col(0); col < num_rows; ++col) {
left_inverse->values[col] /=
scaler_.RowUnscalingFactor(ColToRowIndex(col)) * global_factor;
}
} else {
for (const ColIndex col : left_inverse->non_zeros) {
left_inverse->values[col] /=
scaler_.RowUnscalingFactor(ColToRowIndex(col)) * global_factor;
}
}
}
void LpScalingHelper::UnscaleColumnRightSolve(
const RowToColMapping& basis, ColIndex col,
ScatteredColumn* right_inverse) const {
const Fractional global_factor = scaler_.ColScalingFactor(col);
// [RowScale * B * BColScale] * inverse = RowScale * column * ColScale.
// That is B * (BColScale * inverse) = column * ColScale[col].
if (right_inverse->non_zeros.empty()) {
const RowIndex num_rows = right_inverse->values.size();
for (RowIndex row(0); row < num_rows; ++row) {
right_inverse->values[row] /=
scaler_.ColUnscalingFactor(basis[row]) * global_factor;
}
} else {
for (const RowIndex row : right_inverse->non_zeros) {
right_inverse->values[row] /=
scaler_.ColUnscalingFactor(basis[row]) * global_factor;
}
}
}
} // namespace glop
} // namespace operations_research