glop_solver.cc

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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.
 
#include "ortools/math_opt/solvers/glop_solver.h"
 
#include <algorithm>
#include <atomic>
#include <cstdint>
#include <functional>
#include <limits>
#include <memory>
#include <string>
#include <utility>
#include <vector>
 
#include "absl/container/flat_hash_map.h"
#include "absl/memory/memory.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/str_join.h"
#include "absl/strings/str_split.h"
#include "absl/strings/string_view.h"
#include "absl/time/clock.h"
#include "absl/time/time.h"
#include "absl/types/span.h"
#include "ortools/base/cleanup.h"
#include "ortools/base/integral_types.h"
#include "ortools/base/logging.h"
#include "ortools/base/map_util.h"
#include "ortools/base/protoutil.h"
#include "ortools/base/status_macros.h"
#include "ortools/base/strong_int.h"
#include "ortools/base/strong_vector.h"
#include "ortools/glop/lp_solver.h"
#include "ortools/glop/parameters.pb.h"
#include "ortools/lp_data/lp_data.h"
#include "ortools/lp_data/lp_types.h"
#include "ortools/math_opt/callback.pb.h"
#include "ortools/math_opt/core/math_opt_proto_utils.h"
#include "ortools/math_opt/core/solve_interrupter.h"
#include "ortools/math_opt/core/solver_interface.h"
#include "ortools/math_opt/core/sparse_vector_view.h"
#include "ortools/math_opt/model.pb.h"
#include "ortools/math_opt/model_parameters.pb.h"
#include "ortools/math_opt/model_update.pb.h"
#include "ortools/math_opt/parameters.pb.h"
#include "ortools/math_opt/result.pb.h"
#include "ortools/math_opt/solution.pb.h"
#include "ortools/math_opt/sparse_containers.pb.h"
#include "ortools/math_opt/validators/callback_validator.h"
#include "ortools/port/proto_utils.h"
#include "ortools/util/time_limit.h"
 
namespace operations_research {
namespace math_opt {
 
namespace {
 
constexpr double kInf = std::numeric_limits<double>::infinity();
 
absl::string_view SafeName(const VariablesProto& variables, int index) {
  if (variables.names().empty()) {
    return {};
  }
  return variables.names(index);
}
 
absl::string_view SafeName(const LinearConstraintsProto& linear_constraints,
                           int index) {
  if (linear_constraints.names().empty()) {
    return {};
  }
  return linear_constraints.names(index);
}
 
absl::StatusOr<TerminationProto> BuildTermination(
    const glop::ProblemStatus status,
    const SolveInterrupter* const interrupter) {
  switch (status) {
    case glop::ProblemStatus::OPTIMAL:
      return TerminateForReason(TERMINATION_REASON_OPTIMAL);
    case glop::ProblemStatus::PRIMAL_INFEASIBLE:
    case glop::ProblemStatus::DUAL_UNBOUNDED:
      return TerminateForReason(TERMINATION_REASON_INFEASIBLE);
    case glop::ProblemStatus::PRIMAL_UNBOUNDED:
      return TerminateForReason(TERMINATION_REASON_UNBOUNDED);
    case glop::ProblemStatus::DUAL_INFEASIBLE:
    case glop::ProblemStatus::INFEASIBLE_OR_UNBOUNDED:
      return TerminateForReason(TERMINATION_REASON_INFEASIBLE_OR_UNBOUNDED);
    case glop::ProblemStatus::INIT:
    case glop::ProblemStatus::DUAL_FEASIBLE:
      // Glop may flip the `interrupt_solve` atomic when it is terminated for a
      // reason other than interruption so we should ignore its value. Instead
      // we use the interrupter.
      // A primal feasible solution is only returned for PRIMAL_FEASIBLE (see
      // comments in FillSolution).
      return NoSolutionFoundTermination(interrupter != nullptr &&
                                                interrupter->IsInterrupted()
                                            ? LIMIT_INTERRUPTED
                                            : LIMIT_UNDETERMINED);
    case glop::ProblemStatus::PRIMAL_FEASIBLE:
      // Glop may flip the `interrupt_solve` atomic when it is terminated for a
      // reason other than interruption so we should ignore its value. Instead
      // we use the interrupter.
      // A primal feasible solution is only returned for PRIMAL_FEASIBLE (see
      // comments in FillSolution).
      return FeasibleTermination(interrupter != nullptr &&
                                         interrupter->IsInterrupted()
                                     ? LIMIT_INTERRUPTED
                                     : LIMIT_UNDETERMINED);
    case glop::ProblemStatus::IMPRECISE:
      return TerminateForReason(TERMINATION_REASON_IMPRECISE);
    case glop::ProblemStatus::ABNORMAL:
    case glop::ProblemStatus::INVALID_PROBLEM:
      return absl::InternalError(
          absl::StrCat("Unexpected GLOP termination reason: ",
                       glop::GetProblemStatusString(status)));
  }
  LOG(FATAL) << "Unimplemented GLOP termination reason: "
             << glop::GetProblemStatusString(status);
}
 
}  // namespace
 
GlopSolver::GlopSolver() : linear_program_(), lp_solver_() {}
 
void GlopSolver::AddVariables(const VariablesProto& variables) {
  for (int i = 0; i < NumVariables(variables); ++i) {
    const glop::ColIndex col_index = linear_program_.CreateNewVariable();
    linear_program_.SetVariableBounds(col_index, variables.lower_bounds(i),
                                      variables.upper_bounds(i));
    linear_program_.SetVariableName(col_index, SafeName(variables, i));
    gtl::InsertOrDie(&variables_, variables.ids(i), col_index);
  }
}
 
// Note that this relies on the fact that when variable/constraint
// are deleted, Glop re-index everything by compacting the
// index domain in a stable way.
template <typename IndexType>
void UpdateIdIndexMap(glop::StrictITIVector<IndexType, bool> indices_to_delete,
 
                      IndexType num_indices,
                      absl::flat_hash_map<int64_t, IndexType>& id_index_map) {
  absl::StrongVector<IndexType, IndexType> new_indices(num_indices.value(),
                                                       IndexType(0));
  IndexType new_index(0);
  for (IndexType index(0); index < num_indices; ++index) {
    if (indices_to_delete[index]) {
      // Mark deleted index
      new_indices[index] = IndexType(-1);
    } else {
      new_indices[index] = new_index;
      ++new_index;
    }
  }
  for (auto it = id_index_map.begin(); it != id_index_map.end();) {
    IndexType index = it->second;
    if (indices_to_delete[index]) {
      // This safely deletes the entry and moves the iterator one step ahead.
      id_index_map.erase(it++);
    } else {
      it->second = new_indices[index];
      ++it;
    }
  }
}
 
void GlopSolver::DeleteVariables(absl::Span<const int64_t> ids_to_delete) {
  const glop::ColIndex num_cols = linear_program_.num_variables();
  glop::StrictITIVector<glop::ColIndex, bool> columns_to_delete(num_cols,
                                                                false);
  for (const int64_t deleted_variable_id : ids_to_delete) {
    columns_to_delete[variables_.at(deleted_variable_id)] = true;
  }
  linear_program_.DeleteColumns(columns_to_delete);
  UpdateIdIndexMap<glop::ColIndex>(columns_to_delete, num_cols, variables_);
}
 
void GlopSolver::DeleteLinearConstraints(
    absl::Span<const int64_t> ids_to_delete) {
  const glop::RowIndex num_rows = linear_program_.num_constraints();
  glop::DenseBooleanColumn rows_to_delete(num_rows, false);
  for (const int64_t deleted_constraint_id : ids_to_delete) {
    rows_to_delete[linear_constraints_.at(deleted_constraint_id)] = true;
  }
  linear_program_.DeleteRows(rows_to_delete);
  UpdateIdIndexMap<glop::RowIndex>(rows_to_delete, num_rows,
                                   linear_constraints_);
}
 
void GlopSolver::AddLinearConstraints(
    const LinearConstraintsProto& linear_constraints) {
  for (int i = 0; i < NumConstraints(linear_constraints); ++i) {
    const glop::RowIndex row_index = linear_program_.CreateNewConstraint();
    linear_program_.SetConstraintBounds(row_index,
                                        linear_constraints.lower_bounds(i),
                                        linear_constraints.upper_bounds(i));
    linear_program_.SetConstraintName(row_index,
                                      SafeName(linear_constraints, i));
    gtl::InsertOrDie(&linear_constraints_, linear_constraints.ids(i),
                     row_index);
  }
}
 
void GlopSolver::SetOrUpdateObjectiveCoefficients(
    const SparseDoubleVectorProto& linear_objective_coefficients) {
  for (int i = 0; i < linear_objective_coefficients.ids_size(); ++i) {
    const glop::ColIndex col_index =
        variables_.at(linear_objective_coefficients.ids(i));
    linear_program_.SetObjectiveCoefficient(
        col_index, linear_objective_coefficients.values(i));
  }
}
 
void GlopSolver::SetOrUpdateConstraintMatrix(
    const SparseDoubleMatrixProto& linear_constraint_matrix) {
  for (int j = 0; j < NumMatrixNonzeros(linear_constraint_matrix); ++j) {
    const glop::ColIndex col_index =
        variables_.at(linear_constraint_matrix.column_ids(j));
    const glop::RowIndex row_index =
        linear_constraints_.at(linear_constraint_matrix.row_ids(j));
    const double coefficient = linear_constraint_matrix.coefficients(j);
    linear_program_.SetCoefficient(row_index, col_index, coefficient);
  }
}
 
void GlopSolver::UpdateVariableBounds(
    const VariableUpdatesProto& variable_updates) {
  for (const auto [id, lb] : MakeView(variable_updates.lower_bounds())) {
    const auto col_index = variables_.at(id);
    linear_program_.SetVariableBounds(
        col_index, lb, linear_program_.variable_upper_bounds()[col_index]);
  }
  for (const auto [id, ub] : MakeView(variable_updates.upper_bounds())) {
    const auto col_index = variables_.at(id);
    linear_program_.SetVariableBounds(
        col_index, linear_program_.variable_lower_bounds()[col_index], ub);
  }
}
 
void GlopSolver::UpdateLinearConstraintBounds(
    const LinearConstraintUpdatesProto& linear_constraint_updates) {
  for (const auto [id, lb] :
       MakeView(linear_constraint_updates.lower_bounds())) {
    const auto row_index = linear_constraints_.at(id);
    linear_program_.SetConstraintBounds(
        row_index, lb, linear_program_.constraint_upper_bounds()[row_index]);
  }
  for (const auto [id, ub] :
       MakeView(linear_constraint_updates.upper_bounds())) {
    const auto row_index = linear_constraints_.at(id);
    linear_program_.SetConstraintBounds(
        row_index, linear_program_.constraint_lower_bounds()[row_index], ub);
  }
}
 
absl::StatusOr<glop::GlopParameters> GlopSolver::MergeSolveParameters(
    const SolveParametersProto& solver_parameters,
    const bool setting_initial_basis, const bool has_message_callback) {
  glop::GlopParameters result = solver_parameters.glop();
  std::vector<std::string> warnings;
  if (!result.has_max_time_in_seconds() && solver_parameters.has_time_limit()) {
    const absl::Duration time_limit =
        util_time::DecodeGoogleApiProto(solver_parameters.time_limit()).value();
    result.set_max_time_in_seconds(absl::ToDoubleSeconds(time_limit));
  }
  if (has_message_callback) {
    // If we have a message callback, we must set log_search_progress to get any
    // logs. We ignore the user's input on specific solver parameters here since
    // it would be confusing to accept a callback but never call it.
    result.set_log_search_progress(true);
 
    // We don't want the logs to be also printed to stdout when we have a
    // message callback. Here we ignore the user input since message callback
    // can be used in the context of a server and printing to stdout could be a
    // problem.
    result.set_log_to_stdout(false);
  } else if (!result.has_log_search_progress()) {
    result.set_log_search_progress(solver_parameters.enable_output());
  }
  if (!result.has_num_omp_threads() && solver_parameters.has_threads()) {
    result.set_num_omp_threads(solver_parameters.threads());
  }
  if (!result.has_random_seed() && solver_parameters.has_random_seed()) {
    const int random_seed = std::max(0, solver_parameters.random_seed());
    result.set_random_seed(random_seed);
  }
  if (!result.has_max_number_of_iterations() &&
      solver_parameters.iteration_limit()) {
    result.set_max_number_of_iterations(solver_parameters.iteration_limit());
  }
  if (solver_parameters.has_node_limit()) {
    warnings.emplace_back("GLOP does snot support 'node_limit' parameter");
  }
  if (!result.has_use_dual_simplex() &&
      solver_parameters.lp_algorithm() != LP_ALGORITHM_UNSPECIFIED) {
    switch (solver_parameters.lp_algorithm()) {
      case LP_ALGORITHM_PRIMAL_SIMPLEX:
        result.set_use_dual_simplex(false);
        break;
      case LP_ALGORITHM_DUAL_SIMPLEX:
        result.set_use_dual_simplex(true);
        break;
      case LP_ALGORITHM_BARRIER:
        warnings.emplace_back(
            "GLOP does not support 'LP_ALGORITHM_BARRIER' value for "
            "'lp_algorithm' parameter.");
        break;
      default:
        LOG(FATAL) << "LPAlgorithm: "
                   << ProtoEnumToString(solver_parameters.lp_algorithm())
                   << " unknown, error setting GLOP parameters";
    }
  }
  if (!result.has_use_scaling() && !result.has_scaling_method() &&
      solver_parameters.scaling() != EMPHASIS_UNSPECIFIED) {
    switch (solver_parameters.scaling()) {
      case EMPHASIS_OFF:
        result.set_use_scaling(false);
        break;
      case EMPHASIS_LOW:
      case EMPHASIS_MEDIUM:
        result.set_use_scaling(true);
        result.set_scaling_method(glop::GlopParameters::EQUILIBRATION);
        break;
      case EMPHASIS_HIGH:
      case EMPHASIS_VERY_HIGH:
        result.set_use_scaling(true);
        result.set_scaling_method(glop::GlopParameters::LINEAR_PROGRAM);
        break;
      default:
        LOG(FATAL) << "Scaling emphasis: "
                   << ProtoEnumToString(solver_parameters.scaling())
                   << " unknown, error setting GLOP parameters";
    }
  }
  if (setting_initial_basis) {
    result.set_use_preprocessing(false);
  } else if (!result.has_use_preprocessing() &&
             solver_parameters.presolve() != EMPHASIS_UNSPECIFIED) {
    switch (solver_parameters.presolve()) {
      case EMPHASIS_OFF:
        result.set_use_preprocessing(false);
        break;
      case EMPHASIS_LOW:
      case EMPHASIS_MEDIUM:
      case EMPHASIS_HIGH:
      case EMPHASIS_VERY_HIGH:
        result.set_use_preprocessing(true);
        break;
      default:
        LOG(FATAL) << "Presolve emphasis: "
                   << ProtoEnumToString(solver_parameters.presolve())
                   << " unknown, error setting GLOP parameters";
    }
  }
  if (solver_parameters.cuts() != EMPHASIS_UNSPECIFIED) {
    warnings.push_back(absl::StrCat(
        "GLOP does not support 'cuts' parameters, but cuts was set to: ",
        ProtoEnumToString(solver_parameters.cuts())));
  }
  if (solver_parameters.heuristics() != EMPHASIS_UNSPECIFIED) {
    warnings.push_back(
        absl::StrCat("GLOP does not support 'heuristics' parameter, but "
                     "heuristics was set to: ",
                     ProtoEnumToString(solver_parameters.heuristics())));
  }
  if (solver_parameters.has_cutoff_limit()) {
    warnings.push_back("GLOP does not support 'cutoff_limit' parameter");
  }
  if (solver_parameters.has_objective_limit()) {
    warnings.push_back("GLOP does not support 'objective_limit' parameter");
  }
  if (solver_parameters.has_best_bound_limit()) {
    warnings.push_back("GLOP does not support 'best_bound_limit' parameter");
  }
  if (solver_parameters.has_solution_limit()) {
    warnings.push_back("GLOP does not support 'solution_limit' parameter");
  }
  if (!warnings.empty()) {
    return absl::InvalidArgumentError(absl::StrJoin(warnings, "; "));
  }
  return result;
}
 
bool GlopSolver::CanUpdate(const ModelUpdateProto& model_update) {
  return model_update.objective_updates()
      .quadratic_coefficients()
      .row_ids()
      .empty();
}
 
template <typename IndexType>
SparseDoubleVectorProto FillSparseDoubleVector(
    const std::vector<int64_t>& ids_in_order,
    const absl::flat_hash_map<int64_t, IndexType>& id_map,
    const glop::StrictITIVector<IndexType, glop::Fractional>& values,
    const SparseVectorFilterProto& filter) {
  SparseVectorFilterPredicate predicate(filter);
  SparseDoubleVectorProto result;
  for (const int64_t variable_id : ids_in_order) {
    const double value = values[id_map.at(variable_id)];
    if (predicate.AcceptsAndUpdate(variable_id, value)) {
      result.add_ids(variable_id);
      result.add_values(value);
    }
  }
  return result;
}
 
// ValueType should be glop's VariableStatus or ConstraintStatus.
template <typename ValueType>
BasisStatusProto FromGlopBasisStatus(const ValueType glop_basis_status) {
  switch (glop_basis_status) {
    case ValueType::BASIC:
      return BasisStatusProto::BASIS_STATUS_BASIC;
    case ValueType::FIXED_VALUE:
      return BasisStatusProto::BASIS_STATUS_FIXED_VALUE;
    case ValueType::AT_LOWER_BOUND:
      return BasisStatusProto::BASIS_STATUS_AT_LOWER_BOUND;
    case ValueType::AT_UPPER_BOUND:
      return BasisStatusProto::BASIS_STATUS_AT_UPPER_BOUND;
    case ValueType::FREE:
      return BasisStatusProto::BASIS_STATUS_FREE;
  }
  return BasisStatusProto::BASIS_STATUS_UNSPECIFIED;
}
 
template <typename IndexType, typename ValueType>
SparseBasisStatusVector FillSparseBasisStatusVector(
    const std::vector<int64_t>& ids_in_order,
    const absl::flat_hash_map<int64_t, IndexType>& id_map,
    const glop::StrictITIVector<IndexType, ValueType>& values) {
  SparseBasisStatusVector result;
  for (const int64_t variable_id : ids_in_order) {
    const ValueType value = values[id_map.at(variable_id)];
    result.add_ids(variable_id);
    result.add_values(FromGlopBasisStatus(value));
  }
  return result;
}
 
// ValueType should be glop's VariableStatus or ConstraintStatus.
template <typename ValueType>
ValueType ToGlopBasisStatus(const BasisStatusProto basis_status) {
  switch (basis_status) {
    case BASIS_STATUS_BASIC:
      return ValueType::BASIC;
    case BASIS_STATUS_FIXED_VALUE:
      return ValueType::FIXED_VALUE;
    case BASIS_STATUS_AT_LOWER_BOUND:
      return ValueType::AT_LOWER_BOUND;
    case BASIS_STATUS_AT_UPPER_BOUND:
      return ValueType::AT_UPPER_BOUND;
    case BASIS_STATUS_FREE:
      return ValueType::FREE;
    default:
      LOG(FATAL) << "Unexpected invalid initial_basis.";
      return ValueType::FREE;
  }
}
 
template <typename T>
std::vector<int64_t> GetSortedIs(
    const absl::flat_hash_map<int64_t, T>& id_map) {
  std::vector<int64_t> sorted;
  sorted.reserve(id_map.size());
  for (const auto& entry : id_map) {
    sorted.emplace_back(entry.first);
  }
  std::sort(sorted.begin(), sorted.end());
  return sorted;
}
 
// Returns a vector of containing the MathOpt id of each row or column. Here T
// is either (Col|Row)Index and id_map is expected to be
// GlopSolver::(linear_constraints_|variables_).
template <typename T>
glop::StrictITIVector<T, int64_t> IndexToId(
    const absl::flat_hash_map<int64_t, T>& id_map) {
  // Guard value used to identify not-yet-set elements of index_to_id.
  constexpr int64_t kEmptyId = -1;
  glop::StrictITIVector<T, int64_t> index_to_id(T(id_map.size()), kEmptyId);
  for (const auto& [id, index] : id_map) {
    CHECK(index >= 0 && index < index_to_id.size()) << index;
    CHECK_EQ(index_to_id[index], kEmptyId);
    index_to_id[index] = id;
  }
 
  // At this point, index_to_id can't contain any kEmptyId values since
  // index_to_id.size() == id_map.size() and we modified id_map.size() elements
  // in the loop, after checking that the modified element was changed by a
  // previous iteration.
  return index_to_id;
}
 
InvertedBounds GlopSolver::ListInvertedBounds() const {
  // Identify rows and columns by index first.
  std::vector<glop::ColIndex> inverted_columns;
  const glop::ColIndex num_cols = linear_program_.num_variables();
  for (glop::ColIndex col(0); col < num_cols; ++col) {
    if (linear_program_.variable_lower_bounds()[col] >
        linear_program_.variable_upper_bounds()[col]) {
      inverted_columns.push_back(col);
    }
  }
  std::vector<glop::RowIndex> inverted_rows;
  const glop::RowIndex num_rows = linear_program_.num_constraints();
  for (glop::RowIndex row(0); row < num_rows; ++row) {
    if (linear_program_.constraint_lower_bounds()[row] >
        linear_program_.constraint_upper_bounds()[row]) {
      inverted_rows.push_back(row);
    }
  }
 
  // Convert column/row indices into MathOpt ids. We avoid calling the expensive
  // IndexToId() when not necessary.
  InvertedBounds inverted_bounds;
  if (!inverted_columns.empty()) {
    const glop::StrictITIVector<glop::ColIndex, int64_t> ids =
        IndexToId(variables_);
    CHECK_EQ(ids.size(), num_cols);
    inverted_bounds.variables.reserve(inverted_columns.size());
    for (const glop::ColIndex col : inverted_columns) {
      inverted_bounds.variables.push_back(ids[col]);
    }
  }
  if (!inverted_rows.empty()) {
    const glop::StrictITIVector<glop::RowIndex, int64_t> ids =
        IndexToId(linear_constraints_);
    CHECK_EQ(ids.size(), num_rows);
    inverted_bounds.linear_constraints.reserve(inverted_rows.size());
    for (const glop::RowIndex row : inverted_rows) {
      inverted_bounds.linear_constraints.push_back(ids[row]);
    }
  }
 
  return inverted_bounds;
}
 
void GlopSolver::FillSolution(const glop::ProblemStatus status,
                              const ModelSolveParametersProto& model_parameters,
                              SolveResultProto& solve_result) {
  // Meaningfull solutions are available if optimality is proven in
  // preprocessing or after 1 simplex iteration.
  // TODO(b/195295177): Discuss what to do with glop::ProblemStatus::IMPRECISE
  // looks like it may be set also when rays are imprecise.
  const bool phase_I_solution_available =
      (status == glop::ProblemStatus::INIT) &&
      (lp_solver_.GetNumberOfSimplexIterations() > 0);
  if (status != glop::ProblemStatus::OPTIMAL &&
      status != glop::ProblemStatus::PRIMAL_FEASIBLE &&
      status != glop::ProblemStatus::DUAL_FEASIBLE &&
      status != glop::ProblemStatus::PRIMAL_UNBOUNDED &&
      status != glop::ProblemStatus::DUAL_UNBOUNDED &&
      !phase_I_solution_available) {
    return;
  }
  auto sorted_variables = GetSortedIs(variables_);
  auto sorted_constraints = GetSortedIs(linear_constraints_);
  SolutionProto* const solution = solve_result.add_solutions();
  BasisProto* const basis = solution->mutable_basis();
  PrimalSolutionProto* const primal_solution =
      solution->mutable_primal_solution();
  DualSolutionProto* const dual_solution = solution->mutable_dual_solution();
 
  // Fill in feasibility statuses
  // Note: if we reach here and status != OPTIMAL, then at least 1 simplex
  // iteration has been executed.
  if (status == glop::ProblemStatus::OPTIMAL) {
    primal_solution->set_feasibility_status(SOLUTION_STATUS_FEASIBLE);
    basis->set_basic_dual_feasibility(SOLUTION_STATUS_FEASIBLE);
    dual_solution->set_feasibility_status(SOLUTION_STATUS_FEASIBLE);
  } else if (status == glop::ProblemStatus::PRIMAL_FEASIBLE) {
    // Solve reached phase II of primal simplex and current basis is not
    // optimal. Hence basis is primal feasible, but cannot be dual feasible.
    // Dual solution could still be feasible as noted in
    // go/mathopt-basis-advanced#dualfeasibility
    primal_solution->set_feasibility_status(SOLUTION_STATUS_FEASIBLE);
    dual_solution->set_feasibility_status(SOLUTION_STATUS_UNDETERMINED);
    basis->set_basic_dual_feasibility(SOLUTION_STATUS_INFEASIBLE);
  } else if (status == glop::ProblemStatus::DUAL_FEASIBLE) {
    // Solve reached phase II of dual simplex and current basis is not optimal.
    // Hence basis is dual feasible, but cannot be primal feasible. In addition,
    // glop applies dual feasibility correction in dual simplex so feasibility
    // of the dual solution matches dual feasibility of the basis (i.e the issue
    // described in go/mathopt-basis-advanced#dualfeasibility cannot happen).
    // TODO(b/195295177): confirm with fdid
    primal_solution->set_feasibility_status(SOLUTION_STATUS_INFEASIBLE);
    dual_solution->set_feasibility_status(SOLUTION_STATUS_FEASIBLE);
    basis->set_basic_dual_feasibility(SOLUTION_STATUS_FEASIBLE);
  } else {  // status == INIT
    // Phase I of primal or dual simplex ran for at least one iteration
    if (lp_solver_.GetParameters().use_dual_simplex()) {
      // Phase I did not finish so basis is not dual feasible. In addition,
      // glop applies dual feasibility correction so feasibility of the dual
      // solution matches dual feasibility of the basis (i.e the issue described
      // in go/mathopt-basis-advanced#dualfeasibility cannot happen).
      // TODO(b/195295177): confirm with fdid
      primal_solution->set_feasibility_status(SOLUTION_STATUS_UNDETERMINED);
      dual_solution->set_feasibility_status(SOLUTION_STATUS_INFEASIBLE);
      basis->set_basic_dual_feasibility(SOLUTION_STATUS_INFEASIBLE);
    } else {
      // Phase I did not finish so basis is not primal feasible.
      primal_solution->set_feasibility_status(SOLUTION_STATUS_INFEASIBLE);
      dual_solution->set_feasibility_status(SOLUTION_STATUS_UNDETERMINED);
      basis->set_basic_dual_feasibility(SOLUTION_STATUS_UNDETERMINED);
    }
  }
 
  // Fill in objective values
  primal_solution->set_objective_value(lp_solver_.GetObjectiveValue());
  if (basis->basic_dual_feasibility() == SOLUTION_STATUS_FEASIBLE) {
    // Primal and dual objectives are the same for a dual feasible basis
    // see go/mathopt-basis-advanced#cs-obj-dual-feasible-dual-feasible-basis
    dual_solution->set_objective_value(primal_solution->objective_value());
  }
 
  // Fill solution and basis
  *basis->mutable_constraint_status() = *basis->mutable_variable_status() =
      FillSparseBasisStatusVector(sorted_variables, variables_,
                                  lp_solver_.variable_statuses());
  *basis->mutable_constraint_status() =
      FillSparseBasisStatusVector(sorted_constraints, linear_constraints_,
                                  lp_solver_.constraint_statuses());
 
  *primal_solution->mutable_variable_values() = FillSparseDoubleVector(
      sorted_variables, variables_, lp_solver_.variable_values(),
      model_parameters.variable_values_filter());
 
  *dual_solution->mutable_dual_values() = FillSparseDoubleVector(
      sorted_constraints, linear_constraints_, lp_solver_.dual_values(),
      model_parameters.dual_values_filter());
  *dual_solution->mutable_reduced_costs() = FillSparseDoubleVector(
      sorted_variables, variables_, lp_solver_.reduced_costs(),
      model_parameters.reduced_costs_filter());
 
  if (!lp_solver_.primal_ray().empty()) {
    PrimalRayProto* const primal_ray = solve_result.add_primal_rays();
 
    *primal_ray->mutable_variable_values() = FillSparseDoubleVector(
        sorted_variables, variables_, lp_solver_.primal_ray(),
        model_parameters.variable_values_filter());
  }
  if (!lp_solver_.constraints_dual_ray().empty() &&
      !lp_solver_.variable_bounds_dual_ray().empty()) {
    DualRayProto* const dual_ray = solve_result.add_dual_rays();
    *dual_ray->mutable_dual_values() =
        FillSparseDoubleVector(sorted_constraints, linear_constraints_,
                               lp_solver_.constraints_dual_ray(),
                               model_parameters.dual_values_filter());
    *dual_ray->mutable_reduced_costs() = FillSparseDoubleVector(
        sorted_variables, variables_, lp_solver_.variable_bounds_dual_ray(),
        model_parameters.reduced_costs_filter());
  }
}
 
absl::Status GlopSolver::FillSolveStats(const glop::ProblemStatus status,
                                        const absl::Duration solve_time,
                                        SolveStatsProto& solve_stats) {
  const bool is_maximize = linear_program_.IsMaximizationProblem();
 
  // Set default status and bounds.
  solve_stats.mutable_problem_status()->set_primal_status(
      FEASIBILITY_STATUS_UNDETERMINED);
  solve_stats.set_best_primal_bound(is_maximize ? -kInf : kInf);
  solve_stats.mutable_problem_status()->set_dual_status(
      FEASIBILITY_STATUS_UNDETERMINED);
  solve_stats.set_best_dual_bound(is_maximize ? kInf : -kInf);
 
  // Update status and bounds as appropriate.
  switch (status) {
    case glop::ProblemStatus::OPTIMAL:
      solve_stats.mutable_problem_status()->set_primal_status(
          FEASIBILITY_STATUS_FEASIBLE);
      solve_stats.mutable_problem_status()->set_dual_status(
          FEASIBILITY_STATUS_FEASIBLE);
      solve_stats.set_best_primal_bound(lp_solver_.GetObjectiveValue());
      solve_stats.set_best_dual_bound(lp_solver_.GetObjectiveValue());
      break;
    case glop::ProblemStatus::PRIMAL_INFEASIBLE:
      solve_stats.mutable_problem_status()->set_primal_status(
          FEASIBILITY_STATUS_INFEASIBLE);
      break;
    case glop::ProblemStatus::DUAL_UNBOUNDED:
      solve_stats.mutable_problem_status()->set_primal_status(
          FEASIBILITY_STATUS_INFEASIBLE);
      solve_stats.mutable_problem_status()->set_dual_status(
          FEASIBILITY_STATUS_FEASIBLE);
      solve_stats.set_best_dual_bound(is_maximize ? -kInf : kInf);
      break;
    case glop::ProblemStatus::PRIMAL_UNBOUNDED:
      solve_stats.mutable_problem_status()->set_primal_status(
          FEASIBILITY_STATUS_FEASIBLE);
      solve_stats.mutable_problem_status()->set_dual_status(
          FEASIBILITY_STATUS_INFEASIBLE);
      solve_stats.set_best_primal_bound(is_maximize ? kInf : -kInf);
      break;
    case glop::ProblemStatus::DUAL_INFEASIBLE:
      solve_stats.mutable_problem_status()->set_dual_status(
          FEASIBILITY_STATUS_INFEASIBLE);
      break;
    case glop::ProblemStatus::INFEASIBLE_OR_UNBOUNDED:
      solve_stats.mutable_problem_status()->set_primal_or_dual_infeasible(true);
      break;
    case glop::ProblemStatus::PRIMAL_FEASIBLE:
      solve_stats.mutable_problem_status()->set_primal_status(
          FEASIBILITY_STATUS_FEASIBLE);
      solve_stats.set_best_primal_bound(lp_solver_.GetObjectiveValue());
      break;
    case glop::ProblemStatus::DUAL_FEASIBLE:
      solve_stats.mutable_problem_status()->set_dual_status(
          FEASIBILITY_STATUS_FEASIBLE);
      solve_stats.set_best_dual_bound(lp_solver_.GetObjectiveValue());
      break;
    case glop::ProblemStatus::INIT:
    case glop::ProblemStatus::IMPRECISE:
      // TODO(b/195295177): Discuss what to do with
      // glop::ProblemStatus::IMPRECISE
      break;
    case glop::ProblemStatus::ABNORMAL:
    case glop::ProblemStatus::INVALID_PROBLEM:
      return absl::InternalError(
          absl::StrCat("Unexpected GLOP termination reason: ",
                       glop::GetProblemStatusString(status)));
  }
 
  // Fill remaining stats
  solve_stats.set_simplex_iterations(lp_solver_.GetNumberOfSimplexIterations());
  RETURN_IF_ERROR(util_time::EncodeGoogleApiProto(
      solve_time, solve_stats.mutable_solve_time()));
 
  return absl::OkStatus();
}
 
absl::StatusOr<SolveResultProto> GlopSolver::MakeSolveResult(
    const glop::ProblemStatus status,
    const ModelSolveParametersProto& model_parameters,
    const SolveInterrupter* const interrupter,
    const absl::Duration solve_time) {
  SolveResultProto solve_result;
  ASSIGN_OR_RETURN(*solve_result.mutable_termination(),
                   BuildTermination(status, interrupter));
  FillSolution(status, model_parameters, solve_result);
  RETURN_IF_ERROR(
      FillSolveStats(status, solve_time, *solve_result.mutable_solve_stats()));
  return solve_result;
}
 
void GlopSolver::SetGlopBasis(const BasisProto& basis) {
  glop::VariableStatusRow variable_statuses(linear_program_.num_variables());
  for (const auto [id, value] : MakeView(basis.variable_status())) {
    variable_statuses[variables_.at(id)] =
        ToGlopBasisStatus<glop::VariableStatus>(
            static_cast<BasisStatusProto>(value));
  }
  glop::ConstraintStatusColumn constraint_statuses(
      linear_program_.num_constraints());
  for (const auto [id, value] : MakeView(basis.constraint_status())) {
    constraint_statuses[linear_constraints_.at(id)] =
        ToGlopBasisStatus<glop::ConstraintStatus>(
            static_cast<BasisStatusProto>(value));
  }
  lp_solver_.SetInitialBasis(variable_statuses, constraint_statuses);
}
 
absl::StatusOr<SolveResultProto> GlopSolver::Solve(
    const SolveParametersProto& parameters,
    const ModelSolveParametersProto& model_parameters,
    const MessageCallback message_cb,
    const CallbackRegistrationProto& callback_registration, const Callback cb,
    SolveInterrupter* const interrupter) {
  RETURN_IF_ERROR(CheckRegisteredCallbackEvents(callback_registration,
                                                /*supported_events=*/{}));
 
  const absl::Time start = absl::Now();
  ASSIGN_OR_RETURN(
      const glop::GlopParameters glop_parameters,
      MergeSolveParameters(
          parameters,
          /*setting_initial_basis=*/model_parameters.has_initial_basis(),
          /*has_message_callback=*/message_cb != nullptr));
  lp_solver_.SetParameters(glop_parameters);
 
  if (model_parameters.has_initial_basis()) {
    SetGlopBasis(model_parameters.initial_basis());
  }
 
  std::atomic<bool> interrupt_solve = false;
  const std::unique_ptr<TimeLimit> time_limit =
      TimeLimit::FromParameters(lp_solver_.GetParameters());
  time_limit->RegisterExternalBooleanAsLimit(&interrupt_solve);
 
  const ScopedSolveInterrupterCallback scoped_interrupt_cb(interrupter, [&]() {
    CHECK_NE(interrupter, nullptr);
    interrupt_solve = true;
  });
 
  if (message_cb != nullptr) {
    // Please note that the logging is enabled in MergeSolveParameters() where
    // we also disable logging to stdout. We can't modify the SolverLogger here
    // since the values are overwritten from the parameters at the beginning of
    // the solve.
    //
    // Here we test that there are no other callbacks since we will clear them
    // all in the cleanup below.
    CHECK_EQ(lp_solver_.GetSolverLogger().NumInfoLoggingCallbacks(), 0);
    lp_solver_.GetSolverLogger().AddInfoLoggingCallback(
        [&](const std::string& message) {
          message_cb(absl::StrSplit(message, '\n'));
        });
  }
  const auto message_cb_cleanup = absl::MakeCleanup([&]() {
    if (message_cb != nullptr) {
      // Check that no other callbacks have been added to the logger.
      CHECK_EQ(lp_solver_.GetSolverLogger().NumInfoLoggingCallbacks(), 1);
      lp_solver_.GetSolverLogger().ClearInfoLoggingCallbacks();
    }
  });
 
  // Glop returns an error when bounds are inverted and does not list the
  // offending variables/constraints. Here we want to return a more detailed
  // status.
  RETURN_IF_ERROR(ListInvertedBounds().ToStatus());
 
  const glop::ProblemStatus status =
      lp_solver_.SolveWithTimeLimit(linear_program_, time_limit.get());
  const absl::Duration solve_time = absl::Now() - start;
  return MakeSolveResult(status, model_parameters, interrupter, solve_time);
}
 
absl::StatusOr<std::unique_ptr<SolverInterface>> GlopSolver::New(
    const ModelProto& model, const InitArgs& init_args) {
  if (!model.objective().quadratic_coefficients().row_ids().empty()) {
    return absl::InvalidArgumentError(
        "Glop does not support quadratic objectives");
  }
  auto solver = absl::WrapUnique(new GlopSolver);
  // By default Glop CHECKs that bounds are always consistent (lb < ub); thus it
  // would fail if the initial model or later updates temporarily set inverted
  // bounds.
  solver->linear_program_.SetDcheckBounds(false);
 
  solver->linear_program_.SetName(model.name());
  solver->linear_program_.SetMaximizationProblem(model.objective().maximize());
  solver->linear_program_.SetObjectiveOffset(model.objective().offset());
 
  solver->AddVariables(model.variables());
  solver->SetOrUpdateObjectiveCoefficients(
      model.objective().linear_coefficients());
 
  solver->AddLinearConstraints(model.linear_constraints());
  solver->SetOrUpdateConstraintMatrix(model.linear_constraint_matrix());
  solver->linear_program_.CleanUp();
  return solver;
}
 
absl::Status GlopSolver::Update(const ModelUpdateProto& model_update) {
  if (model_update.objective_updates().has_direction_update()) {
    linear_program_.SetMaximizationProblem(
        model_update.objective_updates().direction_update());
  }
  if (model_update.objective_updates().has_offset_update()) {
    linear_program_.SetObjectiveOffset(
        model_update.objective_updates().offset_update());
  }
 
  DeleteVariables(model_update.deleted_variable_ids());
  AddVariables(model_update.new_variables());
 
  SetOrUpdateObjectiveCoefficients(
      model_update.objective_updates().linear_coefficients());
  UpdateVariableBounds(model_update.variable_updates());
 
  DeleteLinearConstraints(model_update.deleted_linear_constraint_ids());
  AddLinearConstraints(model_update.new_linear_constraints());
  UpdateLinearConstraintBounds(model_update.linear_constraint_updates());
 
  SetOrUpdateConstraintMatrix(model_update.linear_constraint_matrix_updates());
 
  linear_program_.CleanUp();
 
  return absl::OkStatus();
}
 
MATH_OPT_REGISTER_SOLVER(SOLVER_TYPE_GLOP, GlopSolver::New)
 
}  // namespace math_opt
}  // namespace operations_research