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simplification.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.
// Implementation of a pure SAT presolver. This roughly follows the paper:
//
// "Effective Preprocessing in SAT through Variable and Clause Elimination",
// Niklas Een and Armin Biere, published in the SAT 2005 proceedings.
#ifndef OR_TOOLS_SAT_SIMPLIFICATION_H_
#define OR_TOOLS_SAT_SIMPLIFICATION_H_
#include <deque>
#include <memory>
#include <set>
#include <vector>
#include "absl/types/span.h"
#include "ortools/base/adjustable_priority_queue.h"
#include "ortools/base/int_type.h"
#include "ortools/base/integral_types.h"
#include "ortools/base/macros.h"
#include "ortools/base/strong_vector.h"
#include "ortools/sat/drat_proof_handler.h"
#include "ortools/sat/sat_base.h"
#include "ortools/sat/sat_parameters.pb.h"
#include "ortools/sat/sat_solver.h"
#include "ortools/util/time_limit.h"
namespace operations_research {
namespace sat {
// A simple sat postsolver.
//
// The idea is that any presolve algorithm can just update this class, and at
// the end, this class will recover a solution of the initial problem from a
// solution of the presolved problem.
class SatPostsolver {
public:
explicit SatPostsolver(int num_variables);
// The postsolver will process the Add() calls in reverse order. If the given
// clause has all its literals at false, it simply sets the literal x to true.
// Note that x must be a literal of the given clause.
void Add(Literal x, const absl::Span<const Literal> clause);
// Tells the postsolver that the given literal must be true in any solution.
// We currently check that the variable is not already fixed.
//
// TODO(user): this as almost the same effect as adding an unit clause, and we
// should probably remove this to simplify the code.
void FixVariable(Literal x);
// This assumes that the given variable mapping has been applied to the
// problem. All the subsequent Add() and FixVariable() will refer to the new
// problem. During postsolve, the initial solution must also correspond to
// this new problem. Note that if mapping[v] == -1, then the literal v is
// assumed to be deleted.
//
// This can be called more than once. But each call must refer to the current
// variables set (after all the previous mapping have been applied).
void ApplyMapping(
const absl::StrongVector<BooleanVariable, BooleanVariable>& mapping);
// Extracts the current assignment of the given solver and postsolve it.
//
// Node(fdid): This can currently be called only once (but this is easy to
// change since only some CHECK will fail).
std::vector<bool> ExtractAndPostsolveSolution(const SatSolver& solver);
std::vector<bool> PostsolveSolution(const std::vector<bool>& solution);
// Getters to the clauses managed by this class.
// Important: This will always put the associated literal first.
int NumClauses() const { return clauses_start_.size(); }
std::vector<Literal> Clause(int i) const {
// TODO(user): we could avoid the copy here, but because clauses_literals_
// is a deque, we do need a special return class and cannot juste use
// absl::Span<Literal> for instance.
const int begin = clauses_start_[i];
const int end = i + 1 < clauses_start_.size() ? clauses_start_[i + 1]
: clauses_literals_.size();
std::vector<Literal> result(clauses_literals_.begin() + begin,
clauses_literals_.begin() + end);
for (int j = 0; j < result.size(); ++j) {
if (result[j] == associated_literal_[i]) {
std::swap(result[0], result[j]);
break;
}
}
return result;
}
private:
Literal ApplyReverseMapping(Literal l);
void Postsolve(VariablesAssignment* assignment) const;
// The presolve can add new variables, so we need to store the number of
// original variables in order to return a solution with the correct number
// of variables.
const int initial_num_variables_;
int num_variables_;
// Stores the arguments of the Add() calls: clauses_start_[i] is the index of
// the first literal of the clause #i in the clauses_literals_ deque.
std::vector<int> clauses_start_;
std::deque<Literal> clauses_literals_;
std::vector<Literal> associated_literal_;
// All the added clauses will be mapped back to the initial variables using
// this reverse mapping. This way, clauses_ and associated_literal_ are only
// in term of the initial problem.
absl::StrongVector<BooleanVariable, BooleanVariable> reverse_mapping_;
// This will stores the fixed variables value and later the postsolved
// assignment.
VariablesAssignment assignment_;
DISALLOW_COPY_AND_ASSIGN(SatPostsolver);
};
// This class holds a SAT problem (i.e. a set of clauses) and the logic to
// presolve it by a series of subsumption, self-subsuming resolution, and
// variable elimination by clause distribution.
//
// Note that this does propagate unit-clauses, but probably much
// less efficiently than the propagation code in the SAT solver. So it is better
// to use a SAT solver to fix variables before using this class.
//
// TODO(user): Interact more with a SAT solver to reuse its propagation logic.
//
// TODO(user): Forbid the removal of some variables. This way we can presolve
// only the clause part of a general Boolean problem by not removing variables
// appearing in pseudo-Boolean constraints.
class SatPresolver {
public:
// TODO(user): use IntType!
typedef int32 ClauseIndex;
explicit SatPresolver(SatPostsolver* postsolver)
: postsolver_(postsolver),
num_trivial_clauses_(0),
drat_proof_handler_(nullptr) {}
void SetParameters(const SatParameters& params) { parameters_ = params; }
void SetTimeLimit(TimeLimit* time_limit) { time_limit_ = time_limit; }
// Registers a mapping to encode equivalent literals.
// See ProbeAndFindEquivalentLiteral().
void SetEquivalentLiteralMapping(
const absl::StrongVector<LiteralIndex, LiteralIndex>& mapping) {
equiv_mapping_ = mapping;
}
// Adds new clause to the SatPresolver.
void SetNumVariables(int num_variables);
void AddBinaryClause(Literal a, Literal b);
void AddClause(absl::Span<const Literal> clause);
// Presolves the problem currently loaded. Returns false if the model is
// proven to be UNSAT during the presolving.
//
// TODO(user): Add support for a time limit and some kind of iterations limit
// so that this can never take too much time.
bool Presolve();
// Same as Presolve() but only allow to remove BooleanVariable whose index
// is set to true in the given vector.
bool Presolve(const std::vector<bool>& var_that_can_be_removed,
bool log_info = false);
// All the clauses managed by this class.
// Note that deleted clauses keep their indices (they are just empty).
int NumClauses() const { return clauses_.size(); }
const std::vector<Literal>& Clause(ClauseIndex ci) const {
return clauses_[ci];
}
// The number of variables. This is computed automatically from the clauses
// added to the SatPresolver.
int NumVariables() const { return literal_to_clause_sizes_.size() / 2; }
// After presolving, Some variables in [0, NumVariables()) have no longer any
// clause pointing to them. This return a mapping that maps this interval to
// [0, new_size) such that now all variables are used. The unused variable
// will be mapped to BooleanVariable(-1).
absl::StrongVector<BooleanVariable, BooleanVariable> VariableMapping() const;
// Loads the current presolved problem in to the given sat solver.
// Note that the variables will be re-indexed according to the mapping given
// by GetMapping() so that they form a dense set.
//
// IMPORTANT: This is not const because it deletes the presolver clauses as
// they are added to the SatSolver in order to save memory. After this is
// called, only VariableMapping() will still works.
void LoadProblemIntoSatSolver(SatSolver* solver);
// Visible for Testing. Takes a given clause index and looks for clause that
// can be subsumed or strengthened using this clause. Returns false if the
// model is proven to be unsat.
bool ProcessClauseToSimplifyOthers(ClauseIndex clause_index);
// Visible for testing. Tries to eliminate x by clause distribution.
// This is also known as bounded variable elimination.
//
// It is always possible to remove x by resolving each clause containing x
// with all the clauses containing not(x). Hence the cross-product name. Note
// that this function only do that if the number of clauses is reduced.
bool CrossProduct(Literal x);
// Visible for testing. Just applies the BVA step of the presolve.
void PresolveWithBva();
void SetDratProofHandler(DratProofHandler* drat_proof_handler) {
drat_proof_handler_ = drat_proof_handler;
}
private:
// Internal function used by ProcessClauseToSimplifyOthers().
bool ProcessClauseToSimplifyOthersUsingLiteral(ClauseIndex clause_index,
Literal lit);
// Internal function to add clauses generated during the presolve. The clause
// must already be sorted with the default Literal order and will be cleared
// after this call.
void AddClauseInternal(std::vector<Literal>* clause);
// Clause removal function.
void Remove(ClauseIndex ci);
void RemoveAndRegisterForPostsolve(ClauseIndex ci, Literal x);
void RemoveAndRegisterForPostsolveAllClauseContaining(Literal x);
// Call ProcessClauseToSimplifyOthers() on all the clauses in
// clause_to_process_ and empty the list afterwards. Note that while some
// clauses are processed, new ones may be added to the list. Returns false if
// the problem is shown to be UNSAT.
bool ProcessAllClauses();
// Finds the literal from the clause that occur the less in the clause
// database.
Literal FindLiteralWithShortestOccurrenceList(
const std::vector<Literal>& clause);
LiteralIndex FindLiteralWithShortestOccurrenceListExcluding(
const std::vector<Literal>& clause, Literal to_exclude);
// Tests and maybe perform a Simple Bounded Variable addition starting from
// the given literal as described in the paper: "Automated Reencoding of
// Boolean Formulas", Norbert Manthey, Marijn J. H. Heule, and Armin Biere,
// Volume 7857 of the series Lecture Notes in Computer Science pp 102-117,
// 2013.
// https://www.research.ibm.com/haifa/conferences/hvc2012/papers/paper16.pdf
//
// This seems to have a mostly positive effect, except on the crafted problem
// familly mugrauer_balint--GI.crafted_nxx_d6_cx_numxx where the reduction
// is big, but apparently the problem is harder to prove UNSAT for the solver.
void SimpleBva(LiteralIndex l);
// Display some statistics on the current clause database.
void DisplayStats(double elapsed_seconds);
// Returns a hash of the given clause variables (not literal) in such a way
// that hash1 & not(hash2) == 0 iff the set of variable of clause 1 is a
// subset of the one of clause2.
uint64 ComputeSignatureOfClauseVariables(ClauseIndex ci);
// The "active" variables on which we want to call CrossProduct() are kept
// in a priority queue so that we process first the ones that occur the least
// often in the clause database.
void InitializePriorityQueue();
void UpdatePriorityQueue(BooleanVariable var);
struct PQElement {
PQElement() : heap_index(-1), variable(-1), weight(0.0) {}
// Interface for the AdjustablePriorityQueue.
void SetHeapIndex(int h) { heap_index = h; }
int GetHeapIndex() const { return heap_index; }
// Priority order. The AdjustablePriorityQueue returns the largest element
// first, but our weight goes this other way around (smaller is better).
bool operator<(const PQElement& other) const {
return weight > other.weight;
}
int heap_index;
BooleanVariable variable;
double weight;
};
absl::StrongVector<BooleanVariable, PQElement> var_pq_elements_;
AdjustablePriorityQueue<PQElement> var_pq_;
// Literal priority queue for BVA. The literals are ordered by descending
// number of occurrences in clauses.
void InitializeBvaPriorityQueue();
void UpdateBvaPriorityQueue(LiteralIndex lit);
void AddToBvaPriorityQueue(LiteralIndex lit);
struct BvaPqElement {
BvaPqElement() : heap_index(-1), literal(-1), weight(0.0) {}
// Interface for the AdjustablePriorityQueue.
void SetHeapIndex(int h) { heap_index = h; }
int GetHeapIndex() const { return heap_index; }
// Priority order.
// The AdjustablePriorityQueue returns the largest element first.
bool operator<(const BvaPqElement& other) const {
return weight < other.weight;
}
int heap_index;
LiteralIndex literal;
double weight;
};
std::deque<BvaPqElement> bva_pq_elements_; // deque because we add variables.
AdjustablePriorityQueue<BvaPqElement> bva_pq_;
// Temporary data for SimpleBva().
std::set<LiteralIndex> m_lit_;
std::vector<ClauseIndex> m_cls_;
absl::StrongVector<LiteralIndex, int> literal_to_p_size_;
std::vector<std::pair<LiteralIndex, ClauseIndex>> flattened_p_;
std::vector<Literal> tmp_new_clause_;
// List of clauses on which we need to call ProcessClauseToSimplifyOthers().
// See ProcessAllClauses().
std::vector<bool> in_clause_to_process_;
std::deque<ClauseIndex> clause_to_process_;
// The set of all clauses.
// An empty clause means that it has been removed.
std::vector<std::vector<Literal>> clauses_; // Indexed by ClauseIndex
// The cached value of ComputeSignatureOfClauseVariables() for each clause.
std::vector<uint64> signatures_; // Indexed by ClauseIndex
int64 num_inspected_signatures_ = 0;
int64 num_inspected_literals_ = 0;
// Occurrence list. For each literal, contains the ClauseIndex of the clause
// that contains it (ordered by clause index).
absl::StrongVector<LiteralIndex, std::vector<ClauseIndex>>
literal_to_clauses_;
// Because we only lazily clean the occurrence list after clause deletions,
// we keep the size of the occurrence list (without the deleted clause) here.
absl::StrongVector<LiteralIndex, int> literal_to_clause_sizes_;
// Used for postsolve.
SatPostsolver* postsolver_;
// Equivalent literal mapping.
absl::StrongVector<LiteralIndex, LiteralIndex> equiv_mapping_;
int num_trivial_clauses_;
SatParameters parameters_;
DratProofHandler* drat_proof_handler_;
TimeLimit* time_limit_ = nullptr;
DISALLOW_COPY_AND_ASSIGN(SatPresolver);
};
// Visible for testing. Returns true iff:
// - a subsume b (subsumption): the clause a is a subset of b, in which case
// opposite_literal is set to -1.
// - b is strengthened by self-subsumption using a (self-subsuming resolution):
// the clause a with one of its literal negated is a subset of b, in which
// case opposite_literal is set to this negated literal index. Moreover, this
// opposite_literal is then removed from b.
//
// If num_inspected_literals_ is not nullptr, the "complexity" of this function
// will be added to it in order to track the amount of work done.
//
// TODO(user): when a.size() << b.size(), we should use binary search instead
// of scanning b linearly.
bool SimplifyClause(const std::vector<Literal>& a, std::vector<Literal>* b,
LiteralIndex* opposite_literal,
int64* num_inspected_literals = nullptr);
// Visible for testing. Returns kNoLiteralIndex except if:
// - a and b differ in only one literal.
// - For a it is the given literal l.
// In which case, returns the LiteralIndex of the literal in b that is not in a.
LiteralIndex DifferAtGivenLiteral(const std::vector<Literal>& a,
const std::vector<Literal>& b, Literal l);
// Visible for testing. Computes the resolvant of 'a' and 'b' obtained by
// performing the resolution on 'x'. If the resolvant is trivially true this
// returns false, otherwise it returns true and fill 'out' with the resolvant.
//
// Note that the resolvant is just 'a' union 'b' with the literals 'x' and
// not(x) removed. The two clauses are assumed to be sorted, and the computed
// resolvant will also be sorted.
//
// This is the basic operation when a variable is eliminated by clause
// distribution.
bool ComputeResolvant(Literal x, const std::vector<Literal>& a,
const std::vector<Literal>& b, std::vector<Literal>* out);
// Same as ComputeResolvant() but just returns the resolvant size.
// Returns -1 when ComputeResolvant() returns false.
int ComputeResolvantSize(Literal x, const std::vector<Literal>& a,
const std::vector<Literal>& b);
// Presolver that does literals probing and finds equivalent literals by
// computing the strongly connected components of the graph:
// literal l -> literals propagated by l.
//
// Clears the mapping if there are no equivalent literals. Otherwise, mapping[l]
// is the representative of the equivalent class of l. Note that mapping[l] may
// be equal to l.
//
// The postsolver will be updated so it can recover a solution of the mapped
// problem. Note that this works on any problem the SatSolver can handle, not
// only pure SAT problem, but the returned mapping do need to be applied to all
// constraints.
void ProbeAndFindEquivalentLiteral(
SatSolver* solver, SatPostsolver* postsolver,
DratProofHandler* drat_proof_handler,
absl::StrongVector<LiteralIndex, LiteralIndex>* mapping);
// Given a 'solver' with a problem already loaded, this will try to simplify the
// problem (i.e. presolve it) before calling solver->Solve(). In the process,
// because of the way the presolve is implemented, the underlying SatSolver may
// change (it is why we use this unique_ptr interface). In particular, the final
// variables and 'solver' state may have nothing to do with the problem
// originaly present in the solver. That said, if the problem is shown to be
// SAT, then the returned solution will be in term of the original variables.
//
// Note that the full presolve is only executed if the problem is a pure SAT
// problem with only clauses.
SatSolver::Status SolveWithPresolve(
std::unique_ptr<SatSolver>* solver, TimeLimit* time_limit,
std::vector<bool>* solution /* only filled if SAT */,
DratProofHandler* drat_proof_handler /* can be nullptr */);
} // namespace sat
} // namespace operations_research
#endif // OR_TOOLS_SAT_SIMPLIFICATION_H_