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linear_solver.cc
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linear_solver.cc
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// 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
//
// 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/linear_solver/linear_solver.h"
#if !defined(_MSC_VER)
#include <unistd.h>
#endif
#include <cmath>
#include <cstddef>
#include <utility>
#include "absl/strings/str_cat.h"
#include "absl/strings/str_format.h"
#include "absl/synchronization/mutex.h"
#include "ortools/base/accurate_sum.h"
#include "ortools/base/canonical_errors.h"
#include "ortools/base/commandlineflags.h"
#include "ortools/base/integral_types.h"
#include "ortools/base/logging.h"
#include "ortools/base/map_util.h"
#include "ortools/base/status_macros.h"
#include "ortools/base/stl_util.h"
#include "ortools/linear_solver/linear_solver.pb.h"
#include "ortools/linear_solver/model_exporter.h"
#include "ortools/linear_solver/model_validator.h"
#include "ortools/port/file.h"
#include "ortools/util/fp_utils.h"
DEFINE_bool(verify_solution, false,
"Systematically verify the solution when calling Solve()"
", and change the return value of Solve() to ABNORMAL if"
" an error was detected.");
DEFINE_bool(log_verification_errors, true,
"If --verify_solution is set: LOG(ERROR) all errors detected"
" during the verification of the solution.");
DEFINE_bool(linear_solver_enable_verbose_output, false,
"If set, enables verbose output for the solver. Setting this flag"
" is the same as calling MPSolver::EnableOutput().");
DEFINE_bool(mpsolver_bypass_model_validation, false,
"If set, the user-provided Model won't be verified before Solve()."
" Invalid models will typically trigger various error responses"
" from the underlying solvers; sometimes crashes.");
namespace operations_research {
double MPConstraint::GetCoefficient(const MPVariable* const var) const {
DLOG_IF(DFATAL, !interface_->solver_->OwnsVariable(var)) << var;
if (var == nullptr) return 0.0;
return gtl::FindWithDefault(coefficients_, var, 0.0);
}
void MPConstraint::SetCoefficient(const MPVariable* const var, double coeff) {
DLOG_IF(DFATAL, !interface_->solver_->OwnsVariable(var)) << var;
if (var == nullptr) return;
if (coeff == 0.0) {
auto it = coefficients_.find(var);
// If setting a coefficient to 0 when this coefficient did not
// exist or was already 0, do nothing: skip
// interface_->SetCoefficient() and do not store a coefficient in
// the map. Note that if the coefficient being set to 0 did exist
// and was not 0, we do have to keep a 0 in the coefficients_ map,
// because the extraction of the constraint might rely on it,
// depending on the underlying solver.
if (it != coefficients_.end() && it->second != 0.0) {
const double old_value = it->second;
it->second = 0.0;
interface_->SetCoefficient(this, var, 0.0, old_value);
}
return;
}
auto insertion_result = coefficients_.insert(std::make_pair(var, coeff));
const double old_value =
insertion_result.second ? 0.0 : insertion_result.first->second;
insertion_result.first->second = coeff;
interface_->SetCoefficient(this, var, coeff, old_value);
}
void MPConstraint::Clear() {
interface_->ClearConstraint(this);
coefficients_.clear();
}
void MPConstraint::SetBounds(double lb, double ub) {
const bool change = lb != lb_ || ub != ub_;
lb_ = lb;
ub_ = ub;
if (change && interface_->constraint_is_extracted(index_)) {
interface_->SetConstraintBounds(index_, lb_, ub_);
}
}
double MPConstraint::dual_value() const {
if (!interface_->IsContinuous()) {
LOG(DFATAL) << "Dual value only available for continuous problems";
return 0.0;
}
if (!interface_->CheckSolutionIsSynchronizedAndExists()) return 0.0;
return dual_value_;
}
MPSolver::BasisStatus MPConstraint::basis_status() const {
if (!interface_->IsContinuous()) {
LOG(DFATAL) << "Basis status only available for continuous problems";
return MPSolver::FREE;
}
if (!interface_->CheckSolutionIsSynchronizedAndExists()) {
return MPSolver::FREE;
}
// This is done lazily as this method is expected to be rarely used.
return interface_->row_status(index_);
}
bool MPConstraint::ContainsNewVariables() {
const int last_variable_index = interface_->last_variable_index();
for (const auto& entry : coefficients_) {
const int variable_index = entry.first->index();
if (variable_index >= last_variable_index ||
!interface_->variable_is_extracted(variable_index)) {
return true;
}
}
return false;
}
// ----- MPObjective -----
double MPObjective::GetCoefficient(const MPVariable* const var) const {
DLOG_IF(DFATAL, !interface_->solver_->OwnsVariable(var)) << var;
if (var == nullptr) return 0.0;
return gtl::FindWithDefault(coefficients_, var, 0.0);
}
void MPObjective::SetCoefficient(const MPVariable* const var, double coeff) {
DLOG_IF(DFATAL, !interface_->solver_->OwnsVariable(var)) << var;
if (var == nullptr) return;
if (coeff == 0.0) {
auto it = coefficients_.find(var);
// See the discussion on MPConstraint::SetCoefficient() for 0 coefficients,
// the same reasoning applies here.
if (it == coefficients_.end() || it->second == 0.0) return;
it->second = 0.0;
} else {
coefficients_[var] = coeff;
}
interface_->SetObjectiveCoefficient(var, coeff);
}
void MPObjective::SetOffset(double value) {
offset_ = value;
interface_->SetObjectiveOffset(offset_);
}
namespace {
void CheckLinearExpr(const MPSolver& solver, const LinearExpr& linear_expr) {
for (auto var_value_pair : linear_expr.terms()) {
CHECK(solver.OwnsVariable(var_value_pair.first))
<< "Bad MPVariable* in LinearExpr, did you try adding an integer to an "
"MPVariable* directly?";
}
}
} // namespace
void MPObjective::OptimizeLinearExpr(const LinearExpr& linear_expr,
bool is_maximization) {
CheckLinearExpr(*interface_->solver_, linear_expr);
interface_->ClearObjective();
coefficients_.clear();
offset_ = linear_expr.offset();
for (const auto& kv : linear_expr.terms()) {
SetCoefficient(kv.first, kv.second);
}
SetOptimizationDirection(is_maximization);
}
void MPObjective::AddLinearExpr(const LinearExpr& linear_expr) {
CheckLinearExpr(*interface_->solver_, linear_expr);
offset_ += linear_expr.offset();
for (const auto& kv : linear_expr.terms()) {
SetCoefficient(kv.first, GetCoefficient(kv.first) + kv.second);
}
}
void MPObjective::Clear() {
interface_->ClearObjective();
coefficients_.clear();
offset_ = 0.0;
SetMinimization();
}
void MPObjective::SetOptimizationDirection(bool maximize) {
// Note(user): The maximize_ bool would more naturally belong to the
// MPObjective, but it actually has to be a member of MPSolverInterface,
// because some implementations (such as GLPK) need that bool for the
// MPSolverInterface constructor, i.e at a time when the MPObjective is not
// constructed yet (MPSolverInterface is always built before MPObjective
// when a new MPSolver is constructed).
interface_->maximize_ = maximize;
interface_->SetOptimizationDirection(maximize);
}
bool MPObjective::maximization() const { return interface_->maximize_; }
bool MPObjective::minimization() const { return !interface_->maximize_; }
double MPObjective::Value() const {
// Note(user): implementation-wise, the objective value belongs more
// naturally to the MPSolverInterface, since all of its implementations write
// to it directly.
return interface_->objective_value();
}
double MPObjective::BestBound() const {
// Note(user): the best objective bound belongs to the interface for the
// same reasons as the objective value does.
return interface_->best_objective_bound();
}
// ----- MPVariable -----
double MPVariable::solution_value() const {
if (!interface_->CheckSolutionIsSynchronizedAndExists()) return 0.0;
// If the underlying solver supports integer variables, and this is an integer
// variable, we round the solution value (i.e., clients usually expect precise
// integer values for integer variables).
return (integer_ && interface_->IsMIP()) ? round(solution_value_)
: solution_value_;
}
double MPVariable::unrounded_solution_value() const {
if (!interface_->CheckSolutionIsSynchronizedAndExists()) return 0.0;
return solution_value_;
}
double MPVariable::reduced_cost() const {
if (!interface_->IsContinuous()) {
LOG(DFATAL) << "Reduced cost only available for continuous problems";
return 0.0;
}
if (!interface_->CheckSolutionIsSynchronizedAndExists()) return 0.0;
return reduced_cost_;
}
MPSolver::BasisStatus MPVariable::basis_status() const {
if (!interface_->IsContinuous()) {
LOG(DFATAL) << "Basis status only available for continuous problems";
return MPSolver::FREE;
}
if (!interface_->CheckSolutionIsSynchronizedAndExists()) {
return MPSolver::FREE;
}
// This is done lazily as this method is expected to be rarely used.
return interface_->column_status(index_);
}
void MPVariable::SetBounds(double lb, double ub) {
const bool change = lb != lb_ || ub != ub_;
lb_ = lb;
ub_ = ub;
if (change && interface_->variable_is_extracted(index_)) {
interface_->SetVariableBounds(index_, lb_, ub_);
}
}
void MPVariable::SetInteger(bool integer) {
if (integer_ != integer) {
integer_ = integer;
if (interface_->variable_is_extracted(index_)) {
interface_->SetVariableInteger(index_, integer);
}
}
}
void MPVariable::SetBranchingPriority(int priority) {
if (priority == branching_priority_) return;
branching_priority_ = priority;
interface_->BranchingPriorityChangedForVariable(index_);
}
// ----- Interface shortcuts -----
bool MPSolver::IsMIP() const { return interface_->IsMIP(); }
std::string MPSolver::SolverVersion() const {
return interface_->SolverVersion();
}
void* MPSolver::underlying_solver() { return interface_->underlying_solver(); }
// ---- Solver-specific parameters ----
util::Status MPSolver::SetNumThreads(int num_threads) {
if (num_threads < 1) {
return util::InvalidArgumentError("num_threads must be a positive number.");
}
const util::Status status = interface_->SetNumThreads(num_threads);
if (status.ok()) {
num_threads_ = num_threads;
}
return status;
}
bool MPSolver::SetSolverSpecificParametersAsString(
const std::string& parameters) {
solver_specific_parameter_string_ = parameters;
return interface_->SetSolverSpecificParametersAsString(parameters);
}
// ----- Solver -----
#if defined(USE_CLP) || defined(USE_CBC)
extern MPSolverInterface* BuildCLPInterface(MPSolver* const solver);
#endif
#if defined(USE_CBC)
extern MPSolverInterface* BuildCBCInterface(MPSolver* const solver);
#endif
#if defined(USE_GLPK)
extern MPSolverInterface* BuildGLPKInterface(bool mip, MPSolver* const solver);
#endif
extern MPSolverInterface* BuildBopInterface(MPSolver* const solver);
extern MPSolverInterface* BuildGLOPInterface(MPSolver* const solver);
extern MPSolverInterface* BuildSatInterface(MPSolver* const solver);
#if defined(USE_SCIP)
extern MPSolverInterface* BuildSCIPInterface(MPSolver* const solver);
#endif
#if defined(USE_GUROBI)
extern MPSolverInterface* BuildGurobiInterface(bool mip,
MPSolver* const solver);
#endif
#if defined(USE_CPLEX)
extern MPSolverInterface* BuildCplexInterface(bool mip, MPSolver* const solver);
extern MPSolverInterface* BuildGLOPInterface(MPSolver* const solver);
#endif
#if defined(USE_XPRESS)
extern MPSolverInterface* BuildXpressInterface(bool mip,
MPSolver* const solver);
#endif
namespace {
MPSolverInterface* BuildSolverInterface(MPSolver* const solver) {
DCHECK(solver != nullptr);
switch (solver->ProblemType()) {
case MPSolver::BOP_INTEGER_PROGRAMMING:
return BuildBopInterface(solver);
case MPSolver::SAT_INTEGER_PROGRAMMING:
return BuildSatInterface(solver);
case MPSolver::GLOP_LINEAR_PROGRAMMING:
return BuildGLOPInterface(solver);
#if defined(USE_GLPK)
case MPSolver::GLPK_LINEAR_PROGRAMMING:
return BuildGLPKInterface(false, solver);
case MPSolver::GLPK_MIXED_INTEGER_PROGRAMMING:
return BuildGLPKInterface(true, solver);
#endif
#if defined(USE_CLP) || defined(USE_CBC)
case MPSolver::CLP_LINEAR_PROGRAMMING:
return BuildCLPInterface(solver);
#endif
#if defined(USE_CBC)
case MPSolver::CBC_MIXED_INTEGER_PROGRAMMING:
return BuildCBCInterface(solver);
#endif
#if defined(USE_SCIP)
case MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING:
return BuildSCIPInterface(solver);
#endif
#if defined(USE_GUROBI)
case MPSolver::GUROBI_LINEAR_PROGRAMMING:
return BuildGurobiInterface(false, solver);
case MPSolver::GUROBI_MIXED_INTEGER_PROGRAMMING:
return BuildGurobiInterface(true, solver);
#endif
#if defined(USE_CPLEX)
case MPSolver::CPLEX_LINEAR_PROGRAMMING:
return BuildCplexInterface(false, solver);
case MPSolver::CPLEX_MIXED_INTEGER_PROGRAMMING:
return BuildCplexInterface(true, solver);
#endif
#if defined(USE_XPRESS)
case MPSolver::XPRESS_MIXED_INTEGER_PROGRAMMING:
return BuildXpressInterface(true, solver);
case MPSolver::XPRESS_LINEAR_PROGRAMMING:
return BuildXpressInterface(false, solver);
#endif
default:
// TODO(user): Revert to the best *available* interface.
LOG(FATAL) << "Linear solver not recognized.";
}
return nullptr;
}
} // namespace
namespace {
int NumDigits(int n) {
// Number of digits needed to write a non-negative integer in base 10.
// Note(user): max(1, log(0) + 1) == max(1, -inf) == 1.
#if defined(_MSC_VER)
return static_cast<int>(std::max(1.0L, log(1.0L * n) / log(10.0L) + 1.0));
#else
return static_cast<int>(std::max(1.0, log10(static_cast<double>(n)) + 1.0));
#endif
}
} // namespace
MPSolver::MPSolver(const std::string& name,
OptimizationProblemType problem_type)
: name_(name),
problem_type_(problem_type),
construction_time_(absl::Now()) {
interface_.reset(BuildSolverInterface(this));
if (FLAGS_linear_solver_enable_verbose_output) {
EnableOutput();
}
objective_.reset(new MPObjective(interface_.get()));
}
MPSolver::~MPSolver() { Clear(); }
// static
bool MPSolver::SupportsProblemType(OptimizationProblemType problem_type) {
#ifdef USE_CLP
if (problem_type == CLP_LINEAR_PROGRAMMING) return true;
#endif
#ifdef USE_GLPK
if (problem_type == GLPK_LINEAR_PROGRAMMING) return true;
if (problem_type == GLPK_MIXED_INTEGER_PROGRAMMING) return true;
#endif
if (problem_type == BOP_INTEGER_PROGRAMMING) return true;
if (problem_type == SAT_INTEGER_PROGRAMMING) return true;
if (problem_type == GLOP_LINEAR_PROGRAMMING) return true;
#ifdef USE_GUROBI
if (problem_type == GUROBI_LINEAR_PROGRAMMING) return true;
if (problem_type == GUROBI_MIXED_INTEGER_PROGRAMMING) return true;
#endif
#ifdef USE_SCIP
if (problem_type == SCIP_MIXED_INTEGER_PROGRAMMING) return true;
#endif
#ifdef USE_CBC
if (problem_type == CBC_MIXED_INTEGER_PROGRAMMING) return true;
#endif
#ifdef USE_XPRESS
if (problem_type == XPRESS_MIXED_INTEGER_PROGRAMMING) return true;
if (problem_type == XPRESS_LINEAR_PROGRAMMING) return true;
#endif
#ifdef USE_CPLEX
if (problem_type == CPLEX_LINEAR_PROGRAMMING) return true;
if (problem_type == CPLEX_MIXED_INTEGER_PROGRAMMING) return true;
#endif
return false;
}
// TODO(user): post c++ 14, instead use
// std::pair<MPSolver::OptimizationProblemType, const absl::string_view>
// once pair gets a constexpr constructor.
namespace {
struct NamedOptimizationProblemType {
MPSolver::OptimizationProblemType problem_type;
absl::string_view name;
};
} // namespace
#if defined(_MSC_VER)
const
#else
constexpr
#endif
NamedOptimizationProblemType kOptimizationProblemTypeNames[] = {
{MPSolver::GLOP_LINEAR_PROGRAMMING, "glop"},
#if defined(USE_GLPK)
{MPSolver::GLPK_LINEAR_PROGRAMMING, "glpk_lp"},
#endif
#if defined(USE_CLP)
{MPSolver::CLP_LINEAR_PROGRAMMING, "clp"},
#endif
#if defined(USE_GUROBI)
{MPSolver::GUROBI_LINEAR_PROGRAMMING, "gurobi_lp"},
#endif
#if defined(USE_CPLEX)
{MPSolver::CPLEX_LINEAR_PROGRAMMING, "cplex_lp"},
#endif
#if defined(USE_XPRESS)
{MPSolver::XPRESS_LINEAR_PROGRAMMING, "xpress_lp"},
#endif
#if defined(USE_SCIP)
{MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING, "scip"},
#endif
#if defined(USE_CBC)
{MPSolver::CBC_MIXED_INTEGER_PROGRAMMING, "cbc"},
#endif
#if defined(USE_GLPK)
{MPSolver::GLPK_MIXED_INTEGER_PROGRAMMING, "glpk_mip"},
#endif
{MPSolver::BOP_INTEGER_PROGRAMMING, "bop"},
{MPSolver::SAT_INTEGER_PROGRAMMING, "sat"},
#if defined(USE_GUROBI)
{MPSolver::GUROBI_MIXED_INTEGER_PROGRAMMING, "gurobi_mip"},
#endif
#if defined(USE_CPLEX)
{MPSolver::CPLEX_MIXED_INTEGER_PROGRAMMING, "cplex_mip"},
#endif
#if defined(USE_XPRESS)
{MPSolver::XPRESS_MIXED_INTEGER_PROGRAMMING, "xpress_mip"},
#endif
};
// static
bool MPSolver::ParseSolverType(absl::string_view solver,
MPSolver::OptimizationProblemType* type) {
for (const auto& named_solver : kOptimizationProblemTypeNames) {
if (named_solver.name == solver) {
*type = named_solver.problem_type;
return true;
}
}
return false;
}
const absl::string_view ToString(
MPSolver::OptimizationProblemType optimization_problem_type) {
for (const auto& named_solver : kOptimizationProblemTypeNames) {
if (named_solver.problem_type == optimization_problem_type) {
return named_solver.name;
}
}
LOG(FATAL) << "Unrecognized solver type: "
<< static_cast<int>(optimization_problem_type);
return "";
}
bool AbslParseFlag(const absl::string_view text,
MPSolver::OptimizationProblemType* solver_type,
std::string* error) {
DCHECK(solver_type != nullptr);
DCHECK(error != nullptr);
const bool result = MPSolver::ParseSolverType(text, solver_type);
if (!result) {
*error = absl::StrCat("Solver type: ", text, " does not exist.");
}
return result;
}
MPVariable* MPSolver::LookupVariableOrNull(const std::string& var_name) const {
if (!variable_name_to_index_) GenerateVariableNameIndex();
absl::flat_hash_map<std::string, int>::const_iterator it =
variable_name_to_index_->find(var_name);
if (it == variable_name_to_index_->end()) return nullptr;
return variables_[it->second];
}
MPConstraint* MPSolver::LookupConstraintOrNull(
const std::string& constraint_name) const {
if (!constraint_name_to_index_) GenerateConstraintNameIndex();
const auto it = constraint_name_to_index_->find(constraint_name);
if (it == constraint_name_to_index_->end()) return nullptr;
return constraints_[it->second];
}
// ----- Methods using protocol buffers -----
MPSolverResponseStatus MPSolver::LoadModelFromProto(
const MPModelProto& input_model, std::string* error_message) {
// The variable and constraint names are dropped, because we allow
// duplicate names in the proto (they're not considered as 'ids'),
// unlike the MPSolver C++ API which crashes if there are duplicate names.
// Clearing the names makes the MPSolver generate unique names.
return LoadModelFromProtoInternal(input_model, /*clear_names=*/true,
/*check_model_validity=*/true,
error_message);
}
MPSolverResponseStatus MPSolver::LoadModelFromProtoWithUniqueNamesOrDie(
const MPModelProto& input_model, std::string* error_message) {
// Force variable and constraint name indexing (which CHECKs name uniqueness).
GenerateVariableNameIndex();
GenerateConstraintNameIndex();
return LoadModelFromProtoInternal(input_model, /*clear_names=*/false,
/*check_model_validity=*/true,
error_message);
}
MPSolverResponseStatus MPSolver::LoadModelFromProtoInternal(
const MPModelProto& input_model, bool clear_names,
bool check_model_validity, std::string* error_message) {
CHECK(error_message != nullptr);
if (check_model_validity) {
const std::string error = FindErrorInMPModelProto(input_model);
if (!error.empty()) {
*error_message = error;
LOG_IF(INFO, OutputIsEnabled())
<< "Invalid model given to LoadModelFromProto(): " << error;
if (FLAGS_mpsolver_bypass_model_validation) {
LOG_IF(INFO, OutputIsEnabled())
<< "Ignoring the model error(s) because of"
<< " --mpsolver_bypass_model_validation.";
} else {
return error.find("Infeasible") == std::string::npos
? MPSOLVER_MODEL_INVALID
: MPSOLVER_INFEASIBLE;
}
}
}
if (input_model.has_quadratic_objective()) {
*error_message =
"Optimizing a quadratic objective is only supported through direct "
"proto solves. Please use MPSolver::SolveWithProto, or the solver's "
"direct proto solve function.";
return MPSOLVER_MODEL_INVALID;
}
MPObjective* const objective = MutableObjective();
// Passing empty names makes the MPSolver generate unique names.
const std::string empty;
for (int i = 0; i < input_model.variable_size(); ++i) {
const MPVariableProto& var_proto = input_model.variable(i);
MPVariable* variable =
MakeNumVar(var_proto.lower_bound(), var_proto.upper_bound(),
clear_names ? empty : var_proto.name());
variable->SetInteger(var_proto.is_integer());
if (var_proto.branching_priority() != 0) {
variable->SetBranchingPriority(var_proto.branching_priority());
}
objective->SetCoefficient(variable, var_proto.objective_coefficient());
}
for (const MPConstraintProto& ct_proto : input_model.constraint()) {
if (ct_proto.lower_bound() == -infinity() &&
ct_proto.upper_bound() == infinity()) {
continue;
}
MPConstraint* const ct =
MakeRowConstraint(ct_proto.lower_bound(), ct_proto.upper_bound(),
clear_names ? empty : ct_proto.name());
ct->set_is_lazy(ct_proto.is_lazy());
for (int j = 0; j < ct_proto.var_index_size(); ++j) {
ct->SetCoefficient(variables_[ct_proto.var_index(j)],
ct_proto.coefficient(j));
}
}
for (const MPGeneralConstraintProto& general_constraint :
input_model.general_constraint()) {
switch (general_constraint.general_constraint_case()) {
case MPGeneralConstraintProto::kIndicatorConstraint: {
const auto& proto =
general_constraint.indicator_constraint().constraint();
if (proto.lower_bound() == -infinity() &&
proto.upper_bound() == infinity()) {
continue;
}
const int constraint_index = NumConstraints();
MPConstraint* const constraint = new MPConstraint(
constraint_index, proto.lower_bound(), proto.upper_bound(),
clear_names ? "" : proto.name(), interface_.get());
if (constraint_name_to_index_) {
gtl::InsertOrDie(&*constraint_name_to_index_, constraint->name(),
constraint_index);
}
constraints_.push_back(constraint);
constraint_is_extracted_.push_back(false);
constraint->set_is_lazy(proto.is_lazy());
for (int j = 0; j < proto.var_index_size(); ++j) {
constraint->SetCoefficient(variables_[proto.var_index(j)],
proto.coefficient(j));
}
MPVariable* const variable =
variables_[general_constraint.indicator_constraint().var_index()];
constraint->indicator_variable_ = variable;
constraint->indicator_value_ =
general_constraint.indicator_constraint().var_value();
if (!interface_->AddIndicatorConstraint(constraint)) {
*error_message = "Solver doesn't support indicator constraints";
return MPSOLVER_MODEL_INVALID;
}
break;
}
default:
*error_message = absl::StrFormat(
"Optimizing general constraints of type %i is only supported "
"through direct proto solves. Please use MPSolver::SolveWithProto, "
"or the solver's direct proto solve function.",
general_constraint.general_constraint_case());
return MPSOLVER_MODEL_INVALID;
}
}
objective->SetOptimizationDirection(input_model.maximize());
if (input_model.has_objective_offset()) {
objective->SetOffset(input_model.objective_offset());
}
// Stores any hints about where to start the solve.
solution_hint_.clear();
for (int i = 0; i < input_model.solution_hint().var_index_size(); ++i) {
solution_hint_.push_back(
std::make_pair(variables_[input_model.solution_hint().var_index(i)],
input_model.solution_hint().var_value(i)));
}
return MPSOLVER_MODEL_IS_VALID;
}
namespace {
MPSolverResponseStatus ResultStatusToMPSolverResponseStatus(
MPSolver::ResultStatus status) {
switch (status) {
case MPSolver::OPTIMAL:
return MPSOLVER_OPTIMAL;
case MPSolver::FEASIBLE:
return MPSOLVER_FEASIBLE;
case MPSolver::INFEASIBLE:
return MPSOLVER_INFEASIBLE;
case MPSolver::UNBOUNDED:
return MPSOLVER_UNBOUNDED;
case MPSolver::ABNORMAL:
return MPSOLVER_ABNORMAL;
case MPSolver::MODEL_INVALID:
return MPSOLVER_MODEL_INVALID;
case MPSolver::NOT_SOLVED:
return MPSOLVER_NOT_SOLVED;
}
return MPSOLVER_UNKNOWN_STATUS;
}
} // namespace
void MPSolver::FillSolutionResponseProto(MPSolutionResponse* response) const {
CHECK(response != nullptr);
response->Clear();
response->set_status(
ResultStatusToMPSolverResponseStatus(interface_->result_status_));
if (interface_->result_status_ == MPSolver::OPTIMAL ||
interface_->result_status_ == MPSolver::FEASIBLE) {
response->set_objective_value(Objective().Value());
for (int i = 0; i < variables_.size(); ++i) {
response->add_variable_value(variables_[i]->solution_value());
}
if (interface_->IsMIP()) {
response->set_best_objective_bound(interface_->best_objective_bound());
} else {
// Dual values have no meaning in MIP.
for (int j = 0; j < constraints_.size(); ++j) {
response->add_dual_value(constraints_[j]->dual_value());
}
// Reduced cost have no meaning in MIP.
for (int i = 0; i < variables_.size(); ++i) {
response->add_reduced_cost(variables_[i]->reduced_cost());
}
}
}
}
// static
void MPSolver::SolveWithProto(const MPModelRequest& model_request,
MPSolutionResponse* response) {
CHECK(response != nullptr);
MPSolver solver(model_request.model().name(),
static_cast<MPSolver::OptimizationProblemType>(
model_request.solver_type()));
if (model_request.enable_internal_solver_output()) {
solver.EnableOutput();
}
auto optional_response = solver.interface_->DirectlySolveProto(model_request);
if (optional_response) {
*response = std::move(optional_response).value();
return;
}
const absl::optional<LazyMutableCopy<MPModelProto>> optional_model =
ExtractValidMPModelOrPopulateResponseStatus(model_request, response);
if (!optional_model) {
LOG_IF(WARNING, model_request.enable_internal_solver_output())
<< "Failed to extract a valid model from protocol buffer. Status: "
<< ProtoEnumToString<MPSolverResponseStatus>(response->status()) << " ("
<< response->status() << "): " << response->status_str();
return;
}
std::string error_message;
response->set_status(solver.LoadModelFromProtoInternal(
optional_model->get(), /*clear_names=*/true,
/*check_model_validity=*/false, &error_message));
// Even though we don't re-check model validity here, there can be some
// problems found by LoadModelFromProto, eg. unsupported features.
if (response->status() != MPSOLVER_MODEL_IS_VALID) {
response->set_status_str(error_message);
LOG_IF(WARNING, model_request.enable_internal_solver_output())
<< "LoadModelFromProtoInternal() failed even though the model was "
<< "valid! Status: "
<< ProtoEnumToString<MPSolverResponseStatus>(response->status()) << " ("
<< response->status() << "); Error: " << error_message;
return;
}
if (model_request.has_solver_time_limit_seconds()) {
solver.SetTimeLimit(
absl::Seconds(model_request.solver_time_limit_seconds()));
}
solver.SetSolverSpecificParametersAsString(
model_request.solver_specific_parameters());
solver.Solve();
solver.FillSolutionResponseProto(response);
}
void MPSolver::ExportModelToProto(MPModelProto* output_model) const {
DCHECK(output_model != nullptr);
output_model->Clear();
// Name
output_model->set_name(Name());
// Variables
for (int j = 0; j < variables_.size(); ++j) {
const MPVariable* const var = variables_[j];
MPVariableProto* const variable_proto = output_model->add_variable();
// TODO(user): Add option to avoid filling the var name to avoid overly
// large protocol buffers.
variable_proto->set_name(var->name());
variable_proto->set_lower_bound(var->lb());
variable_proto->set_upper_bound(var->ub());
variable_proto->set_is_integer(var->integer());
if (objective_->GetCoefficient(var) != 0.0) {
variable_proto->set_objective_coefficient(
objective_->GetCoefficient(var));
}
if (var->branching_priority() != 0) {
variable_proto->set_branching_priority(var->branching_priority());
}
}
// Map the variables to their indices. This is needed to output the
// variables in the order they were created, which in turn is needed to have
// repeatable results with ExportModelAsLpFormat and ExportModelAsMpsFormat.
// This step is needed as long as the variable indices are given by the
// underlying solver at the time of model extraction.
// TODO(user): remove this step.
absl::flat_hash_map<const MPVariable*, int> var_to_index;
for (int j = 0; j < variables_.size(); ++j) {
var_to_index[variables_[j]] = j;
}
// Constraints
for (int i = 0; i < constraints_.size(); ++i) {
MPConstraint* const constraint = constraints_[i];
MPConstraintProto* constraint_proto;
if (constraint->indicator_variable() != nullptr) {
MPGeneralConstraintProto* const general_constraint_proto =
output_model->add_general_constraint();
general_constraint_proto->set_name(constraint->name());
MPIndicatorConstraint* const indicator_constraint_proto =
general_constraint_proto->mutable_indicator_constraint();
indicator_constraint_proto->set_var_index(
constraint->indicator_variable()->index());
indicator_constraint_proto->set_var_value(constraint->indicator_value());
constraint_proto = indicator_constraint_proto->mutable_constraint();
} else {
constraint_proto = output_model->add_constraint();
}
constraint_proto->set_name(constraint->name());
constraint_proto->set_lower_bound(constraint->lb());
constraint_proto->set_upper_bound(constraint->ub());
constraint_proto->set_is_lazy(constraint->is_lazy());
// Vector linear_term will contain pairs (variable index, coeff), that will
// be sorted by variable index.
std::vector<std::pair<int, double>> linear_term;
for (const auto& entry : constraint->coefficients_) {
const MPVariable* const var = entry.first;
const int var_index = gtl::FindWithDefault(var_to_index, var, -1);
DCHECK_NE(-1, var_index);
const double coeff = entry.second;
linear_term.push_back(std::pair<int, double>(var_index, coeff));
}
// The cost of sort is expected to be low as constraints usually have very
// few terms.
std::sort(linear_term.begin(), linear_term.end());
// Now use linear term.
for (const std::pair<int, double> var_and_coeff : linear_term) {
constraint_proto->add_var_index(var_and_coeff.first);
constraint_proto->add_coefficient(var_and_coeff.second);
}
}
output_model->set_maximize(Objective().maximization());
output_model->set_objective_offset(Objective().offset());
if (!solution_hint_.empty()) {
PartialVariableAssignment* const hint =
output_model->mutable_solution_hint();
for (const auto& var_value_pair : solution_hint_) {
hint->add_var_index(var_value_pair.first->index());
hint->add_var_value(var_value_pair.second);
}
}
}
util::Status MPSolver::LoadSolutionFromProto(const MPSolutionResponse& response,
double tolerance) {
interface_->result_status_ = static_cast<ResultStatus>(response.status());
if (response.status() != MPSOLVER_OPTIMAL &&
response.status() != MPSOLVER_FEASIBLE) {
return util::InvalidArgumentError(absl::StrCat(
"Cannot load a solution unless its status is OPTIMAL or FEASIBLE"
" (status was: ",
ProtoEnumToString<MPSolverResponseStatus>(response.status()), ")"));
}
// Before touching the variables, verify that the solution looks legit:
// each variable of the MPSolver must have its value listed exactly once, and
// each listed solution should correspond to a known variable.
if (response.variable_value_size() != variables_.size()) {
return util::InvalidArgumentError(absl::StrCat(
"Trying to load a solution whose number of variables (",
response.variable_value_size(),
") does not correspond to the Solver's (", variables_.size(), ")"));
}
interface_->ExtractModel();
if (tolerance != infinity()) {
// Look further: verify that the variable values are within the bounds.
double largest_error = 0;
int num_vars_out_of_bounds = 0;
int last_offending_var = -1;
for (int i = 0; i < response.variable_value_size(); ++i) {
const double var_value = response.variable_value(i);
MPVariable* var = variables_[i];
// TODO(user): Use parameter when they become available in this class.
const double lb_error = var->lb() - var_value;
const double ub_error = var_value - var->ub();
if (lb_error > tolerance || ub_error > tolerance) {
++num_vars_out_of_bounds;
largest_error = std::max(largest_error, std::max(lb_error, ub_error));
last_offending_var = i;
}
}
if (num_vars_out_of_bounds > 0) {
return util::InvalidArgumentError(absl::StrCat(
"Loaded a solution whose variables matched the solver's, but ",
num_vars_out_of_bounds, " of ", variables_.size(),
" variables were out of their bounds, by more than the primal"
" tolerance which is: ",
tolerance, ". Max error: ", largest_error, ", last offender var is #",
last_offending_var, ": '", variables_[last_offending_var]->name(),
"'"));
}
}
for (int i = 0; i < response.variable_value_size(); ++i) {
variables_[i]->set_solution_value(response.variable_value(i));
}
// Set the objective value, if is known.
// NOTE(user): We do not verify the objective, even though we could!
if (response.has_objective_value()) {
interface_->objective_value_ = response.objective_value();
}
// Mark the status as SOLUTION_SYNCHRONIZED, so that users may inspect the
// solution normally.
interface_->sync_status_ = MPSolverInterface::SOLUTION_SYNCHRONIZED;
return util::OkStatus();
}
void MPSolver::Clear() {
MutableObjective()->Clear();
gtl::STLDeleteElements(&variables_);
gtl::STLDeleteElements(&constraints_);
variables_.clear();
if (variable_name_to_index_) {
variable_name_to_index_->clear();
}
variable_is_extracted_.clear();
constraints_.clear();
if (constraint_name_to_index_) {
constraint_name_to_index_->clear();
}
constraint_is_extracted_.clear();
interface_->Reset();
solution_hint_.clear();
}
void MPSolver::Reset() { interface_->Reset(); }
bool MPSolver::InterruptSolve() { return interface_->InterruptSolve(); }
void MPSolver::SetStartingLpBasis(