solution.h

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// Copyright 2010-2025 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.
 
// IWYU pragma: private, include "ortools/math_opt/cpp/math_opt.h"
// IWYU pragma: friend "ortools/math_opt/cpp/.*"
 
#ifndef OR_TOOLS_MATH_OPT_CPP_SOLUTION_H_
#define OR_TOOLS_MATH_OPT_CPP_SOLUTION_H_
 
#include <optional>
 
#include "absl/container/flat_hash_map.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "ortools/math_opt/constraints/quadratic/quadratic_constraint.h"
#include "ortools/math_opt/cpp/basis_status.h"
#include "ortools/math_opt/cpp/enums.h"  // IWYU pragma: export
#include "ortools/math_opt/cpp/linear_constraint.h"
#include "ortools/math_opt/cpp/objective.h"
#include "ortools/math_opt/cpp/variable_and_expressions.h"
#include "ortools/math_opt/result.pb.h"  // IWYU pragma: export
#include "ortools/math_opt/solution.pb.h"
#include "ortools/math_opt/storage/model_storage.h"
 
namespace operations_research {
namespace math_opt {
 
// Feasibility of a primal or dual solution as claimed by the solver.
enum class SolutionStatus {
  // Solver does not claim a feasibility status.
  kUndetermined = SOLUTION_STATUS_UNDETERMINED,
 
  // Solver claims the solution is feasible.
  kFeasible = SOLUTION_STATUS_FEASIBLE,
 
  // Solver claims the solution is infeasible.
  kInfeasible = SOLUTION_STATUS_INFEASIBLE,
};
 
MATH_OPT_DEFINE_ENUM(SolutionStatus, SOLUTION_STATUS_UNSPECIFIED);
 
// A solution to an optimization problem.
//
// E.g. consider a simple linear program:
//   min c * x
//   s.t. A * x >= b
//   x >= 0.
// A primal solution is assignment values to x. It is feasible if it satisfies
// A * x >= b and x >= 0 from above. In the class PrimalSolution,
// variable_values is x and objective_value is c * x.
//
// For the general case of a MathOpt optimization model.
struct PrimalSolution {
  // Returns the PrimalSolution equivalent of primal_solution_proto.
  //
  // Returns an error when:
  //  * VariableValuesFromProto(primal_solution_proto.variable_values) fails.
  //  * the feasibility_status is not specified.
  static absl::StatusOr<PrimalSolution> FromProto(
      ModelStorageCPtr model, const PrimalSolutionProto& primal_solution_proto);
 
  // Returns the proto equivalent of this.
  PrimalSolutionProto Proto() const;
 
  // Returns the value for the given `objective`.
  //
  // Will CHECK-fail if `objective` has been deleted, or if it is from the is
  // from the wrong model (however, if the solution has no variables, this CHECK
  // will not occur due to an implementation detail of the struct).
  double get_objective_value(Objective objective) const;
 
  VariableMap<double> variable_values;
  double objective_value = 0.0;
  absl::flat_hash_map<Objective, double> auxiliary_objective_values;
 
  SolutionStatus feasibility_status = SolutionStatus::kUndetermined;
};
 
// A direction of unbounded improvement to an optimization problem;
// equivalently, a certificate of infeasibility for the dual of the
// optimization problem.
//
// E.g. consider a simple linear program:
//   min c * x
//   s.t. A * x >= b
//   x >= 0
// A primal ray is an x that satisfies:
//   c * x < 0
//   A * x >= 0
//   x >= 0
// Observe that given a feasible solution, any positive multiple of the primal
// ray plus that solution is still feasible, and gives a better objective
// value. A primal ray also proves the dual optimization problem infeasible.
//
// In the class PrimalRay, variable_values is this x.
//
// For the general case of a MathOpt optimization model.
struct PrimalRay {
  // Returns the PrimalRay equivalent of primal_ray_proto.
  //
  // Returns an error when
  // VariableValuesFromProto(primal_ray_proto.variable_values) fails.
  static absl::StatusOr<PrimalRay> FromProto(
      ModelStorageCPtr model, const PrimalRayProto& primal_ray_proto);
 
  // Returns the proto equivalent of this.
  PrimalRayProto Proto() const;
 
  VariableMap<double> variable_values;
};
 
// A solution to the dual of an optimization problem.
//
// E.g. consider the primal dual pair linear program pair:
//   (Primal)             (Dual)
//   min c * x            max b * y
//   s.t. A * x >= b      s.t. y * A + r = c
//   x >= 0               y, r >= 0.
// The dual solution is the pair (y, r). It is feasible if it satisfies the
// constraints from (Dual) above.
//
// Below, y is dual_values, r is reduced_costs, and b * y is objective value.
struct DualSolution {
  // Returns the DualSolution equivalent of dual_solution_proto.
  //
  // Returns an error when any of:
  //  * VariableValuesFromProto(dual_solution_proto.reduced_costs) fails.
  //  * LinearConstraintValuesFromProto(dual_solution_proto.dual_values) fails.
  //  * dual_solution_proto.feasibility_status is not specified.
  static absl::StatusOr<DualSolution> FromProto(
      ModelStorageCPtr model, const DualSolutionProto& dual_solution_proto);
 
  // Returns the proto equivalent of this.
  DualSolutionProto Proto() const;
 
  LinearConstraintMap<double> dual_values;
 
  // Note: Some solvers only return quadratic constraint duals when a
  // solver-specific parameter is set
  // (see go/mathopt-qcqp-dual#solver-specific).
  absl::flat_hash_map<QuadraticConstraint, double> quadratic_dual_values;
  VariableMap<double> reduced_costs;
  std::optional<double> objective_value;
 
  SolutionStatus feasibility_status = SolutionStatus::kUndetermined;
};
 
// A direction of unbounded improvement to the dual of an optimization
// problem; equivalently, a certificate of primal infeasibility.
//
// E.g. consider the primal dual linear program pair:
//    (Primal)              (Dual)
//    min c * x             max b * y
//    s.t. A * x >= b       s.t. y * A + r = c
//    x >= 0                y, r >= 0.
// The dual ray is the pair (y, r) satisfying:
//   b * y > 0
//   y * A + r = 0
//   y, r >= 0
// Observe that adding a positive multiple of (y, r) to dual feasible solution
// maintains dual feasibility and improves the objective (proving the dual is
// unbounded). The dual ray also proves the primal problem is infeasible.
//
// In the class DualRay, y is dual_values and r is reduced_costs.
struct DualRay {
  // Returns the DualRay equivalent of dual_ray_proto.
  //
  // Returns an error when either of:
  //  * VariableValuesFromProto(dual_ray_proto.reduced_costs) fails.
  //  * LinearConstraintValuesFromProto(dual_ray_proto.dual_values) fails.
  static absl::StatusOr<DualRay> FromProto(ModelStorageCPtr model,
                                           const DualRayProto& dual_ray_proto);
 
  // Returns the proto equivalent of this.
  DualRayProto Proto() const;
 
  LinearConstraintMap<double> dual_values;
  VariableMap<double> reduced_costs;
};
 
// A combinatorial characterization for a solution to a linear program.
//
// The simplex method for solving linear programs always returns a "basic
// feasible solution" which can be described combinatorially as a Basis. A
// basis assigns a BasisStatus for every variable and linear constraint.
//
// E.g. consider a standard form LP:
//   min c * x
//   s.t. A * x = b
//   x >= 0
// that has more variables than constraints and with full row rank A.
//
// Let n be the number of variables and m the number of linear constraints. A
// valid basis for this problem can be constructed as follows:
//  * All constraints will have basis status FIXED.
//  * Pick m variables such that the columns of A are linearly independent and
//    assign the status BASIC.
//  * Assign the status AT_LOWER for the remaining n - m variables.
//
// The basic solution for this basis is the unique solution of A * x = b that
// has all variables with status AT_LOWER fixed to their lower bounds (all
// zero). The resulting solution is called a basic feasible solution if it
// also satisfies x >= 0.
struct Basis {
  // Returns the equivalent Basis object for basis_proto.
  //
  // Returns an error if:
  //  * VariableBasisFromProto(basis_proto.variable_status) fails.
  //  * LinearConstraintBasisFromProto(basis_proto.constraint_status) fails.
  static absl::StatusOr<Basis> FromProto(ModelStorageCPtr model,
                                         const BasisProto& basis_proto);
 
  // Returns a failure if the referenced variables don't belong to the input
  // expected_storage (which must not be nullptr).
  absl::Status CheckModelStorage(ModelStorageCPtr expected_storage) const;
 
  // Returns the proto equivalent of this object.
  //
  // The caller should use CheckModelStorage() as this function does not check
  // internal consistency of the referenced variables and constraints.
  BasisProto Proto() const;
 
  LinearConstraintMap<BasisStatus> constraint_status;
  VariableMap<BasisStatus> variable_status;
 
  // This is an advanced feature used by MathOpt to characterize feasibility of
  // suboptimal LP solutions (optimal solutions will always have status
  // SolutionStatus::kFeasible).
  //
  // For single-sided LPs it should be equal to the feasibility status of the
  // associated dual solution. For two-sided LPs it may be different in some
  // edge cases (e.g. incomplete solves with primal simplex).
  //
  // If you are providing a starting basis via
  // `ModelSolveParameters.initial_basis`, this value is ignored. It is only
  // relevant for the basis returned by `Solution.basis`, and it is is always
  // populated in a Basis returned by a call to Solve().
  std::optional<SolutionStatus> basic_dual_feasibility;
};
 
// What is included in a solution depends on the kind of problem and solver.
// The current common patterns are
//   1. MIP solvers return only a primal solution.
//   2. Simplex LP solvers often return a basis and the primal and dual
//      solutions associated to this basis.
//   3. Other continuous solvers often return a primal and dual solution that
//      are connected in a solver-dependent form.
struct Solution {
  // Returns the Solution equivalent of solution_proto.
  //
  // Returns an error if FromProto() fails on any field that is not std::nullopt
  // (see the static FromProto() functions for each field type for details).
  static absl::StatusOr<Solution> FromProto(
      ModelStorageCPtr model, const SolutionProto& solution_proto);
 
  // Returns the proto equivalent of this.
  SolutionProto Proto() const;
 
  std::optional<PrimalSolution> primal_solution;
  std::optional<DualSolution> dual_solution;
  std::optional<Basis> basis;
};
 
}  // namespace math_opt
}  // namespace operations_research
 
#endif  // OR_TOOLS_MATH_OPT_CPP_SOLUTION_H_