/****************************************************************************** * Top contributors (to current version): * Tim King, Mathias Preiner, Morgan Deters * * 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/simplex.h" #include "util/statistics_stats.h" namespace cvc5 { namespace theory { namespace arith { class DualSimplexDecisionProcedure : public SimplexDecisionProcedure{ public: DualSimplexDecisionProcedure(LinearEqualityModule& linEq, ErrorSet& errors, RaiseConflict conflictChannel, TempVarMalloc tvmalloc); Result::Sat findModel(bool exactResult) override { return dualFindModel(exactResult); } private: /** * Maps a variable to how many times they have been used as a pivot in the * simplex search. */ DenseMultiset d_pivotsInRound; Result::Sat dualFindModel(bool exactResult); /** * This is the main simplex for DPLL(T) loop. * It runs for at most maxIterations. * * Returns true iff it has found a conflict. * d_conflictVariable will be set and the conflict for this row is reported. */ bool searchForFeasibleSolution(uint32_t maxIterations); bool processSignals(){ TimerStat &timer = d_statistics.d_processSignalsTime; IntStat& conflictStat = d_statistics.d_recentViolationCatches; return standardProcessSignals(timer, conflictStat); } /** These fields are designed to be accessible to TheoryArith methods. */ class Statistics { public: IntStat d_statUpdateConflicts; TimerStat d_processSignalsTime; IntStat d_simplexConflicts; IntStat d_recentViolationCatches; TimerStat d_searchTime; ReferenceStat d_finalCheckPivotCounter; Statistics(uint32_t& pivots); } d_statistics; };/* class DualSimplexDecisionProcedure */ } // namespace arith } // namespace theory } // namespace cvc5