/******************************************************************************

 * 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 {

public:

 AttemptSolutionSDP(Env& env,

                    LinearEqualityModule& linEq,

                    ErrorSet& errors,

                    RaiseConflict conflictChannel,

                    TempVarMalloc tvmalloc);

 Result::Sat attempt(const ApproximateSimplex::Solution& sol);

private:

 bool matchesNewValue(const DenseMap<DeltaRational>& nv, ArithVar v) const;

 {

  }

  /** 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