iteration_stats.h

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// Copyright 2010-2021 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.
 
#ifndef PDLP_ITERATION_STATS_H_
#define PDLP_ITERATION_STATS_H_
 
#include <limits>
#include <string>
#include <vector>
 
#include "Eigen/Core"
#include "absl/types/optional.h"
#include "ortools/pdlp/sharded_quadratic_program.h"
#include "ortools/pdlp/solve_log.pb.h"
#include "ortools/pdlp/solvers.pb.h"
 
namespace operations_research::pdlp {
 
// Returns convergence statistics about a primal/dual solution pair. The stats
// are with respect to sharded_qp (which is typically scaled).
// This function is equivalent to ComputeConvergenceInformation given scaling
// vectors uniformly equal to one.
ConvergenceInformation ComputeScaledConvergenceInformation(
    const ShardedQuadraticProgram& sharded_qp,
    const Eigen::VectorXd& primal_solution,
    const Eigen::VectorXd& dual_solution, PointType candidate_type);
 
// Returns convergence statistics about a primal/dual solution pair. It is
// assumed that scaled_sharded_qp has been transformed from the original qp by
// ShardedQuadraticProgram::RescaleQuadraticProgram(col_scaling_vec,
// row_scaling_vec). scaled_primal_solution and scaled_dual_solution are
// solutions for the scaled problem. The stats are computed with respect to the
// implicit original problem.
// NOTE: This function assumes that scaled_primal_solution satisfies the
// variable bounds and scaled_dual_solution satisfies the dual variable bounds;
// see
// https://developers.google.com/optimization/lp/pdlp_math#dual_variable_bounds.
ConvergenceInformation ComputeConvergenceInformation(
    const ShardedQuadraticProgram& 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);
 
// Returns infeasibility statistics about a primal/dual infeasibility
// certificate estimate. It is assumed that scaled_sharded_qp has been
// transformed from the original qp by
// ShardedQuadraticProgram::RescaleQuadraticProgram(col_scaling_vec,
// row_scaling_vec). primal_ray and dual_ray are certificates for the scaled
// problem. The stats are computed with respect to the implicit original
// problem.
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);
 
// Computes the reduced costs vector, objective_matrix * primal_solution +
// objective_vector - constraint_matrix * dual_solution - dual_residuals, when
// use_zero_primal_objective is false, and -constraint_matrix * dual_solution -
// dual_residuals when use_zero_primal_objective is true. The elements of the
// vector are corrected component-wise to zero to ensure that the dual objective
// takes a finite value. See
// https://developers.google.com/optimization/lp/pdlp_math#reduced_costs_dual_residuals_and_the_corrected_dual_objective.
Eigen::VectorXd ReducedCosts(const ShardedQuadraticProgram& scaled_sharded_qp,
                             const Eigen::VectorXd& primal_solution,
                             const Eigen::VectorXd& dual_solution,
                             bool use_zero_primal_objective = false);
 
// Finds and returns the ConvergenceInformation with the specified
// candidate_type, or absl::nullopt if no such candidate exists.
absl::optional<ConvergenceInformation> GetConvergenceInformation(
    const IterationStats& stats, PointType candidate_type);
 
// Finds and returns the InfeasibilityInformation with the specified
// candidate_type, or absl::nullopt if no such candidate exists.
absl::optional<InfeasibilityInformation> GetInfeasibilityInformation(
    const IterationStats& stats, PointType candidate_type);
 
// Finds and returns the PointMetadata with the specified
// point_type, or absl::nullopt if no such point exists.
absl::optional<PointMetadata> GetPointMetadata(const IterationStats& stats,
                                               PointType point_type);
 
// For each entry in random_projection_seeds, computes a random projection of
// the primal/dual solution pair onto pseudo-random vectors generated from that
// seed and adds the results to
// random_primal_projections/random_dual_projections in metadata.
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);
 
}  // namespace operations_research::pdlp
 
#endif  // PDLP_ITERATION_STATS_H_