feasibility_pump.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 OR_TOOLS_SAT_FEASIBILITY_PUMP_H_
#define OR_TOOLS_SAT_FEASIBILITY_PUMP_H_
 
#include <cstdint>
 
#include "ortools/base/strong_vector.h"
#include "ortools/glop/revised_simplex.h"
#include "ortools/lp_data/lp_data.h"
#include "ortools/lp_data/lp_data_utils.h"
#include "ortools/lp_data/lp_types.h"
#include "ortools/sat/cp_model_loader.h"
#include "ortools/sat/integer.h"
#include "ortools/sat/linear_constraint.h"
#include "ortools/sat/sat_solver.h"
#include "ortools/sat/synchronization.h"
#include "ortools/sat/util.h"
 
namespace operations_research {
namespace sat {
 
class FeasibilityPump {
 public:
  explicit FeasibilityPump(Model* model);
  ~FeasibilityPump();
 
  typedef glop::RowIndex ConstraintIndex;
 
  void SetMaxFPIterations(int max_iter) {
    max_fp_iterations_ = std::max(1, max_iter);
  }
 
  // Add a new linear constraint to this LP.
  void AddLinearConstraint(const LinearConstraint& ct);
 
  // Set the coefficient of the variable in the objective. Calling it twice will
  // overwrite the previous value. Note that this doesn't set the objective
  // coefficient if the variable doesn't appear in any constraints. So this has
  // to be called after all the constraints are added.
  void SetObjectiveCoefficient(IntegerVariable ivar, IntegerValue coeff);
 
  // Returns the LP value of a variable in the current
  // solution. These functions should only be called when HasSolution() is true.
  bool HasLPSolution() const { return lp_solution_is_set_; }
  double LPSolutionObjectiveValue() const { return lp_objective_; }
  double GetLPSolutionValue(IntegerVariable variable) const;
  bool LPSolutionIsInteger() const { return lp_solution_is_integer_; }
  double LPSolutionFractionality() const { return lp_solution_fractionality_; }
 
  // Returns the Integer solution value of a variable in the current rounded
  // solution. These functions should only be called when HasIntegerSolution()
  // is true.
  bool HasIntegerSolution() const { return integer_solution_is_set_; }
  int64_t IntegerSolutionObjectiveValue() const {
    return integer_solution_objective_;
  }
  bool IntegerSolutionIsFeasible() const {
    return integer_solution_is_feasible_;
  }
  int64_t GetIntegerSolutionValue(IntegerVariable variable) const;
 
  // Returns false if the model is proven to be infeasible.
  bool Solve();
 
 private:
  // Solve the LP, returns false if something went wrong in the LP solver.
  bool SolveLp();
 
  // Calls the specified rounding method in the parameters. Returns false if the
  // rounding couldn't be finished.
  bool Round();
 
  // Round the fractional LP solution values to nearest integer values. This
  // rounding always finishes so always returns true.
  bool NearestIntegerRounding();
 
  // Counts the number of up and down locks as defined below.
  // #up_locks = #upper bounded constraints with positive coeff for var
  //           + #lower bounded constraints with negative coeff for var.
  // #down_locks = #lower bounded constraints with positive coeff for var
  //             + #upper bounded constraints with negative coeff for var.
  // Rounds the variable in the direction of lesser locks. When the
  // fractionality is low (less than 0.1), this reverts to nearest integer
  // rounding to avoid rounding almost integer values in wrong direction.
  // This rounding always finishes so always returns true.
  bool LockBasedRounding();
 
  // Similar to LockBasedRounding except this only considers locks of active
  // constraints.
  bool ActiveLockBasedRounding();
 
  // This is expensive rounding algorithm. We round variables one by one and
  // propagate the bounds in between. If none of the rounded values fall in
  // the continuous domain specified by lower and upper bound, we use the
  // current lower/upper bound (whichever one is closest) instead of rounding
  // the fractional lp solution value. If both the rounded values are in the
  // domain, we round to nearest integer. This idea was presented in the paper
  // "Feasibility pump 2.0" (2009) by Matteo Fischetti, Domenico Salvagnin.
  //
  // This rounding might not finish either because the time limit is reached or
  // the model is detected to be unsat. Returns false in those cases.
  bool PropagationRounding();
 
  void FillIntegerSolutionStats();
 
  // Loads the lp_data_.
  void InitializeWorkingLP();
 
  // Changes the LP objective and bounds of the norm constraints so the new
  // objective also tries to minimize the distance to the rounded solution.
  void L1DistanceMinimize();
 
  // Stores the solutions in the shared repository. Stores LP solution if it is
  // integer and stores the integer solution if it is feasible.
  void MaybePushToRepo();
 
  void PrintStats();
 
  // Returns the variable value on the same scale as the CP variable value.
  double GetVariableValueAtCpScale(glop::ColIndex var);
 
  // Shortcut for an integer linear expression type.
  using LinearExpression = std::vector<std::pair<glop::ColIndex, IntegerValue>>;
 
  // Gets or creates an LP variable that mirrors a model variable.
  // The variable should be a positive reference.
  glop::ColIndex GetOrCreateMirrorVariable(IntegerVariable positive_variable);
 
  // Updates the bounds of the LP variables from the CP bounds.
  void UpdateBoundsOfLpVariables();
 
  // This epsilon is related to the precision of the value returned by the LP
  // once they have been scaled back into the CP domain. So for large domain or
  // cost coefficient, we may have some issues.
  static const double kCpEpsilon;
 
  // Initial problem in integer form.
  // We always sort the inner vectors by increasing glop::ColIndex.
  struct LinearConstraintInternal {
    IntegerValue lb;
    IntegerValue ub;
    LinearExpression terms;
  };
  LinearExpression integer_objective_;
  IntegerValue objective_infinity_norm_ = IntegerValue(0);
  double objective_normalization_factor_ = 0.0;
  double mixing_factor_ = 1.0;
 
  absl::StrongVector<glop::RowIndex, LinearConstraintInternal> integer_lp_;
  int model_vars_size_ = 0;
 
  // Underlying LP solver API.
  glop::LinearProgram lp_data_;
  glop::RevisedSimplex simplex_;
 
  glop::ColMapping norm_variables_;
  glop::ColToRowMapping norm_lhs_constraints_;
  glop::ColToRowMapping norm_rhs_constraints_;
 
  // For the scaling.
  glop::LpScalingHelper scaler_;
 
  // Structures used for mirroring IntegerVariables inside the underlying LP
  // solver: an integer variable var is mirrored by mirror_lp_variable_[var].
  // Note that these indices are dense in [0, mirror_lp_variable_.size()] so
  // they can be used as vector indices.
  std::vector<IntegerVariable> integer_variables_;
  absl::flat_hash_map<IntegerVariable, glop::ColIndex> mirror_lp_variable_;
 
  // True if the variable was binary before we apply scaling.
  std::vector<bool> var_is_binary_;
 
  // The following lock information is computed only once.
  // Number of constraints restricting variable to take higher (resp. lower)
  // values.
  std::vector<int> var_up_locks_;
  std::vector<int> var_down_locks_;
 
  // We need to remember what to optimize if an objective is given, because
  // then we will switch the objective between feasibility and optimization.
  bool objective_is_defined_ = false;
 
  // Singletons from Model.
  const SatParameters& sat_parameters_;
  TimeLimit* time_limit_;
  IntegerTrail* integer_trail_;
  Trail* trail_;
  IntegerEncoder* integer_encoder_;
  SharedIncompleteSolutionManager* incomplete_solutions_;
  SatSolver* sat_solver_;
  IntegerDomains* domains_;
  const CpModelMapping* mapping_;
 
  // Last OPTIMAL/Feasible solution found by a call to the underlying LP solver.
  bool lp_solution_is_set_ = false;
  bool lp_solution_is_integer_ = false;
  double lp_objective_;
  std::vector<double> lp_solution_;
  std::vector<double> best_lp_solution_;
  // We use max fractionality of all variables.
  double lp_solution_fractionality_;
 
  // Rounded Integer solution. This might not be feasible.
  bool integer_solution_is_set_ = false;
  bool integer_solution_is_feasible_ = false;
  int64_t integer_solution_objective_;
  std::vector<int64_t> integer_solution_;
  std::vector<int64_t> best_integer_solution_;
  int num_infeasible_constraints_;
  // We use max infeasibility of all constraints.
  int64_t integer_solution_infeasibility_;
 
  // Sum of all simplex iterations performed by this class. This is useful to
  // test the incrementality and compare to other solvers.
  int64_t total_num_simplex_iterations_ = 0;
 
  // TODO(user): Tune default value. Expose as parameter.
  int max_fp_iterations_ = 20;
 
  bool model_is_unsat_ = false;
};
 
}  // namespace sat
}  // namespace operations_research
 
#endif  // OR_TOOLS_SAT_FEASIBILITY_PUMP_H_