linear_constraint_manager.h

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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.
 
#ifndef OR_TOOLS_SAT_LINEAR_CONSTRAINT_MANAGER_H_
#define OR_TOOLS_SAT_LINEAR_CONSTRAINT_MANAGER_H_
 
#include <cstddef>
#include <vector>
 
#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "ortools/base/strong_vector.h"
#include "ortools/glop/revised_simplex.h"
#include "ortools/sat/linear_constraint.h"
#include "ortools/sat/model.h"
#include "ortools/sat/sat_parameters.pb.h"
#include "ortools/util/time_limit.h"
 
namespace operations_research {
namespace sat {
 
// This class holds a list of globally valid linear constraints and has some
// logic to decide which one should be part of the LP relaxation. We want more
// for a better relaxation, but for efficiency we do not want to have too much
// constraints while solving the LP.
//
// This class is meant to contain all the initial constraints of the LP
// relaxation and to get new cuts as they are generated. Thus, it can both
// manage cuts but also only add the initial constraints lazily if there is too
// many of them.
class LinearConstraintManager {
 public:
  struct ConstraintInfo {
    LinearConstraint constraint;
    double l2_norm = 0.0;
    int64 inactive_count = 0;
    double objective_parallelism = 0.0;
    bool objective_parallelism_computed = false;
    bool is_in_lp = false;
    size_t hash;
    double current_score = 0.0;
 
    // Updated only for deletable constraints. This is incremented every time
    // ChangeLp() is called and the constraint is active in the LP or not in the
    // LP and violated.
    double active_count = 0.0;
 
    // For now, we mark all the generated cuts as deletable and the problem
    // constraints as undeletable.
    // TODO(user): We can have a better heuristics. Some generated good cuts
    // can be marked undeletable and some unused problem specified constraints
    // can be marked deletable.
    bool is_deletable = false;
  };
 
  explicit LinearConstraintManager(Model* model)
      : sat_parameters_(*model->GetOrCreate<SatParameters>()),
        integer_trail_(*model->GetOrCreate<IntegerTrail>()),
        time_limit_(model->GetOrCreate<TimeLimit>()),
        model_(model) {}
  ~LinearConstraintManager();
 
  // Add a new constraint to the manager. Note that we canonicalize constraints
  // and merge the bounds of constraints with the same terms. We also perform
  // basic preprocessing. If added is given, it will be set to true if this
  // constraint was actually a new one and to false if it was dominated by an
  // already existing one.
  DEFINE_INT_TYPE(ConstraintIndex, int32);
  ConstraintIndex Add(LinearConstraint ct, bool* added = nullptr);
 
  // Same as Add(), but logs some information about the newly added constraint.
  // Cuts are also handled slightly differently than normal constraints.
  //
  // Returns true if a new cut was added and false if this cut is not
  // efficacious or if it is a duplicate of an already existing one.
  bool AddCut(LinearConstraint ct, std::string type_name,
              const absl::StrongVector<IntegerVariable, double>& lp_solution,
              std::string extra_info = "");
 
  // The objective is used as one of the criterion to score cuts.
  // The more a cut is parallel to the objective, the better its score is.
  void SetObjectiveCoefficient(IntegerVariable var, IntegerValue coeff);
 
  // Heuristic to decides what LP is best solved next. The given lp_solution
  // should usually be the optimal solution of the LP returned by GetLp() before
  // this call, but is just used as an heuristic.
  //
  // The current solution state is used for detecting inactive constraints. It
  // is also updated correctly on constraint deletion/addition so that the
  // simplex can be fully iterative on restart by loading this modified state.
  //
  // Returns true iff LpConstraints() will return a different LP than before.
  bool ChangeLp(const absl::StrongVector<IntegerVariable, double>& lp_solution,
                glop::BasisState* solution_state);
 
  // This can be called initially to add all the current constraint to the LP
  // returned by GetLp().
  void AddAllConstraintsToLp();
 
  // All the constraints managed by this class.
  const absl::StrongVector<ConstraintIndex, ConstraintInfo>& AllConstraints()
      const {
    return constraint_infos_;
  }
 
  // The set of constraints indices in AllConstraints() that should be part
  // of the next LP to solve.
  const std::vector<ConstraintIndex>& LpConstraints() const {
    return lp_constraints_;
  }
 
  int64 num_cuts() const { return num_cuts_; }
  int64 num_shortened_constraints() const { return num_shortened_constraints_; }
  int64 num_coeff_strenghtening() const { return num_coeff_strenghtening_; }
 
  // If a debug solution has been loaded, this checks if the given constaint cut
  // it or not. Returns true iff everything is fine and the cut does not violate
  // the loaded solution.
  bool DebugCheckConstraint(const LinearConstraint& cut);
 
 private:
  // Heuristic that decide which constraints we should remove from the current
  // LP. Note that such constraints can be added back later by the heuristic
  // responsible for adding new constraints from the pool.
  //
  // Returns true iff one or more constraints where removed.
  //
  // If the solutions_state is empty, then this function does nothing and
  // returns false (this is used for tests). Otherwise, the solutions_state is
  // assumed to correspond to the current LP and to be of the correct size.
  bool MaybeRemoveSomeInactiveConstraints(glop::BasisState* solution_state);
 
  // Apply basic inprocessing simplification rules:
  //  - remove fixed variable
  //  - reduce large coefficient (i.e. coeff strenghtenning or big-M reduction).
  // This uses level-zero bounds.
  // Returns true if the terms of the constraint changed.
  bool SimplifyConstraint(LinearConstraint* ct);
 
  // Helper method to compute objective parallelism for a given constraint. This
  // also lazily computes objective norm.
  void ComputeObjectiveParallelism(const ConstraintIndex ct_index);
 
  // Multiplies all active counts and the increment counter by the given
  // 'scaling_factor'. This should be called when at least one of the active
  // counts is too high.
  void RescaleActiveCounts(double scaling_factor);
 
  // Removes some deletable constraints with low active counts. For now, we
  // don't remove any constraints which are already in LP.
  void PermanentlyRemoveSomeConstraints();
 
  const SatParameters& sat_parameters_;
  const IntegerTrail& integer_trail_;
 
  // Set at true by Add()/SimplifyConstraint() and at false by ChangeLp().
  bool current_lp_is_changed_ = false;
 
  // Optimization to avoid calling SimplifyConstraint() when not needed.
  int64 last_simplification_timestamp_ = 0;
 
  absl::StrongVector<ConstraintIndex, ConstraintInfo> constraint_infos_;
 
  // The subset of constraints currently in the lp.
  std::vector<ConstraintIndex> lp_constraints_;
 
  // We keep a map from the hash of our constraint terms to their position in
  // constraints_. This is an optimization to detect duplicate constraints. We
  // are robust to collisions because we always relies on the ground truth
  // contained in constraints_ and the code is still okay if we do not merge the
  // constraints.
  absl::flat_hash_map<size_t, ConstraintIndex> equiv_constraints_;
 
  int64 num_simplifications_ = 0;
  int64 num_merged_constraints_ = 0;
  int64 num_shortened_constraints_ = 0;
  int64 num_splitted_constraints_ = 0;
  int64 num_coeff_strenghtening_ = 0;
 
  int64 num_cuts_ = 0;
  int64 num_add_cut_calls_ = 0;
  std::map<std::string, int> type_to_num_cuts_;
 
  bool objective_is_defined_ = false;
  bool objective_norm_computed_ = false;
  double objective_l2_norm_ = 0.0;
 
  // Total deterministic time spent in this class.
  double dtime_ = 0.0;
 
  // Dense representation of the objective coeffs indexed by positive variables
  // indices. It contains 0.0 where the variables does not appear in the
  // objective.
  absl::StrongVector<IntegerVariable, double> dense_objective_coeffs_;
 
  TimeLimit* time_limit_;
  Model* model_;
 
  // We want to decay the active counts of all constraints at each call and
  // increase the active counts of active/violated constraints. However this can
  // be too slow in practice. So instead, we keep an increment counter and
  // update only the active/violated constraints. The counter itself is
  // increased by a factor at each call. This has the same effect as decaying
  // all the active counts at each call. This trick is similar to sat clause
  // management.
  double constraint_active_count_increase_ = 1.0;
 
  int32 num_deletable_constraints_ = 0;
};
 
// Keep the top n elements from a stream of elements.
//
// TODO(user): We could use gtl::TopN when/if it gets open sourced. Note that
// we might be slighlty faster here since we use an indirection and don't move
// the Element class around as much.
template <typename Element>
class TopN {
 public:
  explicit TopN(int n) : n_(n) {}
 
  void Clear() {
    heap_.clear();
    elements_.clear();
  }
 
  void Add(Element e, double score) {
    if (heap_.size() < n_) {
      const int index = elements_.size();
      heap_.push_back({index, score});
      elements_.push_back(std::move(e));
      if (heap_.size() == n_) {
        // TODO(user): We could delay that on the n + 1 push.
        std::make_heap(heap_.begin(), heap_.end());
      }
    } else {
      if (score <= heap_.front().score) return;
      const int index_to_replace = heap_.front().index;
      elements_[index_to_replace] = std::move(e);
 
      // If needed, we could be faster here with an update operation.
      std::pop_heap(heap_.begin(), heap_.end());
      heap_.back() = {index_to_replace, score};
      std::push_heap(heap_.begin(), heap_.end());
    }
  }
 
  const std::vector<Element>& UnorderedElements() const { return elements_; }
 
 private:
  const int n_;
 
  // We keep a heap of the n lowest score.
  struct HeapElement {
    int index;  // in elements_;
    double score;
    const double operator<(const HeapElement& other) const {
      return score > other.score;
    }
  };
  std::vector<HeapElement> heap_;
  std::vector<Element> elements_;
};
 
// Before adding cuts to the global pool, it is a classical thing to only keep
// the top n of a given type during one generation round. This is there to help
// doing that.
//
// TODO(user): Avoid computing efficacity twice.
// TODO(user): We don't use any orthogonality consideration here.
// TODO(user): Detect duplicate cuts?
class TopNCuts {
 public:
  explicit TopNCuts(int n) : cuts_(n) {}
 
  // Add a cut to the local pool
  void AddCut(LinearConstraint ct, const std::string& name,
              const absl::StrongVector<IntegerVariable, double>& lp_solution);
 
  // Empty the local pool and add all its content to the manager.
  void TransferToManager(
      const absl::StrongVector<IntegerVariable, double>& lp_solution,
      LinearConstraintManager* manager);
 
 private:
  struct CutCandidate {
    std::string name;
    LinearConstraint cut;
  };
  TopN<CutCandidate> cuts_;
};
 
}  // namespace sat
}  // namespace operations_research
 
#endif  // OR_TOOLS_SAT_LINEAR_CONSTRAINT_MANAGER_H_