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/******************************************************************************
* Top contributors (to current version):
* Tim King, Morgan Deters, Mathias Preiner
*
* This file is part of the cvc5 project.
*
* Copyright (c) 2009-2021 by the authors listed in the file AUTHORS
* in the top-level source directory and their institutional affiliations.
* All rights reserved. See the file COPYING in the top-level source
* directory for licensing information.
* ****************************************************************************
*
* This is an implementation of the Simplex Module for the Simplex for
* DPLL(T) decision procedure.
*
* This implements the Simplex module for the Simpelx for DPLL(T) decision
* procedure.
* See the Simplex for DPLL(T) technical report for more background.(citation?)
* This shares with the theory a Tableau, and a PartialModel that:
* - satisfies the equalities in the Tableau, and
* - the assignment for the non-basic variables satisfies their bounds.
* This is required to either produce a conflict or satisifying PartialModel.
* Further, we require being told when a basic variable updates its value.
*
* During the Simplex search we maintain a queue of variables.
* The queue is required to contain all of the basic variables that voilate
* their bounds.
* As elimination from the queue is more efficient to be done lazily,
* we do not maintain that the queue of variables needs to be only basic
* variables or only variables that satisfy their bounds.
*
* The simplex procedure roughly follows Alberto's thesis. (citation?)
* There is one round of selecting using a heuristic pivoting rule.
* (See PreferenceFunction Documentation for the available options.)
* The non-basic variable is the one that appears in the fewest pivots.
* (Bruno says that Leonardo invented this first.)
* After this, Bland's pivot rule is invoked.
*
* During this proccess, we periodically inspect the queue of variables to
* 1) remove now extraneous extries,
* 2) detect conflicts that are "waiting" on the queue but may not be detected
* by the current queue heuristics, and
* 3) detect multiple conflicts.
*
* Conflicts are greedily slackened to use the weakest bounds that still
* produce the conflict.
*
* Extra things tracked atm: (Subject to change at Tim's whims)
* - A superset of all of the newly pivoted variables.
* - A queue of additional conflicts that were discovered by Simplex.
* These are theory valid and are currently turned into lemmas
*/
#include "cvc5_private.h"
#pragma once
#include "theory/arith/approx_simplex.h"
#include "theory/arith/simplex.h"
#include "util/statistics_stats.h"
namespace cvc5 {
namespace theory {
namespace arith {
class AttemptSolutionSDP : public SimplexDecisionProcedure {
public:
AttemptSolutionSDP(LinearEqualityModule& linEq, ErrorSet& errors, RaiseConflict conflictChannel, TempVarMalloc tvmalloc);
Result::Sat attempt(const ApproximateSimplex::Solution& sol);
Result::Sat findModel(bool exactResult) override { Unreachable(); }
private:
bool matchesNewValue(const DenseMap<DeltaRational>& nv, ArithVar v) const;
bool processSignals(){
TimerStat &timer = d_statistics.d_queueTime;
IntStat& conflictStat = d_statistics.d_conflicts;
return standardProcessSignals(timer, conflictStat);
}
/** These fields are designed to be accessible to TheoryArith methods. */
class Statistics {
public:
TimerStat d_searchTime;
TimerStat d_queueTime;
IntStat d_conflicts;
Statistics();
} d_statistics;
};/* class AttemptSolutionSDP */
} // namespace arith
} // namespace theory
} // namespace cvc5
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