sepa_rapidlearning.c

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/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
/*                                                                           */
/*                  This file is part of the program and library             */
/*         SCIP --- Solving Constraint Integer Programs                      */
/*                                                                           */
/*    Copyright (C) 2002-2019 Konrad-Zuse-Zentrum                            */
/*                            fuer Informationstechnik Berlin                */
/*                                                                           */
/*  SCIP is distributed under the terms of the ZIB Academic License.         */
/*                                                                           */
/*  You should have received a copy of the ZIB Academic License              */
/*  along with SCIP; see the file COPYING. If not visit scip.zib.de.         */
/*                                                                           */
/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */

/**@file   sepa_rapidlearning.c
 * @brief  rapidlearning separator
 * @author Timo Berthold
 */

/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/

#include <assert.h>
#ifndef NDEBUG
#include <string.h>
#endif

#include "scip/sepa_rapidlearning.h"
#include "scip/scipdefplugins.h"
#include "scip/pub_var.h"

#define SEPA_NAME              "rapidlearning"
#define SEPA_DESC               "rapid learning heuristic and separator"
#define SEPA_PRIORITY          -1200000
#define SEPA_FREQ                    -1
#define SEPA_MAXBOUNDDIST           1.0
#define SEPA_USESSUBSCIP           TRUE /**< does the separator use a secondary SCIP instance? */
#define SEPA_DELAY                FALSE /**< should separation method be delayed, if other separators found cuts? */

#define DEFAULT_APPLYCONFLICTS     TRUE /**< should the found conflicts be applied in the original SCIP?                 */
#define DEFAULT_APPLYBDCHGS        TRUE /**< should the found global bound deductions be applied in the original SCIP?
                                         *   apply only if conflicts and incumbent solution will be copied too
                                         */
#define DEFAULT_APPLYINFERVALS     TRUE /**< should the inference values be used as initialization in the original SCIP? */
#define DEFAULT_REDUCEDINFER      FALSE /**< should the inference values only be used when rapid learning found other reductions? */
#define DEFAULT_APPLYPRIMALSOL     TRUE /**< should the incumbent solution be copied to the original SCIP?               */
#define DEFAULT_APPLYSOLVED        TRUE /**< should a solved status be copied to the original SCIP?                      */

#define DEFAULT_MAXNVARS          10000 /**< maximum problem size (variables) for which rapid learning will be called */
#define DEFAULT_MAXNCONSS         10000 /**< maximum problem size (constraints) for which rapid learning will be called */

#define DEFAULT_MINNODES            500 /**< minimum number of nodes considered in rapid learning run */
#define DEFAULT_MAXNODES           5000 /**< maximum number of nodes considered in rapid learning run */

#define DEFAULT_CONTVARS          FALSE /**< should rapid learning be applied when there are continuous variables? */
#define DEFAULT_CONTVARSQUOT        0.3 /**< maximal portion of continuous variables to apply rapid learning       */
#define DEFAULT_LPITERQUOT          0.2 /**< maximal fraction of LP iterations compared to node LP iterations      */
#define DEFAULT_COPYCUTS           TRUE /**< should all active cuts from the cutpool of the
                                         *   original scip be copied to constraints of the subscip
                                         */


/*
 * Data structures
 */

/** separator data */
struct SCIP_SepaData
{
   SCIP_Bool             applyconflicts;     /**< should the found conflicts be applied in the original SCIP?                 */
   SCIP_Bool             applybdchgs;        /**< should the found global bound deductions be applied in the original SCIP?   */
   SCIP_Bool             applyinfervals;     /**< should the inference values be used as initialization in the original SCIP? */
   SCIP_Bool             reducedinfer;       /**< should the inference values only be used when rapid learning found other reductions? */
   SCIP_Bool             applyprimalsol;     /**< should the incumbent solution be copied to the original SCIP?               */
   SCIP_Bool             applysolved;        /**< should a solved status ba copied to the original SCIP?                      */
   int                   maxnvars;           /**< maximum problem size (variables) for which rapid learning will be called   */
   int                   maxnconss;          /**< maximum problem size (constraints) for which rapid learning will be called */
   int                   minnodes;           /**< minimum number of nodes considered in rapid learning run */
   int                   maxnodes;           /**< maximum number of nodes considered in rapid learning run */
   SCIP_Bool             contvars;           /**< should rapid learning be applied when there are continuous variables? */
   SCIP_Real             contvarsquot;       /**< maximal portion of continuous variables to apply rapid learning       */
   SCIP_Real             lpiterquot;         /**< maximal fraction of LP iterations compared to node LP iterations      */
   SCIP_Bool             copycuts;           /**< should all active cuts from cutpool be copied to constraints in
                                              *   subproblem?
                                              */
};

/** creates a new solution for the original problem by copying the solution of the subproblem */
static
SCIP_RETCODE createNewSol(
   SCIP*                 scip,               /**< original SCIP data structure                        */
   SCIP*                 subscip,            /**< SCIP structure of the subproblem                    */
   SCIP_VAR**            subvars,            /**< the variables of the subproblem                     */
   SCIP_HEUR*            heur,               /**< trysol heuristic structure                          */
   SCIP_SOL*             subsol,             /**< solution of the subproblem                          */
   SCIP_Bool*            success             /**< used to store whether new solution was found or not */
   )
{
   SCIP_VAR** vars;                          /* the original problem's variables                */
   int        nvars;
   SCIP_Real* subsolvals;                    /* solution values of the subproblem               */
   SCIP_SOL*  newsol;                        /* solution to be created for the original problem */

   assert( scip != NULL );
   assert( subscip != NULL );
   assert( subvars != NULL );
   assert( heur != NULL );
   assert( subsol != NULL );
   assert( success != NULL );

   /* get variables' data */
   SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
   /* sub-SCIP may have more variables than the number of active (transformed) variables in the main SCIP
    * since constraint copying may have required the copy of variables that are fixed in the main SCIP
    */
   assert(nvars <= SCIPgetNOrigVars(subscip));

   SCIP_CALL( SCIPallocBufferArray(scip, &subsolvals, nvars) );

   /* copy the solution */
   SCIP_CALL( SCIPgetSolVals(subscip, subsol, nvars, subvars, subsolvals) );

   /* create new solution for the original problem */
   SCIP_CALL( SCIPcreateSol(scip, &newsol, heur) );
   SCIP_CALL( SCIPsetSolVals(scip, newsol, nvars, vars, subsolvals) );

   /* check feasibility of new solution and pass it to trysol heuristic */
   SCIP_CALL( SCIPtrySolFree(scip, &newsol, FALSE, FALSE, TRUE, TRUE, TRUE, success) );

   SCIPfreeBufferArray(scip, &subsolvals);

   return SCIP_OKAY;
}

/*
 * Callback methods of separator
 */

/** copy method for separator plugins (called when SCIP copies plugins) */
static
SCIP_DECL_SEPACOPY(sepaCopyRapidlearning)
{  /*lint --e{715}*/
   assert(scip != NULL);
   assert(sepa != NULL);
   assert(strcmp(SCIPsepaGetName(sepa), SEPA_NAME) == 0);

   /* call inclusion method of constraint handler */
   SCIP_CALL( SCIPincludeSepaRapidlearning(scip) );

   return SCIP_OKAY;
}

/** destructor of separator to free user data (called when SCIP is exiting) */
static
SCIP_DECL_SEPAFREE(sepaFreeRapidlearning)
{  /*lint --e{715}*/
   SCIP_SEPADATA* sepadata;

   assert(sepa != NULL);
   assert(strcmp(SCIPsepaGetName(sepa), SEPA_NAME) == 0);
   assert(scip != NULL);

   /* free separator data */
   sepadata = SCIPsepaGetData(sepa);
   assert(sepadata != NULL);
   SCIPfreeBlockMemory(scip, &sepadata);
   SCIPsepaSetData(sepa, NULL);

   return SCIP_OKAY;
}


/** setup and solve sub-SCIP */
static
SCIP_RETCODE setupAndSolveSubscipRapidlearning(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP*                 subscip,            /**< subSCIP data structure */
   SCIP_SEPADATA*        sepadata,           /**< separator data */
   SCIP_RESULT*          result              /**< result pointer */
   )
{
   SCIP_VAR** vars;                          /* original problem's variables                   */
   SCIP_VAR** subvars;                       /* subproblem's variables                         */
   SCIP_HASHMAP* varmapfw;                   /* mapping of SCIP variables to sub-SCIP variables */
   SCIP_HASHMAP* varmapbw = NULL;            /* mapping of sub-SCIP variables to SCIP variables */

   SCIP_CONSHDLR** conshdlrs = NULL;         /* array of constraint handler's that might that might obtain conflicts */
   int* oldnconss = NULL;                    /* number of constraints without rapid learning conflicts               */

   SCIP_Longint nodelimit;                   /* node limit for the subproblem                  */

   int nconshdlrs;                           /* size of conshdlr and oldnconss array                      */
   int nfixedvars;                           /* number of variables that could be fixed by rapid learning */
   int nvars;                                /* number of variables                                       */
   int restartnum;                           /* maximal number of conflicts that should be created        */
   int i;                                    /* counter                                                   */

   SCIP_Bool success;                        /* was problem creation / copying constraint successful? */

   int nconflicts;                          /* statistic: number of conflicts applied         */
   int nbdchgs;                             /* statistic: number of bound changes applied     */
   int n1startinfers;                       /* statistic: number of one side infer values     */
   int n2startinfers;                       /* statistic: number of both side infer values    */

   SCIP_Bool soladded = FALSE;              /* statistic: was a new incumbent found?          */
   SCIP_Bool dualboundchg;                  /* statistic: was a new dual bound found?         */
   SCIP_Bool disabledualreductions;         /* TRUE, if dual reductions in sub-SCIP are not valid for original SCIP,
                                             * e.g., because a constraint could not be copied or a primal solution
                                             * could not be copied back
                                             */
   SCIP_Bool valid;

   SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );

    /* initializing the subproblem */
    SCIP_CALL( SCIPallocBufferArray(scip, &subvars, nvars) );
    SCIP_CALL( SCIPhashmapCreate(&varmapfw, SCIPblkmem(subscip), nvars) );
    valid = FALSE;

    /* copy the subproblem */
    SCIP_CALL( SCIPcopyConsCompression(scip, subscip, varmapfw, NULL, "rapid", NULL, NULL, 0, FALSE, FALSE, TRUE, &valid) );

    if( sepadata->copycuts )
    {
       /* copies all active cuts from cutpool of sourcescip to linear constraints in targetscip */
       SCIP_CALL( SCIPcopyCuts(scip, subscip, varmapfw, NULL, FALSE, NULL) );
    }

    for( i = 0; i < nvars; i++ )
    {
       subvars[i] = (SCIP_VAR*) SCIPhashmapGetImage(varmapfw, vars[i]);

       /* change implicit integer variables to integer type */
       if( SCIPvarGetType(subvars[i]) == SCIP_VARTYPE_IMPLINT )
       {
          SCIP_Bool infeasible;

          SCIP_CALL( SCIPchgVarType(subscip, subvars[i], SCIP_VARTYPE_INTEGER, &infeasible) );
          assert(!infeasible);
       }

       /* skip the heuristic when the sub-SCIP contains an integer variable with an infinite bound in direction of the
        * objective function; this might lead to very bad branching decisions when enforcing a pseudo solution (#1439)
        */
       if( SCIPvarGetType(subvars[i]) <= SCIP_VARTYPE_INTEGER )
       {
          SCIP_Real lb = SCIPvarGetLbLocal(subvars[i]);
          SCIP_Real ub = SCIPvarGetUbLocal(subvars[i]);
          SCIP_Real obj = SCIPvarGetObj(subvars[i]);

          if( (SCIPisNegative(subscip, obj) && SCIPisInfinity(subscip, ub))
             || (SCIPisPositive(subscip, obj) && SCIPisInfinity(subscip, -lb)) )
          {
             /* free local hash map */
             SCIPhashmapFree(&varmapfw);

             SCIPdebugMsg(scip, "unbounded integer variable %s (in [%g,%g]) with objective %g -> skip heuristic\n",
                SCIPvarGetName(subvars[i]), lb, ub, obj);
             goto TERMINATE;
          }
       }
    }

    SCIPhashmapFree(&varmapfw);

    /* This avoids dual presolving.
     *
     * If the copy is not valid, it should be a relaxation of the problem (constraints might have failed to be copied,
     * but no variables should be missing because we stop earlier anyway if pricers are present).
     * By disabling dual presolving, conflicts found in a relaxation are still valid for the original problem.
     */
    if( ! valid )
    {
       for( i = 0; i < nvars; i++ )
       {
          SCIP_CALL( SCIPaddVarLocksType(subscip, subvars[i], SCIP_LOCKTYPE_MODEL, 1, 1 ) );
       }
    }

    SCIPdebugMsg(scip, "Copying SCIP was%s valid.\n", valid ? "" : " not");

    /* mimic an FD solver: DFS, no LP solving, 1-FUIP instead of all-FUIP */
    if( SCIPisParamFixed(subscip, "lp/solvefreq") )
    {
       SCIPwarningMessage(scip, "unfixing parameter lp/solvefreq in subscip of rapidlearning\n");
       SCIP_CALL( SCIPunfixParam(subscip, "lp/solvefreq") );
    }
    SCIP_CALL( SCIPsetIntParam(subscip, "lp/solvefreq", -1) );
    if( !SCIPisParamFixed(subscip, "conflict/fuiplevels") )
    {
       SCIP_CALL( SCIPsetIntParam(subscip, "conflict/fuiplevels", 1) );
    }
    if( SCIPisParamFixed(subscip, "nodeselection/dfs/stdpriority") )
    {
       SCIPwarningMessage(scip, "unfixing parameter nodeselection/dfs/stdpriority in subscip of rapidlearning\n");
       SCIP_CALL( SCIPunfixParam(subscip, "nodeselection/dfs/stdpriority") );
    }
    SCIP_CALL( SCIPsetIntParam(subscip, "nodeselection/dfs/stdpriority", INT_MAX/4) );

    if( !SCIPisParamFixed(subscip, "propagating/pseudoobj/freq") )
    {
       SCIP_CALL( SCIPsetIntParam(subscip, "propagating/pseudoobj/freq", -1) );
    }
    if( !SCIPisParamFixed(subscip, "constraints/disableenfops") )
    {
       SCIP_CALL( SCIPsetBoolParam(subscip, "constraints/disableenfops", TRUE) );
    }

    /* use inference branching */
    if( !SCIPisParamFixed(subscip, "branching/inference/useweightedsum") )
    {
       SCIP_CALL( SCIPsetBoolParam(subscip, "branching/inference/useweightedsum", FALSE) );
    }

    /* only create short conflicts */
    if( !SCIPisParamFixed(subscip, "conflict/maxvarsfac") )
    {
       SCIP_CALL( SCIPsetRealParam(subscip, "conflict/maxvarsfac", 0.05) );
    }

    /* set limits for the subproblem */
    nodelimit = SCIPgetNLPIterations(scip);
    nodelimit = MAX(sepadata->minnodes, nodelimit);
    nodelimit = MIN(sepadata->maxnodes, nodelimit);

    restartnum = 1000;

 #ifdef SCIP_DEBUG
    /* for debugging, enable full output */
    SCIP_CALL( SCIPsetIntParam(subscip, "display/verblevel", 5) );
    SCIP_CALL( SCIPsetIntParam(subscip, "display/freq", 100000000) );
 #else
    /* disable statistic timing inside sub SCIP and output to console */
    SCIP_CALL( SCIPsetIntParam(subscip, "display/verblevel", 0) );
    SCIP_CALL( SCIPsetBoolParam(subscip, "timing/statistictiming", FALSE) );
 #endif

    /* set limits for the subproblem */
    SCIP_CALL( SCIPcopyLimits(scip, subscip) );
    SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", nodelimit/5) );
    SCIP_CALL( SCIPsetIntParam(subscip, "limits/restarts", 0) );
    SCIP_CALL( SCIPsetIntParam(subscip, "conflict/restartnum", restartnum) );

    /* forbid recursive call of heuristics and separators solving subMIPs */
    SCIP_CALL( SCIPsetSubscipsOff(subscip, TRUE) );

    /* disable cutting plane separation */
    SCIP_CALL( SCIPsetSeparating(subscip, SCIP_PARAMSETTING_OFF, TRUE) );

    /* disable expensive presolving */
    SCIP_CALL( SCIPsetPresolving(subscip, SCIP_PARAMSETTING_FAST, TRUE) );

    /* do not abort subproblem on CTRL-C */
    SCIP_CALL( SCIPsetBoolParam(subscip, "misc/catchctrlc", FALSE) );

    /* add an objective cutoff */
    SCIP_CALL( SCIPsetObjlimit(subscip, SCIPgetUpperbound(scip)) );

    /* create the variable mapping hash map */
    SCIP_CALL( SCIPhashmapCreate(&varmapbw, SCIPblkmem(scip), nvars) );

    /* store reversing mapping of variables */
    SCIP_CALL( SCIPtransformProb(subscip) );
    for( i = 0; i < nvars; ++i)
    {
       SCIP_CALL( SCIPhashmapInsert(varmapbw, SCIPvarGetTransVar(subvars[i]), vars[i]) );
    }

    /* allocate memory for constraints storage. Each constraint that will be created from now on will be a conflict.
     * Therefore, we need to remember oldnconss to get the conflicts from the FD search.
     */
    nconshdlrs = 4;
    SCIP_CALL( SCIPallocBufferArray(scip, &conshdlrs, nconshdlrs) );
    SCIP_CALL( SCIPallocBufferArray(scip, &oldnconss, nconshdlrs) );

    /* store number of constraints before rapid learning search */
    conshdlrs[0] = SCIPfindConshdlr(subscip, "bounddisjunction");
    conshdlrs[1] = SCIPfindConshdlr(subscip, "setppc");
    conshdlrs[2] = SCIPfindConshdlr(subscip, "linear");
    conshdlrs[3] = SCIPfindConshdlr(subscip, "logicor");

    /* redundant constraints might be eliminated in presolving */
    SCIP_CALL( SCIPpresolve(subscip));

    for( i = 0; i < nconshdlrs; ++i)
    {
       if( conshdlrs[i] != NULL )
          oldnconss[i] = SCIPconshdlrGetNConss(conshdlrs[i]);
    }

    nfixedvars = SCIPgetNFixedVars(scip);

    /* solve the subproblem, abort after errors in debug mode */
    SCIP_CALL_ABORT( SCIPsolve(subscip) );

    /* if problem was already solved do not increase limits to run again */
    if( SCIPgetStage(subscip) == SCIP_STAGE_SOLVED )
    {
       SCIPdebugMsg(scip, "Subscip was completely solved, status %d.\n", SCIPgetStatus(subscip));
    }
    /* abort solving, if limit of applied conflicts is reached */
    else if( SCIPgetNConflictConssApplied(subscip) >= restartnum )
    {
       SCIPdebugMsg(scip, "finish after %" SCIP_LONGINT_FORMAT " successful conflict calls.\n", SCIPgetNConflictConssApplied(subscip));
    }
    /* if the first 20% of the solution process were successful, proceed */
    else if( (sepadata->applyprimalsol && SCIPgetNSols(subscip) > 0 && SCIPisFeasLT(scip, SCIPgetUpperbound(subscip), SCIPgetUpperbound(scip) ) )
       || (sepadata->applybdchgs && SCIPgetNFixedVars(subscip) > nfixedvars)
       || (sepadata->applyconflicts && SCIPgetNConflictConssApplied(subscip) > 0) )
    {
       SCIPdebugMsg(scip, "proceed solving after the first 20%% of the solution process, since:\n");

       if( SCIPgetNSols(subscip) > 0 && SCIPisFeasLE(scip, SCIPgetUpperbound(subscip), SCIPgetUpperbound(scip) ) )
       {
          SCIPdebugMsg(scip, "   - there was a better solution (%f < %f)\n",SCIPgetUpperbound(subscip), SCIPgetUpperbound(scip));
       }
       if( SCIPgetNFixedVars(subscip) > nfixedvars )
       {
          SCIPdebugMsg(scip, "   - there were %d variables fixed\n", SCIPgetNFixedVars(scip)-nfixedvars );
       }
       if( SCIPgetNConflictConssFound(subscip) > 0 )
       {
          SCIPdebugMsg(scip, "   - there were %" SCIP_LONGINT_FORMAT " conflict constraints created\n", SCIPgetNConflictConssApplied(subscip));
       }

       /* set node limit to 100% */
       SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", nodelimit) );

       /* solve the subproblem, abort after errors in debug mode */
        SCIP_CALL_ABORT( SCIPsolve(subscip) );
    }
    else
    {
       SCIPdebugMsg(scip, "do not proceed solving after the first 20%% of the solution process.\n");
    }

 #ifdef SCIP_DEBUG
    SCIP_CALL( SCIPprintStatistics(subscip, NULL) );
 #endif

    disabledualreductions = FALSE;

    /* check, whether a solution was found */
    if( sepadata->applyprimalsol && SCIPgetNSols(subscip) > 0 && SCIPfindHeur(scip, "trysol") != NULL )
    {
       SCIP_HEUR* heurtrysol;
       SCIP_SOL** subsols;
       int nsubsols;

       /* check, whether a solution was found;
        * due to numerics, it might happen that not all solutions are feasible -> try all solutions until was declared to be feasible
        */
       nsubsols = SCIPgetNSols(subscip);
       subsols = SCIPgetSols(subscip);
       soladded = FALSE;
       heurtrysol = SCIPfindHeur(scip, "trysol");

       /* sequentially add solutions to trysol heuristic */
       for( i = 0; i < nsubsols && !soladded; ++i )
       {
          SCIPdebugMsg(scip, "Try to create new solution by copying subscip solution.\n");
          SCIP_CALL( createNewSol(scip, subscip, subvars, heurtrysol, subsols[i], &soladded) );
       }
       if( !soladded || !SCIPisEQ(scip, SCIPgetSolOrigObj(subscip, subsols[i-1]), SCIPgetSolOrigObj(subscip, subsols[0])) )
          disabledualreductions = TRUE;
    }

    /* if the sub problem was solved completely, we update the dual bound */
    dualboundchg = FALSE;
    if( sepadata->applysolved && !disabledualreductions
       && (SCIPgetStatus(subscip) == SCIP_STATUS_OPTIMAL || SCIPgetStatus(subscip) == SCIP_STATUS_INFEASIBLE) )
    {
       /* we need to multiply the dualbound with the scaling factor and add the offset,
        * because this information has been disregarded in the sub-SCIP
        */
       SCIPdebugMsg(scip, "Update old dualbound %g to new dualbound %g.\n", SCIPgetDualbound(scip), SCIPretransformObj(scip, SCIPgetDualbound(subscip)));

       SCIP_CALL( SCIPupdateLocalDualbound(scip, SCIPretransformObj(scip, SCIPgetDualbound(subscip))) );
       dualboundchg = TRUE;
    }

    /* check, whether conflicts were created */
    nconflicts = 0;
    if( sepadata->applyconflicts && !disabledualreductions && SCIPgetNConflictConssApplied(subscip) > 0 )
    {
       SCIP_HASHMAP* consmap;
       int hashtablesize;

       assert(SCIPgetNConflictConssApplied(subscip) < (SCIP_Longint) INT_MAX);
       hashtablesize = (int) SCIPgetNConflictConssApplied(subscip);
       assert(hashtablesize < INT_MAX/5);

       /* create the variable mapping hash map */
       SCIP_CALL( SCIPhashmapCreate(&consmap, SCIPblkmem(scip), hashtablesize) );

       /* loop over all constraint handlers that might contain conflict constraints */
       for( i = 0; i < nconshdlrs; ++i)
       {
          /* copy constraints that have been created in FD run */
          if( conshdlrs[i] != NULL && SCIPconshdlrGetNConss(conshdlrs[i]) > oldnconss[i] )
          {
             SCIP_CONS** conss;
             int c;
             int nconss;

             nconss = SCIPconshdlrGetNConss(conshdlrs[i]);
             conss = SCIPconshdlrGetConss(conshdlrs[i]);

             /* loop over all constraints that have been added in sub-SCIP run, these are the conflicts */
             for( c = oldnconss[i]; c < nconss; ++c)
             {
                SCIP_CONS* cons;
                SCIP_CONS* conscopy;

                cons = conss[c];
                assert(cons != NULL);

                success = FALSE;

                /* @todo assert that flags are as they should be for conflicts */
                SCIP_CALL( SCIPgetConsCopy(subscip, scip, cons, &conscopy, conshdlrs[i], varmapbw, consmap, NULL,
                      SCIPconsIsInitial(cons), SCIPconsIsSeparated(cons), SCIPconsIsEnforced(cons), SCIPconsIsChecked(cons),
                      SCIPconsIsPropagated(cons), TRUE, FALSE, SCIPconsIsDynamic(cons),
                      SCIPconsIsRemovable(cons), FALSE, TRUE, &success) );

                if( success )
                {
                   nconflicts++;
                   SCIP_CALL( SCIPaddCons(scip, conscopy) );
                   SCIP_CALL( SCIPreleaseCons(scip, &conscopy) );
                }
                else
                {
                   SCIPdebugMsg(scip, "failed to copy conflict constraint %s back to original SCIP\n", SCIPconsGetName(cons));
                }
             }
          }
       }
       SCIPhashmapFree(&consmap);
    }

    /* check, whether tighter global bounds were detected */
    nbdchgs = 0;
    if( sepadata->applybdchgs && !disabledualreductions )
       for( i = 0; i < nvars; ++i )
       {
          SCIP_Bool infeasible;
          SCIP_Bool tightened;

          assert(SCIPisLE(scip, SCIPvarGetLbGlobal(vars[i]), SCIPvarGetLbGlobal(subvars[i])));
          assert(SCIPisLE(scip, SCIPvarGetLbGlobal(subvars[i]), SCIPvarGetUbGlobal(subvars[i])));
          assert(SCIPisLE(scip, SCIPvarGetUbGlobal(subvars[i]), SCIPvarGetUbGlobal(vars[i])));

          /* update the bounds of the original SCIP, if a better bound was proven in the sub-SCIP */
          /* @todo handle infeasible pointer? can it be set to TRUE? */
          SCIP_CALL( SCIPtightenVarUb(scip, vars[i], SCIPvarGetUbGlobal(subvars[i]), FALSE, &infeasible, &tightened) );
          if( tightened )
             nbdchgs++;

          SCIP_CALL( SCIPtightenVarLb(scip, vars[i], SCIPvarGetLbGlobal(subvars[i]), FALSE, &infeasible, &tightened) );
          if( tightened )
             nbdchgs++;
       }

    n1startinfers = 0;
    n2startinfers = 0;

    /* install start values for inference branching */
    /* @todo use different nbranching counters for pseudo cost and inference values and update inference values in the tree */
    if( sepadata->applyinfervals && SCIPgetDepth(scip) == 0 && (!sepadata->reducedinfer || soladded || nbdchgs+nconflicts > 0) )
    {
       for( i = 0; i < nvars; ++i )
       {
          SCIP_Real downinfer;
          SCIP_Real upinfer;
          SCIP_Real downvsids;
          SCIP_Real upvsids;
          SCIP_Real downconflen;
          SCIP_Real upconflen;

          /* copy downwards branching statistics */
          downvsids = SCIPgetVarVSIDS(subscip, subvars[i], SCIP_BRANCHDIR_DOWNWARDS);
          downconflen = SCIPgetVarAvgConflictlength(subscip, subvars[i], SCIP_BRANCHDIR_DOWNWARDS);
          downinfer = SCIPgetVarAvgInferences(subscip, subvars[i], SCIP_BRANCHDIR_DOWNWARDS);

          /* copy upwards branching statistics */
          upvsids = SCIPgetVarVSIDS(subscip, subvars[i], SCIP_BRANCHDIR_UPWARDS);
          upconflen = SCIPgetVarAvgConflictlength(subscip, subvars[i], SCIP_BRANCHDIR_UPWARDS);
          upinfer = SCIPgetVarAvgInferences(subscip, subvars[i], SCIP_BRANCHDIR_UPWARDS);

          /* memorize statistics */
          if( downinfer+downconflen+downvsids > 0.0 || upinfer+upconflen+upvsids != 0 )
             n1startinfers++;

          if( downinfer+downconflen+downvsids > 0.0 && upinfer+upconflen+upvsids != 0 )
             n2startinfers++;

          SCIP_CALL( SCIPinitVarBranchStats(scip, vars[i], 0.0, 0.0, downvsids, upvsids, downconflen, upconflen, downinfer, upinfer, 0.0, 0.0) );
       }
    }

    SCIPdebugMsg(scip, "Rapidlearning added %d conflicts, changed %d bounds, %s primal solution, %s dual bound improvement.\n",
       nconflicts, nbdchgs, soladded ? "found" : "no",  dualboundchg ? "found" : "no");

    SCIPdebugMsg(scip, "YYY Infervalues initialized on one side: %5.2f %% of variables, %5.2f %% on both sides\n",
       100.0 * n1startinfers/(SCIP_Real)nvars, 100.0 * n2startinfers/(SCIP_Real)nvars);

    /* change result pointer */
    if( nconflicts > 0 || dualboundchg )
       *result = SCIP_CONSADDED;
    else if( nbdchgs > 0 )
       *result = SCIP_REDUCEDDOM;

    /* free local data */
    assert(oldnconss != NULL);
    assert(conshdlrs != NULL);
    assert(varmapbw != NULL);
    SCIPfreeBufferArray(scip, &oldnconss);
    SCIPfreeBufferArray(scip, &conshdlrs);
    SCIPhashmapFree(&varmapbw);

  TERMINATE:

   /* we are in SCIP_STAGE_SOLVED, so we need to free the transformed problem before releasing the locks */
   SCIP_CALL( SCIPfreeTransform(subscip) );

   /* remove all locks that were added to avoid dual presolving */
   for( i = 0; i < nvars; i++ )
   {
      SCIP_CALL( SCIPaddVarLocksType(subscip, subvars[i], SCIP_LOCKTYPE_MODEL, -1, -1 ) );
   }

   /* free subproblem */
   SCIPfreeBufferArray(scip, &subvars);

   return SCIP_OKAY;
}

/** LP solution separation method of separator */
static
SCIP_DECL_SEPAEXECLP(sepaExeclpRapidlearning)
{/*lint --e{715}*/
   SCIP* subscip;                            /* the subproblem created by rapid learning       */
   SCIP_SEPADATA* sepadata;                  /* separator's private data                       */
   int ndiscvars;
   SCIP_Bool success;
   SCIP_RETCODE retcode;

   assert(sepa != NULL);
   assert(scip != NULL);
   assert(result != NULL);

   *result = SCIP_DIDNOTRUN;

   ndiscvars = SCIPgetNBinVars(scip) + SCIPgetNIntVars(scip) + SCIPgetNImplVars(scip);

   /* only run when still not fixed binary variables exists */
   if( ndiscvars == 0 )
      return SCIP_OKAY;

   /* get separator's data */
   sepadata = SCIPsepaGetData(sepa);
   assert(sepadata != NULL);

   /* only run for integer programs */
   if( !sepadata->contvars && ndiscvars != SCIPgetNVars(scip) )
      return SCIP_OKAY;

   /* only run if there are few enough continuous variables */
   if( sepadata->contvars && SCIPgetNContVars(scip) > sepadata->contvarsquot * SCIPgetNVars(scip) )
      return SCIP_OKAY;

   /* do not run if pricers are present */
   if( SCIPgetNActivePricers(scip) > 0 )
      return SCIP_OKAY;

   /* if the separator should be exclusive to the root node, this prevents multiple calls due to restarts */
   if( SCIPsepaGetFreq(sepa) == 0 && SCIPsepaGetNCalls(sepa) > 0 )
      return SCIP_OKAY;

   /* call separator at most once per node */
   if( SCIPsepaGetNCallsAtNode(sepa) > 0 )
      return SCIP_OKAY;

   /* do not call rapid learning, if the problem is too big */
   if( SCIPgetNVars(scip) > sepadata->maxnvars || SCIPgetNConss(scip) > sepadata->maxnconss )
      return SCIP_OKAY;

   if( SCIPisStopped(scip) )
      return SCIP_OKAY;

   /* check whether there is enough time and memory left */
   SCIP_CALL( SCIPcheckCopyLimits(scip, &success) );

   if( !success)
      return SCIP_OKAY;

   *result = SCIP_DIDNOTFIND;

   SCIP_CALL( SCIPcreate(&subscip) );

   retcode = setupAndSolveSubscipRapidlearning(scip, subscip, sepadata, result);

   SCIP_CALL( SCIPfree(&subscip) );

   return retcode;
}


/*
 * separator specific interface methods
 */

/** creates the rapidlearning separator and includes it in SCIP */
SCIP_RETCODE SCIPincludeSepaRapidlearning(
   SCIP*                 scip                /**< SCIP data structure */
   )
{
   SCIP_SEPADATA* sepadata;
   SCIP_SEPA* sepa;

   /* create rapidlearning separator data */
   SCIP_CALL( SCIPallocBlockMemory(scip, &sepadata) );

   /* include separator */
   SCIP_CALL( SCIPincludeSepaBasic(scip, &sepa, SEPA_NAME, SEPA_DESC, SEPA_PRIORITY, SEPA_FREQ, SEPA_MAXBOUNDDIST,
         SEPA_USESSUBSCIP, SEPA_DELAY,
         sepaExeclpRapidlearning, NULL,
         sepadata) );

   assert(sepa != NULL);

   /* set non-NULL pointers to callback methods */
   SCIP_CALL( SCIPsetSepaCopy(scip, sepa, sepaCopyRapidlearning) );
   SCIP_CALL( SCIPsetSepaFree(scip, sepa, sepaFreeRapidlearning) );

   /* add rapidlearning separator parameters */
   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/applyconflicts",
         "should the found conflicts be applied in the original SCIP?",
         &sepadata->applyconflicts, TRUE, DEFAULT_APPLYCONFLICTS, NULL, NULL) );

   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/applybdchgs",
         "should the found global bound deductions be applied in the original SCIP?",
         &sepadata->applybdchgs, TRUE, DEFAULT_APPLYBDCHGS, NULL, NULL) );

   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/applyinfervals",
         "should the inference values be used as initialization in the original SCIP?",
         &sepadata->applyinfervals, TRUE, DEFAULT_APPLYINFERVALS, NULL, NULL) );

   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/reducedinfer",
         "should the inference values only be used when " SEPA_NAME " found other reductions?",
         &sepadata->reducedinfer, TRUE, DEFAULT_REDUCEDINFER, NULL, NULL) );

   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/applyprimalsol",
         "should the incumbent solution be copied to the original SCIP?",
         &sepadata->applyprimalsol, TRUE, DEFAULT_APPLYPRIMALSOL, NULL, NULL) );

   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/applysolved",
         "should a solved status be copied to the original SCIP?",
         &sepadata->applysolved, TRUE, DEFAULT_APPLYSOLVED, NULL, NULL) );

   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/contvars",
         "should rapid learning be applied when there are continuous variables?",
         &sepadata->contvars, TRUE, DEFAULT_CONTVARS, NULL, NULL) );

   SCIP_CALL( SCIPaddRealParam(scip, "separating/" SEPA_NAME "/contvarsquot",
         "maximal portion of continuous variables to apply rapid learning",
         &sepadata->contvarsquot, TRUE, DEFAULT_CONTVARSQUOT, 0.0, 1.0, NULL, NULL) );

   SCIP_CALL( SCIPaddRealParam(scip, "separating/" SEPA_NAME "/lpiterquot",
         "maximal fraction of LP iterations compared to node LP iterations",
         &sepadata->lpiterquot, TRUE, DEFAULT_LPITERQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );

   SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxnvars",
         "maximum problem size (variables) for which rapid learning will be called",
         &sepadata->maxnvars, TRUE, DEFAULT_MAXNVARS, 0, INT_MAX, NULL, NULL) );

   SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxnconss",
         "maximum problem size (constraints) for which rapid learning will be called",
         &sepadata->maxnconss, TRUE, DEFAULT_MAXNCONSS, 0, INT_MAX, NULL, NULL) );

   SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxnodes",
         "maximum number of nodes considered in rapid learning run",
         &sepadata->maxnodes, TRUE, DEFAULT_MAXNODES, 0, INT_MAX, NULL, NULL) );

   SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/minnodes",
         "minimum number of nodes considered in rapid learning run",
         &sepadata->minnodes, TRUE, DEFAULT_MINNODES, 0, INT_MAX, NULL, NULL) );

   SCIP_CALL( SCIPaddBoolParam(scip, "separating/" SEPA_NAME "/copycuts",
         "should all active cuts from cutpool be copied to constraints in subproblem?",
         &sepadata->copycuts, TRUE, DEFAULT_COPYCUTS, NULL, NULL) );

   return SCIP_OKAY;
}