sepa_cmir.c

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/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
/*                                                                           */
/*                  This file is part of the program and library             */
/*         SCIP --- Solving Constraint Integer Programs                      */
/*                                                                           */
/*    Copyright (C) 2002-2015 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 email to scip@zib.de.      */
/*                                                                           */
/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */

/**@file   sepa_cmir.c
 * @brief  complemented mixed integer rounding cuts separator (Marchand's version)
 * @author Kati Wolter
 * @author Tobias Achterberg
 */

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

#include <assert.h>
#include <string.h>

#include "scip/sepa_cmir.h"
#include "scip/pub_misc.h"


#define SEPA_NAME              "cmir"
#define SEPA_DESC              "complemented mixed integer rounding cuts separator (Marchand's version)"
#define SEPA_PRIORITY             -3000
#define SEPA_FREQ                     0
#define SEPA_MAXBOUNDDIST           0.0
#define SEPA_USESSUBSCIP          FALSE /**< does the separator use a secondary SCIP instance? */
#define SEPA_DELAY                FALSE /**< should separation method be delayed, if other separators found cuts? */

#define DEFAULT_MAXROUNDS             3 /**< maximal number of cmir separation rounds per node (-1: unlimited) */
#define DEFAULT_MAXROUNDSROOT        10 /**< maximal number of cmir separation rounds in the root node (-1: unlimited) */
#define DEFAULT_MAXTRIES            100 /**< maximal number of rows to start aggregation with per separation round
                                         *   (-1: unlimited) */
#define DEFAULT_MAXTRIESROOT         -1 /**< maximal number of rows to start aggregation with per round in the root node
                                         *   (-1: unlimited) */
#define DEFAULT_MAXFAILS             20 /**< maximal number of consecutive unsuccessful aggregation tries (-1: unlimited) */
#define DEFAULT_MAXFAILSROOT        100 /**< maximal number of consecutive unsuccessful aggregation tries in the root node
                                         *   (-1: unlimited) */
#define DEFAULT_MAXAGGRS              3 /**< maximal number of aggregations for each row per separation round */
#define DEFAULT_MAXAGGRSROOT          6 /**< maximal number of aggregations for each row per round in the root node */
#define DEFAULT_MAXSEPACUTS         100 /**< maximal number of cmir cuts separated per separation round */
#define DEFAULT_MAXSEPACUTSROOT     500 /**< maximal number of cmir cuts separated per separation round in root node */
#define DEFAULT_MAXSLACK            0.0 /**< maximal slack of rows to be used in aggregation */
#define DEFAULT_MAXSLACKROOT        0.1 /**< maximal slack of rows to be used in aggregation in the root node */
#define DEFAULT_DENSITYSCORE      1e-04 /**< weight of row density in the aggregation scoring of the rows */
#define DEFAULT_SLACKSCORE        1e-03 /**< weight of slack in the aggregation scoring of the rows */
#define DEFAULT_MAXAGGDENSITY      0.20 /**< maximal density of aggregated row */
#define DEFAULT_MAXROWDENSITY      0.05 /**< maximal density of row to be used in aggregation */
#define DEFAULT_DENSITYOFFSET       100 /**< additional number of variables allowed in row on top of density */
#define DEFAULT_MAXROWFAC          1e+4 /**< maximal row aggregation factor */
#define DEFAULT_MAXTESTDELTA         -1 /**< maximal number of different deltas to try (-1: unlimited) */
#define DEFAULT_MAXCONTS             10 /**< maximal number of active continuous variables in aggregated row */
#define DEFAULT_MAXCONTSROOT         10 /**< maximal number of active continuous variables in aggregated row in the root */
#define DEFAULT_AGGRTOL             0.1 /**< aggregation heuristic: tolerance for bound distances used to select real 
                                         *   variable in current aggregated constraint to be eliminated */
#define DEFAULT_TRYNEGSCALING      TRUE /**< should negative values also be tested in scaling? */
#define DEFAULT_FIXINTEGRALRHS     TRUE /**< should an additional variable be complemented if f0 = 0? */
#define DEFAULT_DYNAMICCUTS        TRUE /**< should generated cuts be removed from the LP if they are no longer tight? */

#define BOUNDSWITCH                 0.5
#define USEVBDS                    TRUE
#define ALLOWLOCAL                 TRUE
#define MINFRAC                    0.05
#define MAXFRAC                    0.999
#define MAKECONTINTEGRAL          FALSE
#define IMPLINTSARECONT

#define MAXAGGRLEN(nvars)          (0.1*(nvars)+1000) /**< maximal length of base inequality */


/*
 * Data structures
 */

/** separator data */
struct SCIP_SepaData
{
   SCIP_Real             maxslack;           /**< maximal slack of rows to be used in aggregation */
   SCIP_Real             maxslackroot;       /**< maximal slack of rows to be used in aggregation in the root node */
   SCIP_Real             densityscore;       /**< weight of row density in the aggregation scoring of the rows */
   SCIP_Real             slackscore;         /**< weight of slack in the aggregation scoring of the rows */
   SCIP_Real             maxaggdensity;      /**< maximal density of aggregated row */
   SCIP_Real             maxrowdensity;      /**< maximal density of row to be used in aggregation */
   SCIP_Real             maxrowfac;          /**< maximal row aggregation factor */
   SCIP_Real             aggrtol;            /**< tolerance for bound distance used in aggregation heuristic */
   int                   maxrounds;          /**< maximal number of cmir separation rounds per node (-1: unlimited) */
   int                   maxroundsroot;      /**< maximal number of cmir separation rounds in the root node (-1: unlimited) */
   int                   maxtries;           /**< maximal number of rows to start aggregation with per separation round
                                              *   (-1: unlimited) */
   int                   maxtriesroot;       /**< maximal number of rows to start aggregation with per round in the root node
                                              *   (-1: unlimited) */
   int                   maxfails;           /**< maximal number of consecutive unsuccessful aggregation tries
                                              *   (-1: unlimited) */
   int                   maxfailsroot;       /**< maximal number of consecutive unsuccessful aggregation tries in the root
                                              *   node (-1: unlimited) */
   int                   maxaggrs;           /**< maximal number of aggregations for each row per separation round */
   int                   maxaggrsroot;       /**< maximal number of aggregations for each row per round in the root node */
   int                   maxsepacuts;        /**< maximal number of cmir cuts separated per separation round */
   int                   maxsepacutsroot;    /**< maximal number of cmir cuts separated per separation round in root node */
   int                   densityoffset;      /**< additional number of variables allowed in row on top of density */
   int                   maxtestdelta;       /**< maximal number of different deltas to try (-1: unlimited) */
   int                   maxconts;           /**< maximal number of active continuous variables in aggregated row */
   int                   maxcontsroot;       /**< maximal number of active continuous variables in aggregated row in the root */
   SCIP_Bool             trynegscaling;      /**< should negative values also be tested in scaling? */
   SCIP_Bool             fixintegralrhs;     /**< should an additional variable be complemented if f0 = 0? */
   SCIP_Bool             dynamiccuts;        /**< should generated cuts be removed from the LP if they are no longer tight? */
};


/*
 * Local methods
 */

/** stores nonzero elements of dense coefficient vector as sparse vector, and calculates activity and norm */
static
SCIP_RETCODE storeCutInArrays(
   SCIP*                 scip,               /**< SCIP data structure */
   int                   nvars,              /**< number of problem variables */
   SCIP_VAR**            vars,               /**< problem variables */
   SCIP_Real*            cutcoefs,           /**< dense coefficient vector */
   SCIP_Real*            varsolvals,         /**< dense variable LP solution vector */
   SCIP_VAR**            cutvars,            /**< array to store variables of sparse cut vector */
   SCIP_Real*            cutvals,            /**< array to store coefficients of sparse cut vector */
   int*                  cutlen,             /**< pointer to store number of nonzero entries in cut */
   SCIP_Real*            cutact              /**< pointer to store activity of cut */
   )
{
   SCIP_Real act;
   int len;
   int v;

   assert(nvars == 0 || cutcoefs != NULL);
   assert(nvars == 0 || varsolvals != NULL);
   assert(cutvars != NULL);
   assert(cutvals != NULL);
   assert(cutlen != NULL);
   assert(cutact != NULL);

   len = 0;
   act = 0.0;
   for( v = 0; v < nvars; ++v )
   {
      SCIP_Real val;

      val = cutcoefs[v];
      if( !SCIPisZero(scip, val) )
      {
         act += val * varsolvals[v];
         cutvars[len] = vars[v];
         cutvals[len] = val;
         len++;
      }
   }

   *cutlen = len;
   *cutact = act;

   return SCIP_OKAY;
}

/** adds given cut to LP if violated */
static
SCIP_RETCODE addCut(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_SEPA*            sepa,               /**< separator */
   SCIP_SOL*             sol,                /**< the solution that should be separated, or NULL for LP solution */
   SCIP_Real*            varsolvals,         /**< solution values of active variables */
   SCIP_Real*            cutcoefs,           /**< coefficients of active variables in cut */
   SCIP_Real             cutrhs,             /**< right hand side of cut */
   SCIP_Bool             cutislocal,         /**< is the cut only locally valid? */
   SCIP_Bool             cutremovable,       /**< should the cut be removed from the LP due to aging or cleanup? */
   int                   cutrank,            /**< rank of the cut */
   const char*           cutclassname,       /**< name of cut class to use for row names */
   SCIP_Bool*            cutoff,             /**< whether a cutoff has been detected */
   int*                  ncuts               /**< pointer to count the number of added cuts */
   )
{
   SCIP_VAR** vars;
   SCIP_VAR** cutvars;
   SCIP_Real* cutvals;
   SCIP_Real cutact;
   int nvars;
   int cutlen;

   assert(scip != NULL);
   assert(varsolvals != NULL);
   assert(cutcoefs != NULL);
   assert(cutoff != NULL);
   assert(ncuts != NULL);

   *cutoff = FALSE;

   /* get active problem variables */
   SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
   assert(nvars == 0 || vars != NULL);

   /* get temporary memory for storing the cut as sparse row */
   SCIP_CALL( SCIPallocBufferArray(scip, &cutvars, nvars) );
   SCIP_CALL( SCIPallocBufferArray(scip, &cutvals, nvars) );

   /* store the cut as sparse row, calculate activity and norm of cut */
   SCIP_CALL( storeCutInArrays(scip, nvars, vars, cutcoefs, varsolvals,
         cutvars, cutvals, &cutlen, &cutact) );

   if( cutlen > 0 )
   {
      SCIP_Real cutnorm;

      cutnorm = SCIPgetVectorEfficacyNorm(scip, cutvals, cutlen);
      if( SCIPisPositive(scip, cutnorm) && SCIPisEfficacious(scip, (cutact - cutrhs)/cutnorm) )
      {
         SCIP_ROW* cut;
         char cutname[SCIP_MAXSTRLEN];
         SCIP_Bool success;

         /* create the cut */
         (void) SCIPsnprintf(cutname, SCIP_MAXSTRLEN, "%s%d_%d", cutclassname, SCIPgetNLPs(scip), *ncuts);
         SCIP_CALL( SCIPcreateEmptyRowSepa(scip, &cut, sepa, cutname, -SCIPinfinity(scip), cutrhs, 
               cutislocal, FALSE, cutremovable) );
         SCIP_CALL( SCIPaddVarsToRow(scip, cut, cutlen, cutvars, cutvals) );

         /* set cut rank */
         SCIProwChgRank(cut, cutrank);

         SCIPdebugMessage(" -> found potential %s cut <%s>: activity=%f, rhs=%f, norm=%f, eff=%f\n",
            cutclassname, cutname, cutact, cutrhs, cutnorm, SCIPgetCutEfficacy(scip, sol, cut));
         SCIPdebug( SCIP_CALL( SCIPprintRow(scip, cut, NULL) ) );

         /* try to scale the cut to integral values, but only if the scaling is small; otherwise keep the fractional cut */
         SCIP_CALL( SCIPmakeRowIntegral(scip, cut, -SCIPepsilon(scip), SCIPsumepsilon(scip),
               (SCIP_Longint) 30, 100.0, MAKECONTINTEGRAL, &success) );
         if( success && !SCIPisCutEfficacious(scip, sol, cut) )
         {
            SCIPdebugMessage(" -> %s cut <%s> no longer efficacious: act=%f, rhs=%f, norm=%f, eff=%f\n",
               cutclassname, cutname, cutact, cutrhs, cutnorm, SCIPgetCutEfficacy(scip, sol, cut));
            SCIPdebug( SCIP_CALL( SCIPprintRow(scip, cut, NULL) ) );
            success = FALSE;
         }
         else
            success = TRUE; /* also use cut if scaling failed */

         /* if scaling was successful, add the cut */
         if( success ) /*lint !e774*/ /* Boolean within 'if' always evaluates to True */
         {
            SCIPdebugMessage(" -> found %s cut <%s>: act=%f, rhs=%f, norm=%f, eff=%f, rank=%d, min=%f, max=%f (range=%g)\n",
               cutclassname, cutname, cutact, cutrhs, cutnorm, SCIPgetCutEfficacy(scip, sol, cut), SCIProwGetRank(cut),
               SCIPgetRowMinCoef(scip, cut), SCIPgetRowMaxCoef(scip, cut),
               SCIPgetRowMaxCoef(scip, cut)/SCIPgetRowMinCoef(scip, cut));
            SCIPdebug( SCIP_CALL( SCIPprintRow(scip, cut, NULL) ) );
            SCIP_CALL( SCIPaddCut(scip, sol, cut, FALSE, cutoff) );
            if( !(*cutoff) && !cutislocal )
            {
               SCIP_CALL( SCIPaddPoolCut(scip, cut) );
            }
            (*ncuts)++;
         }

         /* release the row */
         SCIP_CALL( SCIPreleaseRow(scip, &cut) );
      }
   }

   /* free temporary memory */
   SCIPfreeBufferArray(scip, &cutvals);
   SCIPfreeBufferArray(scip, &cutvars);

   return SCIP_OKAY;   
}

/** adds delta to active continuous variables counter */
static
void updateNActiveConts(
   SCIP*                 scip,               /**< SCIP data structure */ 
   SCIP_Real*            varsolvals,         /**< LP solution value of all variables in LP */
   SCIP_Real*            bestcontlbs,        /**< best lower (variable or standard) bounds of continuous variables */
   SCIP_Real*            bestcontubs,        /**< best upper (variable or standard) bounds of continuous variables */
   int                   nintvars,           /**< number of integer variables */
   SCIP_VAR*             var,                /**< continuous variable */
   int                   delta,              /**< delta value of counters */
   int*                  nactiveconts        /**< pointer to count number of active continuous variables */
   )
{
   assert(nactiveconts != NULL);

   if( !SCIPvarIsIntegral(var) )
   {
      SCIP_Real primsol;
      SCIP_Real lb;
      SCIP_Real ub;
      int probindex;

      probindex = SCIPvarGetProbindex(var);
      assert(probindex >= nintvars);

      primsol = varsolvals[probindex];
      lb = bestcontlbs[probindex - nintvars];
      ub = bestcontubs[probindex - nintvars];

      if( SCIPisLT(scip, lb, primsol) && SCIPisLT(scip, primsol, ub) )
         (*nactiveconts) += delta;
   }
}

/** decreases the score of a row in order to not aggregate it again too soon */
static
void decreaseRowScore(
   SCIP*                 scip,               /**< SCIP data structure */ 
   SCIP_Real*            rowlhsscores,       /**< aggregation scores for left hand sides of row */
   SCIP_Real*            rowrhsscores,       /**< aggregation scores for right hand sides of row */
   int                   rowidx              /**< index of row to decrease score for */
   )
{
   assert(rowlhsscores != NULL);
   assert(rowrhsscores != NULL);
   assert(rowlhsscores[rowidx] >= 0.0);
   assert(rowrhsscores[rowidx] >= 0.0);

   rowlhsscores[rowidx] *= 0.9;
   rowrhsscores[rowidx] *= 0.9;
}

/** calculates the c-MIR cut for the given rowweights and delta value, and updates testeddeltas, bestdelta, and
 *  bestefficacy
 */
static
SCIP_RETCODE tryDelta(
   SCIP*                 scip,               /**< SCIP data structure */ 
   SCIP_SOL*             sol,                /**< the solution that should be separated, or NULL for LP solution */
   int                   nvars,              /**< number of problem variables */
   SCIP_Real*            rowweights,         /**< weight of rows in aggregated row */ 
   SCIP_Real             maxweight,          /**< largest magnitude of weights; set to -1 if sparsity information is unknown */
   int*                  weightinds,         /**< sparsity pattern of weights; size nrowinds; NULL if sparsity info is unknown */
   int                   nweightinds,        /**< number of nonzeros in weights; -1 if rowinds is NULL */
   int                   rowlensum,          /**< total number of non-zeros in used rows (row associated with nonzero weight coefficient); -1 if unknown */
   SCIP_Real*            cutcoefs,           /**< array to store the cut coefficients */
   SCIP_Real*            mksetcoefs,         /**< array to store mixed knapsack set coefficients: size nvars; or NULL */
   SCIP_Bool*            mksetcoefsvalid,    /**< pointer to store whether mixed knapsack set coefficients are valid; or NULL */
   SCIP_Real*            testeddeltas,       /**< array with already tested deltas */
   int*                  ntesteddeltas,      /**< pointer to the number of elements in testeddeltas */
   SCIP_Real             delta,              /**< delta value to scale mixed knapsack equation with */
   SCIP_Real             boundswitch,        /**< fraction of domain up to which lower bound is used in transformation */
   SCIP_Bool             usevbds,            /**< should variable bounds be used in bound transformation? */
   SCIP_Bool             allowlocal,         /**< should local information allowed to be used, resulting in a local cut? */
   SCIP_Bool             fixintegralrhs,     /**< should complementation tried to be adjusted such that rhs gets fractional? */
   int                   maxmksetcoefs,      /**< maximal number of nonzeros allowed in aggregated base inequality */
   SCIP_Real             maxweightrange,     /**< maximal valid range max(|weights|)/min(|weights|) of row weights */
   SCIP_Real             minfrac,            /**< minimal fractionality of rhs to produce MIR cut for */
   SCIP_Real             maxfrac,            /**< maximal fractionality of rhs to produce MIR cut for */
   SCIP_Real*            bestdelta,          /**< pointer to the currently best delta value */
   SCIP_Real*            bestefficacy        /**< pointer to the currently best efficacy */
   )
{
   SCIP_Bool tested;
   int i;

   assert(testeddeltas != NULL);
   assert(ntesteddeltas != NULL);
   assert(bestdelta != NULL);
   assert(bestefficacy != NULL);

   /* do not use too small deltas */
   if( SCIPisFeasZero(scip, delta) )
      return SCIP_OKAY;

   /* check, if delta with mult was already tested */
   tested = FALSE;
   for( i = 0; i < *ntesteddeltas && !tested; i++ )
      tested = SCIPisEQ(scip, testeddeltas[i], delta);
   if( !tested )
   {
      SCIP_Real cutact;
      SCIP_Real cutrhs;
      SCIP_Bool success;
      SCIP_Bool cutislocal;

      testeddeltas[*ntesteddeltas] = delta;
      (*ntesteddeltas)++;

      /* create a MIR cut out of the weighted LP rows */
      SCIP_CALL( SCIPcalcMIR(scip, sol, boundswitch, usevbds, allowlocal, fixintegralrhs, NULL, NULL, maxmksetcoefs,
            maxweightrange, minfrac, maxfrac, rowweights, maxweight, weightinds, nweightinds, rowlensum, NULL, delta,
            mksetcoefs, mksetcoefsvalid, cutcoefs, &cutrhs, &cutact, &success, &cutislocal, NULL) );
      assert(allowlocal || !cutislocal);
      SCIPdebugMessage("delta = %g  -> success: %u, cutact: %g, cutrhs: %g, vio: %g\n",
         delta, success, success ? cutact : 0.0, success ? cutrhs : 0.0, success ? cutact - cutrhs : 0.0);

      /* check if delta generates cut which is more violated */
      if( success && SCIPisFeasGT(scip, cutact, cutrhs) )
      {
         SCIP_Real norm;

         norm = SCIPgetVectorEfficacyNorm(scip, cutcoefs, nvars);
         if( norm > 0.0 )
         {
            SCIP_Real efficacy;

            efficacy = (cutact - cutrhs)/norm;
            SCIPdebugMessage("act = %g  rhs = %g  eff = %g, old besteff = %g, old bestdelta=%g\n", 
                             cutact, cutrhs, efficacy, *bestefficacy, *bestdelta);
            if( efficacy > *bestefficacy )
            {
               *bestdelta = delta;
               *bestefficacy = efficacy;
            }
         }
      }
   }

   return SCIP_OKAY;
}

/** Performs the cut generation heuristic of the c-MIR separation algorithm, i.e., tries to generate a c-MIR cut which is
 *  valid for the mixed knapsack set corresponding to the current aggregated constraint. Cuts will only be added here if 
 *  no pointer to store best scaling factor delta is given.
 */
SCIP_RETCODE SCIPcutGenerationHeuristicCmir(
   SCIP*                 scip,               /**< SCIP data structure */ 
   SCIP_SEPA*            sepa,               /**< separator */
   SCIP_SOL*             sol,                /**< the solution that should be separated, or NULL for LP solution */
   SCIP_Real*            varsolvals,         /**< LP solution value of all variables in LP */
   int                   maxtestdelta,       /**< maximal number of different deltas to try (-1: unlimited) */
   SCIP_Real*            rowweights,         /**< weight of rows in aggregated row */ 
   SCIP_Real             maxweight,          /**< largest magnitude of weights; set to -1.0 if sparsity information is
                                              *   unknown */
   int*                  weightinds,         /**< sparsity pattern of weights; size nrowinds; NULL if sparsity info is
                                              *   unknown */
   int                   nweightinds,        /**< number of nonzeros in weights; -1 if rowinds is NULL */
   int                   rowlensum,          /**< total number of non-zeros in used rows (row associated with nonzero weight coefficient); -1 if unknown */
   SCIP_Real             boundswitch,        /**< fraction of domain up to which lower bound is used in transformation */
   SCIP_Bool             usevbds,            /**< should variable bounds be used in bound transformation? */
   SCIP_Bool             allowlocal,         /**< should local information allowed to be used, resulting in a local cut? */
   SCIP_Bool             fixintegralrhs,     /**< should complementation tried to be adjusted such that rhs gets fractional? */
   int                   maxmksetcoefs,      /**< maximal number of nonzeros allowed in aggregated base inequality */
   SCIP_Real             maxweightrange,     /**< maximal valid range max(|weights|)/min(|weights|) of row weights */
   SCIP_Real             minfrac,            /**< minimal fractionality of rhs to produce MIR cut for */
   SCIP_Real             maxfrac,            /**< maximal fractionality of rhs to produce MIR cut for */
   SCIP_Bool             trynegscaling,      /**< should negative values also be tested in scaling? */
   SCIP_Bool             cutremovable,       /**< should the cut be removed from the LP due to aging or cleanup? */
   const char*           cutclassname,       /**< name of cut class to use for row names */
   SCIP_Bool*            cutoff,             /**< whether a cutoff has been detected */
   int*                  ncuts,              /**< pointer to count the number of generated cuts */
   SCIP_Real*            delta,              /**< pointer to store best delta found; NULL, if cut should be added here */
   SCIP_Bool*            deltavalid          /**< pointer to store whether best delta value is valid or NULL */
)
{  /*lint --e{715}*/
   SCIP_VAR** vars;
   SCIP_Real* cutcoefs;        
   SCIP_Real* mksetcoefs;
   SCIP_Real* testeddeltas;
   SCIP_Real bestdelta;
   SCIP_Real bestefficacy; 
   SCIP_Real maxabsmksetcoef;
   SCIP_Bool mksetcoefsvalid;
   int nvars;
   int ncontvars;
   int nintvars;
   int ntesteddeltas;
   int vi;

   assert( cutoff != NULL );
   *cutoff = FALSE;

   if( maxtestdelta == -1 )
      maxtestdelta = INT_MAX;

   if( delta != NULL )
      *deltavalid = FALSE;

   /* get active problem variables */
   vars = SCIPgetVars(scip);
   nvars = SCIPgetNVars(scip);
   ncontvars = SCIPgetNContVars(scip);
   nintvars = nvars-ncontvars;
   if( nvars == 0 )
      return SCIP_OKAY;
   assert(vars != NULL);

   /* get temporary memory */
   SCIP_CALL( SCIPallocBufferArray(scip, &mksetcoefs, nvars) );
   SCIP_CALL( SCIPallocBufferArray(scip, &cutcoefs, nvars) );
   SCIP_CALL( SCIPallocBufferArray(scip, &testeddeltas, 3 + 2*(nintvars+2)) );

   /* As in Marchand's version. Use the absolute value of the coefficients of the integer variables (lying 
    * strictly between its bounds) in the constructed mixed knapsack set, i.e., 
    * N* = { |alpha'_j| : j in N, alpha'_j != 0 and l_j < x*_j < u_j }
    */ 

   /* search delta for generating a cut with maximum efficacy: 
    * delta = coefficient of integer variable in constructed mixed knapsack set which lies between its bounds
    */ 
   ntesteddeltas = 0;
   bestdelta = 0.0;
   bestefficacy = 0.0;
   maxabsmksetcoef = 0.0;
   mksetcoefsvalid = FALSE;

   /* try delta = 1 and get the coefficients of all variables in the constructed mixed knapsack set;
    * if the aggregated row contains too many nonzero elements the generation of the c-MIR cut is aborted,
    * in this case, mksetcoefs is not valid and we can abort the separation heuristic (as the number of nonzeros
    * keeps the same for different values of delta) 
    */
   SCIP_CALL( tryDelta(scip, sol, nvars, rowweights, maxweight, weightinds, nweightinds, rowlensum, cutcoefs, mksetcoefs,
         &mksetcoefsvalid, testeddeltas, &ntesteddeltas, 1.0, boundswitch, usevbds, allowlocal, fixintegralrhs,
         maxmksetcoefs, maxweightrange, minfrac, maxfrac, &bestdelta, &bestefficacy) );
   if( mksetcoefsvalid && trynegscaling )
   {
      SCIP_CALL( tryDelta(scip, sol, nvars, rowweights, maxweight, weightinds, nweightinds, rowlensum, cutcoefs, NULL,
            NULL, testeddeltas, &ntesteddeltas, -1.0, boundswitch, usevbds, allowlocal, fixintegralrhs, maxmksetcoefs,
            maxweightrange, minfrac, maxfrac, &bestdelta, &bestefficacy) );
   }

   /* find mult in { +1, -1 } and delta in the corresponding set N* leading to the most violated c-MIR cut */
   for( vi = 0; mksetcoefsvalid && vi < nintvars; vi++ )
   {
      SCIP_VAR* var;
      SCIP_Real primsol;
      SCIP_Real lb;
      SCIP_Real ub;
      SCIP_Real absmksetcoef;

      var = vars[vi];
      assert(vi == SCIPvarGetProbindex(var));
      assert(SCIPvarGetType(var) != SCIP_VARTYPE_CONTINUOUS);
      assert(SCIPvarIsActive(var));
      assert(SCIPvarIsIntegral(var));

      /* update maximum coefficient of integer variables in constructed mixed knapsack set for 
       *   mult = +1 and delta = 1 and 
       *   mult = -1 and delta = 1 
       */
      absmksetcoef = REALABS(mksetcoefs[vi]);
      maxabsmksetcoef = MAX(maxabsmksetcoef, absmksetcoef);

      if( ntesteddeltas >= maxtestdelta )
         continue; /* remaining loop is only for maxabsmksetcoef calculation */

      /* ignore variables with current solution value on its bounds */
      primsol = varsolvals[vi];
      lb = SCIPvarGetLbLocal(var);
      ub = SCIPvarGetUbLocal(var);
      if( SCIPisEQ(scip, primsol, lb) || SCIPisEQ(scip, primsol, ub) )
         continue;

      /* try to divide aggregated row by absmksetcoef */
      if( !SCIPisFeasZero(scip, absmksetcoef) )
      {
         SCIP_CALL( tryDelta(scip, sol, nvars, rowweights, maxweight, weightinds, nweightinds, rowlensum, cutcoefs, NULL,
               NULL, testeddeltas, &ntesteddeltas, 1.0/absmksetcoef, boundswitch, usevbds, allowlocal, fixintegralrhs,
               maxmksetcoefs, maxweightrange, minfrac, maxfrac, &bestdelta, &bestefficacy) );
         if( trynegscaling )
         {
            SCIP_CALL( tryDelta(scip, sol, nvars, rowweights, maxweight, weightinds, nweightinds, rowlensum, cutcoefs,
                  NULL, NULL, testeddeltas, &ntesteddeltas, -1.0/absmksetcoef, boundswitch, usevbds, allowlocal,
                  fixintegralrhs, maxmksetcoefs, maxweightrange, minfrac, maxfrac, &bestdelta, &bestefficacy) );
         }
      }
   }

   /* additionally try delta = maxabscoef+1 */
   if( mksetcoefsvalid && !SCIPisFeasZero(scip, maxabsmksetcoef) )
   {
      SCIP_CALL( tryDelta(scip, sol, nvars, rowweights, maxweight, weightinds, nweightinds, rowlensum, cutcoefs, NULL,
            NULL, testeddeltas, &ntesteddeltas, 1.0/(maxabsmksetcoef+1.0), boundswitch, usevbds, allowlocal,
            fixintegralrhs, maxmksetcoefs, maxweightrange, minfrac, maxfrac, &bestdelta, &bestefficacy) );
      if( trynegscaling )
      {
         SCIP_CALL( tryDelta(scip, sol, nvars, rowweights, maxweight, weightinds, nweightinds, rowlensum, cutcoefs, NULL,
               NULL, testeddeltas, &ntesteddeltas, -1.0/(maxabsmksetcoef+1.0), boundswitch, usevbds, allowlocal,
               fixintegralrhs, maxmksetcoefs, maxweightrange, minfrac, maxfrac, &bestdelta, &bestefficacy) );
      }
   }

   /* delta found */
   if( mksetcoefsvalid && SCIPisEfficacious(scip, bestefficacy) )
   {
      SCIP_Real currentdelta;
      SCIP_Real cutrhs;           
      SCIP_Real cutact;
      SCIP_Bool success;
      SCIP_Bool cutislocal;
      int cutrank;
      int i;

      assert(!SCIPisFeasZero(scip, bestdelta));

      /* Try to improve efficacy by multiplying delta with 2, 4 and 8 */
      for( i = 0, currentdelta = 2.0 * bestdelta; i < 3; i++, currentdelta *= 2.0 )
      {
         SCIP_CALL( tryDelta(scip, sol, nvars, rowweights, maxweight, weightinds, nweightinds, rowlensum, cutcoefs,
               NULL, NULL, testeddeltas, &ntesteddeltas, currentdelta, boundswitch, usevbds, allowlocal, fixintegralrhs,
               maxmksetcoefs, maxweightrange, minfrac, maxfrac, &bestdelta, &bestefficacy) );
      }

      /* if no pointer to store delta is given, add cut here (zerohalf cuts will be stored in a separate cut pool first) */
      if( delta == NULL )
      {
         /* generate cut with bestdelta and best boundswitch value */
         SCIP_CALL( SCIPcalcMIR(scip, sol, boundswitch, usevbds, allowlocal, fixintegralrhs, NULL, NULL,
               maxmksetcoefs, maxweightrange, minfrac, maxfrac, rowweights, maxweight, weightinds, nweightinds, rowlensum,
               NULL, bestdelta, NULL, NULL, cutcoefs, &cutrhs, &cutact, &success, &cutislocal, &cutrank) );
         assert(allowlocal || !cutislocal);
         assert(success); 

         /* add the cut to the separation storage */
         SCIP_CALL( addCut(scip, sepa, sol, varsolvals, cutcoefs, cutrhs, cutislocal, cutremovable, cutrank, cutclassname, cutoff, ncuts) );
      }
      else
      {
         *delta = bestdelta;
         *deltavalid = TRUE;
      }
   }

   /* free datastructures */
   SCIPfreeBufferArray(scip, &testeddeltas);
   SCIPfreeBufferArray(scip, &cutcoefs);
   SCIPfreeBufferArray(scip, &mksetcoefs);

   return SCIP_OKAY; 
}

/** returns whether the variable should be tried to be aggregated out */
static
SCIP_Bool varIsContinuous(
   SCIP_VAR*             var                 /**< problem variable */
   )
{
   SCIP_VARTYPE vartype;

   vartype = SCIPvarGetType(var);

#ifdef IMPLINTSARECONT
   return (vartype == SCIP_VARTYPE_CONTINUOUS || vartype == SCIP_VARTYPE_IMPLINT);
#else
   return (vartype == SCIP_VARTYPE_CONTINUOUS);
#endif
}

/** returns the minimal distance of the solution of a continuous variable to its bounds */
static
SCIP_Real getBounddist(
   SCIP*                 scip,               /**< SCIP data structure */ 
   int                   nintvars,           /**< number of integer variables in the problem */
   SCIP_Real*            varsolvals,         /**< LP solution value of all variables in LP */
   SCIP_Real*            bestcontlbs,        /**< best lower (variable or standard) bounds of continuous variables */
   SCIP_Real*            bestcontubs,        /**< best upper (variable or standard) bounds of continuous variables */
   SCIP_VAR*             var                 /**< continuous variable to get bound distance for */
   )
{
   SCIP_Real primsol;
   SCIP_Real lb;
   SCIP_Real ub;
   SCIP_Real distlower;
   SCIP_Real distupper;
   SCIP_Real bounddist;

   assert(varIsContinuous(var));
   assert(SCIPvarGetProbindex(var) >= nintvars);

   primsol = varsolvals[SCIPvarGetProbindex(var)];
   lb = bestcontlbs[SCIPvarGetProbindex(var) - nintvars];
   ub = bestcontubs[SCIPvarGetProbindex(var) - nintvars];
   assert(SCIPisGE(scip, lb, SCIPvarGetLbGlobal(var)));
   assert(SCIPisLE(scip, ub, SCIPvarGetUbGlobal(var)));
   distlower = primsol - lb;
   distupper = ub - primsol;
   bounddist = MIN(distlower, distupper);

#ifdef IMPLINTSARECONT
   /* prefer continuous variables over implicit integers to be aggregated out */
   if( SCIPvarGetType(var) != SCIP_VARTYPE_CONTINUOUS )
      bounddist /= 10.0;
#endif

   return bounddist;
}

/** aggregates different single mixed integer constraints by taking linear combinations of the rows of the LP  */
static
SCIP_RETCODE aggregation(
   SCIP*                 scip,               /**< SCIP data structure */ 
   SCIP_SEPA*            sepa,               /**< separator */
   SCIP_SEPADATA*        sepadata,           /**< separator data */
   SCIP_SOL*             sol,                /**< the solution that should be separated, or NULL for LP solution */
   SCIP_Real*            varsolvals,         /**< LP solution value of all variables in LP */
   SCIP_Real*            bestcontlbs,        /**< best lower (variable or standard) bounds of continuous variables */
   SCIP_Real*            bestcontubs,        /**< best upper (variable or standard) bounds of continuous variables */
   SCIP_Real*            contvarscorebounds, /**< bounds on the maximal rowlhsscores and rowrhsscores the variable is contained in */
   SCIP_Real*            rowlhsscores,       /**< aggregation scores for left hand sides of row */
   SCIP_Real*            rowrhsscores,       /**< aggregation scores for right hand sides of row */
   int                   startrow,           /**< index of row to start aggregation */ 
   int                   maxaggrs,           /**< maximal number of aggregations */
   SCIP_Real             maxslack,           /**< maximal slack of rows to be used in aggregation */
   int                   maxconts,           /**< maximal number of active continuous variables in aggregated row */
   SCIP_Bool*            wastried,           /**< pointer to store whether the given startrow was actually tried */
   SCIP_Bool*            cutoff,             /**< whether a cutoff has been detected */
   int*                  ncuts               /**< pointer to count the number of generated cuts */
   )
{
   SCIP_COL** startnonzcols;   
   SCIP_COL** cols;
   SCIP_VAR** vars;
   SCIP_ROW** rows;
   SCIP_COL* bestcol;          
   SCIP_Real* startnonzcoefs;      
   SCIP_Real* aggrcoefs;       
   SCIP_Real* rowweights;
   int* weightinds;
   int* aggrcontnonzposs;
   SCIP_Real* aggrcontnonzbounddists;
   SCIP_Real maxweight;
   SCIP_Real minweight;
   SCIP_Real startrowact;      
   SCIP_Bool hasfractional;
   int naggrintnonzs;
   int naggrcontnonzs;
   int maxaggrnonzs;
   int nstartnonzcols;         
   int naggrs;
   int nactiveconts;
   int nvars;
   int nintvars;
   int ncontvars;
   int ncols;
   int nrows;
   int indpos;
   int c;
   int r;
   int nweightinds;
   int rowlensum;

   assert(scip != NULL);
   assert(sepadata != NULL);      
   assert(varsolvals != NULL);
   assert(rowlhsscores != NULL);
   assert(rowrhsscores != NULL);
   assert(wastried != NULL);
   assert(cutoff != NULL);
   assert(ncuts != NULL);

   *cutoff = FALSE;
   *wastried = FALSE;

   SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, &ncontvars) );
#ifdef IMPLINTSARECONT
   ncontvars += SCIPgetNImplVars(scip); /* also aggregate out implicit integers */
#endif
   nintvars = nvars - ncontvars;
   assert((nvars == 0 && nintvars == 0 && ncontvars == 0) || vars != NULL);
   SCIP_CALL( SCIPgetLPColsData(scip, &cols, &ncols) );
   assert(ncols == 0 || cols != NULL);
   SCIP_CALL( SCIPgetLPRowsData(scip, &rows, &nrows) );
   assert(nrows == 0 || rows != NULL);
   assert(0 <= startrow && startrow < nrows);

   SCIPdebugMessage("start c-MIR aggregation with row <%s> (%d/%d)\n", SCIProwGetName(rows[startrow]), startrow, nrows);

   /* calculate maximal number of non-zeros in aggregated row */
   maxaggrnonzs = (int)(sepadata->maxaggdensity * ncols) + sepadata->densityoffset;

   /* get temporary memory */
   SCIP_CALL( SCIPallocBufferArray(scip, &aggrcoefs, ncols) );
   BMSclearMemoryArray(aggrcoefs, ncols);
   SCIP_CALL( SCIPallocBufferArray(scip, &aggrcontnonzposs, ncols) );
   SCIP_CALL( SCIPallocBufferArray(scip, &aggrcontnonzbounddists, ncols) );
   SCIP_CALL( SCIPallocBufferArray(scip, &rowweights, nrows) );
   /* initialize weights of rows in aggregation */
   BMSclearMemoryArray(rowweights, nrows);
   SCIP_CALL( SCIPallocBufferArray(scip, &weightinds, nrows) );
   BMSclearMemoryArray(weightinds, nrows);

   startrowact = SCIPgetRowSolActivity(scip, rows[startrow], sol);
   if( startrowact <= 0.5 * SCIProwGetLhs(rows[startrow]) + 0.5 * SCIProwGetRhs(rows[startrow]) )
      rowweights[startrow] = -1.0;
   else 
      rowweights[startrow] = 1.0;

   /* build weights sparse representation */
   nweightinds = 0;
   rowlensum = 0;
   weightinds[nweightinds] = startrow;
   nweightinds++;
   rowlensum += SCIProwGetNNonz(rows[startrow]);

   maxweight = 1.0;
   minweight = 1.0;

   /* get nonzero columns and coefficients of startrow */
   startnonzcols =  SCIProwGetCols(rows[startrow]);
   nstartnonzcols = SCIProwGetNLPNonz(rows[startrow]);
   startnonzcoefs = SCIProwGetVals(rows[startrow]);

   /* for all columns of startrow store coefficient as coefficient in aggregated row */ 
   naggrintnonzs = 0;
   naggrcontnonzs = 0;
   nactiveconts = 0;
   hasfractional = FALSE;
   for( c = 0; c < nstartnonzcols; c++ )
   {
      SCIP_VAR* var;
      int pos;

      var = SCIPcolGetVar(startnonzcols[c]);
      pos = SCIPcolGetLPPos(startnonzcols[c]);
      assert(pos >= 0); 
      assert(!SCIPisZero(scip, startnonzcoefs[c]));
      aggrcoefs[pos] = rowweights[startrow] * startnonzcoefs[c];
      if( varIsContinuous(var) )
      {
         SCIP_Real bounddist;

         updateNActiveConts(scip, varsolvals, bestcontlbs, bestcontubs, nintvars, var, +1, &nactiveconts);

         /* store continuous variable in array sorted by distance to closest bound */
         bounddist = getBounddist(scip, nintvars, varsolvals, bestcontlbs, bestcontubs, var);
         SCIPsortedvecInsertDownRealInt(aggrcontnonzbounddists, aggrcontnonzposs, bounddist, pos, &naggrcontnonzs, NULL);
      }
      else
         naggrintnonzs++;

      if( !hasfractional && SCIPvarIsIntegral(var) )
      {
         SCIP_Real primsol;

         primsol = varsolvals[SCIPvarGetProbindex(var)];
         hasfractional = !SCIPisFeasIntegral(scip, primsol);
      }
   }
   assert(naggrintnonzs + naggrcontnonzs == nstartnonzcols);

   /* don't try aggregation if there is no integer variable with fractional value */
   if( !hasfractional )
   {
      SCIPdebugMessage(" -> row has no fractional integer variables: ignore\n");
      maxaggrs = -1;
   }

   /* decrease score of startrow in order to not aggregate it again too soon */
   decreaseRowScore(scip, rowlhsscores, rowrhsscores, startrow);

   /* try to generate cut from the current aggregated row 
    * add cut if found, otherwise add another row to aggregated row 
    * in order to get rid of a continuous variable
    */
   naggrs = 0;
   while( nactiveconts <= maxconts && naggrs <= maxaggrs && naggrcontnonzs + naggrintnonzs <= maxaggrnonzs )
   {
      SCIP_ROW* bestrow;          
      SCIP_COL** bestrownonzcols;     /* columns with nonzero coefficients in best row to add */
      SCIP_Real* bestrownonzcoefs;    /* nonzero coefficients of columns in best row to add */
      int nbestrownonzcols;           /* number of columns with nonzero coefficients in best row to add */
      SCIP_Real bestbounddist;
      SCIP_Real bestscore;
      int bestrowpos;
      SCIP_Real aggrfac;
      SCIP_Real absaggrfac;
      int nzi;
      int oldncuts;
      int ncanceledcontnonzs;

      *wastried = TRUE;

#ifdef SCIP_DEBUG
      SCIPdebugMessage("aggregation of startrow %d and %d additional rows with %d integer and %d continuous variables (%d active):\n",
         startrow, naggrs, naggrintnonzs, naggrcontnonzs, nactiveconts);
      for( c = 0; c < ncols; ++c )
      {
         if( aggrcoefs[c] != 0.0 )
            SCIPdebugPrintf(" %+g<%s>(%g)", aggrcoefs[c], SCIPvarGetName(SCIPcolGetVar(cols[c])),
               varsolvals[SCIPvarGetProbindex(SCIPcolGetVar(cols[c]))]);
      }
      SCIPdebugPrintf("\n");
#endif

      /* Step 1: 
       * try to generate a MIR cut out of the current aggregation 
       */
      oldncuts = *ncuts;
      SCIP_CALL( SCIPcutGenerationHeuristicCmir(scip, sepa, sol, varsolvals, sepadata->maxtestdelta, rowweights, maxweight,
             weightinds, nweightinds, rowlensum, BOUNDSWITCH, USEVBDS, ALLOWLOCAL, sepadata->fixintegralrhs,
             (int) MAXAGGRLEN(nvars), sepadata->maxrowfac, MINFRAC, MAXFRAC, sepadata->trynegscaling,
             sepadata->dynamiccuts, "cmir", cutoff, ncuts, NULL, NULL) );

      if ( *cutoff )
         break;

      /* if the cut was successfully added, abort the aggregation of further rows */
      if( *ncuts > oldncuts )
      {
         SCIPdebugMessage(" -> abort aggregation: cut found\n");
         break;
      }

      /* Step 2: 
       * aggregate an additional row in order to remove a continuous variable 
       */

      /* abort, if we reached the maximal number of aggregations */
      if( naggrs == maxaggrs )
      {
         SCIPdebugMessage(" -> abort aggregation: %s\n", nactiveconts == 0 ? "no more active continuous variables"
            : "maximal number of aggregations reached");
         break;
      }

      SCIPdebugMessage(" -> search column to eliminate\n");

      /* search for "best" continuous variable in aggregated row:
       * - solution value is strictly between lower and upper bound
       * - it exists a not yet aggregated row with nonzero coefficient in this column
       * out of these variables:
       * - prefer variables with larger distance of current solution value to its bounds
       * - of those with large bound distance, prefer variables that can be eliminated with a row of high score
       */
      bestcol = NULL;
      bestbounddist = -1.0;
      bestscore = 0.0;
      bestrow = NULL;
      aggrfac = 0.0;
      for( nzi = 0; nzi < naggrcontnonzs; ++nzi )
      {
         SCIP_COL* col;
         SCIP_VAR* var;
         SCIP_Real bounddist;

         c = aggrcontnonzposs[nzi];
         assert(0 <= c && c < ncols);
         assert(!SCIPisZero(scip, aggrcoefs[c]));

         col = cols[c];
         var = SCIPcolGetVar(col);
         assert(varIsContinuous(var));
         assert(SCIPvarGetProbindex(var) >= nintvars);

         bounddist = aggrcontnonzbounddists[nzi];
         assert(SCIPisEQ(scip, bounddist, getBounddist(scip, nintvars, varsolvals, bestcontlbs, bestcontubs, var)));
         assert(bounddist <= bestbounddist || bestbounddist == -1.0);

         /* check, if variable is candidate to be the new best variable */
         if( bounddist >= bestbounddist - sepadata->aggrtol )
         {
            SCIP_ROW** nonzrows;
            SCIP_Real* nonzcoefs;
            SCIP_Real maxrowscore;
            int nnonzrows;
            int probindex;

            probindex = SCIPvarGetProbindex(var);
            assert(probindex >= nintvars);

            SCIPdebugMessage("     -> col <%s>[%g,%g]: sol=%g, dist=%g\n", 
               SCIPvarGetName(var), bestcontlbs[probindex - nintvars],
               bestcontubs[probindex - nintvars], varsolvals[probindex], bounddist);

            /* if we know that we will not find a better row, just skip the column */
            if( contvarscorebounds[probindex - nintvars] <= bestscore )
               continue;

            /* look for "best" row to add (minimal slack), but don't add rows again,
             * that are already involved in aggregation
             */
            nnonzrows = SCIPcolGetNLPNonz(col);
            nonzrows = SCIPcolGetRows(col);
            nonzcoefs = SCIPcolGetVals(col);
            maxrowscore = 0.0;

            for( r = 0; r < nnonzrows; r++ )
            {
               SCIP_Real score;
               SCIP_Real rowscore;
               SCIP_Real factor;
               SCIP_Real absfactor;
               SCIP_Real activity;
               SCIP_Real lhs;
               SCIP_Real rhs;
               SCIP_Real rowlhsscore;
               SCIP_Real rowrhsscore;
               int lppos;

               lppos = SCIProwGetLPPos(nonzrows[r]);
               assert(0 <= lppos && lppos < nrows);

               SCIPdebugMessage("        -> r=%d row <%s>: weight=%g, pos=%d, alpha_j=%g, a^r_j=%g, factor=%g, %g <= %g <= %g\n",
                  r, SCIProwGetName(nonzrows[r]), rowweights[lppos], lppos, aggrcoefs[c], nonzcoefs[r], 
                  - aggrcoefs[c] / nonzcoefs[r], SCIProwGetLhs(nonzrows[r]), 
                  SCIPgetRowSolActivity(scip, nonzrows[r], sol), SCIProwGetRhs(nonzrows[r]));

               /* update maxrowscore */
               rowlhsscore = rowlhsscores[lppos];
               rowrhsscore = rowrhsscores[lppos];
               rowscore = MAX(rowlhsscore, rowrhsscore);
               maxrowscore = MAX(maxrowscore, rowscore);

               /* if even the better rowscore does not improve the bestscore, ignore the row */
               if( rowscore <= bestscore )
                  continue;

               /* take only unmodifiable LP rows, that are not yet aggregated */
               if( rowweights[lppos] != 0.0 || SCIProwIsModifiable(nonzrows[r]) )
                  continue;

               /* don't aggregate rows that would lead to a too extreme aggregation factor */
               factor = - aggrcoefs[c] / nonzcoefs[r]; 
               absfactor = REALABS(factor);
               if( !SCIPisPositive(scip, absfactor)
                  || absfactor > sepadata->maxrowfac * minweight
                  || maxweight > sepadata->maxrowfac * absfactor )
                  continue;

               /* for selected real variable y_k, select constraint r with best score SCORE_r with r in P\Q, 
                * where P\Q is the set of constraints not yet involved in the aggregation set
                */
               assert(!SCIPisInfinity(scip, -SCIProwGetLhs(nonzrows[r])) || rowlhsscores[lppos] == 0.0);
               assert(!SCIPisInfinity(scip, SCIProwGetRhs(nonzrows[r])) || rowrhsscores[lppos] == 0.0);
               score = (factor < 0.0 ? rowlhsscore : rowrhsscore);
               if( score <= bestscore )
                  continue;

               /* check, if the row's slack multiplied with the aggregation factor is too large */
               activity = SCIPgetRowSolActivity(scip, nonzrows[r], sol);
               lhs = SCIProwGetLhs(nonzrows[r]);
               rhs = SCIProwGetRhs(nonzrows[r]);
               if( (factor < 0.0 && SCIPisGT(scip, factor * (lhs - activity), maxslack))
                  || (factor > 0.0 && SCIPisGT(scip, factor * (rhs - activity), maxslack)) )
                  continue;

               /* the row passed all tests: it is the best candidate up to now */
               bestbounddist = bounddist;
               bestscore = score; 
               bestcol = col;
               bestrow = nonzrows[r];
               aggrfac = factor;
               SCIPdebugMessage("     -> col <%s>: %g * row <%s>, bounddist=%g, score=%g\n",
                  SCIPvarGetName(SCIPcolGetVar(bestcol)), aggrfac, SCIProwGetName(bestrow), bestbounddist, score);
            }

            /* update score bound of column */
            assert(maxrowscore <= contvarscorebounds[probindex - nintvars]);
            contvarscorebounds[probindex - nintvars] = maxrowscore;
         }
         else
         {
            /* since the nonzero continuous variables are sorted by bound distance, we can abort now */
            break;
         }
      }
      assert((bestcol == NULL) == (bestrow == NULL));

#ifndef NDEBUG
      /* check that the remaining variables really can be ignored */
      for( ; nzi < naggrcontnonzs; ++nzi )
      {
         SCIP_COL* col;
         SCIP_VAR* var;
         SCIP_Real bounddist;

         c = aggrcontnonzposs[nzi];
         assert(0 <= c && c < ncols);
         assert(!SCIPisZero(scip, aggrcoefs[c]));

         col = cols[c];
         var = SCIPcolGetVar(col);
         assert(varIsContinuous(var));

         bounddist = aggrcontnonzbounddists[nzi];

         SCIPdebugMessage("     -> ignoring col <%s>[%g,%g]: sol=%g, dist=%g\n", 
            SCIPvarGetName(var), bestcontlbs[SCIPvarGetProbindex(var) - nintvars],
            bestcontubs[SCIPvarGetProbindex(var) - nintvars], varsolvals[SCIPvarGetProbindex(var)], bounddist);

         assert(SCIPisEQ(scip, bounddist, getBounddist(scip, nintvars, varsolvals, bestcontlbs, bestcontubs, var)));
         assert(bounddist < bestbounddist - sepadata->aggrtol);
      }
#endif

      /* abort, if no row can be added to remove an additional active continuous variable */
      if( bestcol == NULL )
      {
         SCIPdebugMessage(" -> abort aggregation: no removable column found\n");
         break;
      }

      /* Step 3: add row to aggregation */
      bestrowpos = SCIProwGetLPPos(bestrow);
      SCIPdebugMessage(" -> adding %+g<%s> to eliminate variable <%s> (aggregation %d)\n", 
         aggrfac, SCIProwGetName(bestrow), SCIPvarGetName(SCIPcolGetVar(bestcol)), naggrs+1);
      assert(rowweights[bestrowpos] == 0.0);
      assert(!SCIPisZero(scip, aggrfac));

      /* change row's aggregation weight */
      rowweights[bestrowpos] = aggrfac;

      /* build weights sparse representation */
      SCIPsortedvecInsertInt(weightinds, bestrowpos, &nweightinds, &indpos);
      rowlensum += SCIProwGetNNonz(rows[bestrowpos]);

      absaggrfac = REALABS(aggrfac);
      maxweight = MAX(maxweight, absaggrfac);
      minweight = MIN(minweight, absaggrfac);

      /* decrease score of aggregation row in order to not aggregate it again too soon */
      decreaseRowScore(scip, rowlhsscores, rowrhsscores, bestrowpos);

      /* change coefficients of aggregation and update the number of continuous variables */
      bestrownonzcols = SCIProwGetCols(bestrow);
      bestrownonzcoefs = SCIProwGetVals(bestrow);
      nbestrownonzcols = SCIProwGetNLPNonz(bestrow);
      ncanceledcontnonzs = 0;
      for( c = 0; c < nbestrownonzcols; c++ )
      {
         SCIP_VAR* var;
         int pos;
         SCIP_Bool iscont;
         SCIP_Bool waszero;
         SCIP_Bool iszero;

         var = SCIPcolGetVar(bestrownonzcols[c]);
         pos = SCIPcolGetLPPos(bestrownonzcols[c]);
         assert(pos >= 0);
         assert(!SCIPisZero(scip, bestrownonzcoefs[c]));

         iscont = varIsContinuous(var);
         waszero = (aggrcoefs[pos] == 0.0);
         aggrcoefs[pos] += bestrownonzcoefs[c] * aggrfac;
         iszero = SCIPisZero(scip, aggrcoefs[pos]);

         if( iszero )
         {
            aggrcoefs[pos] = 0.0;
            if( !waszero )
            {
               /* coefficient switched from non-zero to zero */
               if( iscont )
               {
                  ncanceledcontnonzs++;
                  /* naggrcontnonzs will be decreased later in a cleanup step */
                  updateNActiveConts(scip, varsolvals, bestcontlbs, bestcontubs, nintvars, var, -1, &nactiveconts);
               }
               else
                  naggrintnonzs--;
            }
         }
         else if( waszero )
         {
            /* coefficient switched from zero to non-zero */
            if( iscont )
            {
               SCIP_Real bounddist;

               assert(naggrcontnonzs < ncols);

               /* store continuous variable in array sorted by distance to closest bound */
               bounddist = getBounddist(scip, nintvars, varsolvals, bestcontlbs, bestcontubs, var);
               SCIPsortedvecInsertDownRealInt(aggrcontnonzbounddists, aggrcontnonzposs, bounddist, pos, &naggrcontnonzs, NULL);

               updateNActiveConts(scip, varsolvals, bestcontlbs, bestcontubs, nintvars, var, +1, &nactiveconts);
            }
            else
               naggrintnonzs++;
         }
      }

      /* remove canceled elements from aggtcontnonzs vector */
      if( ncanceledcontnonzs > 0 )
      {
         int newnaggrintnonzs;

         newnaggrintnonzs = 0;
         for( nzi = 0; nzi < naggrcontnonzs; ++nzi )
         {
            int pos;

            pos = aggrcontnonzposs[nzi];
            assert(0 <= pos && pos < ncols);
            if( aggrcoefs[pos] != 0.0 )
            {
               assert(newnaggrintnonzs <= nzi);
               aggrcontnonzposs[newnaggrintnonzs] = pos;
               aggrcontnonzbounddists[newnaggrintnonzs] = aggrcontnonzbounddists[nzi];
               newnaggrintnonzs++;
            }
         }
         assert(ncanceledcontnonzs == naggrcontnonzs - newnaggrintnonzs);
         naggrcontnonzs = newnaggrintnonzs;
      }

      naggrs++;

      SCIPdebugMessage(" -> %d continuous variables left (%d/%d active), %d/%d nonzeros, %d/%d aggregations\n", 
         naggrcontnonzs, nactiveconts, maxconts, naggrcontnonzs + naggrintnonzs, maxaggrnonzs, naggrs, maxaggrs);
   }
#ifdef SCIP_DEBUG
   if( nactiveconts > maxconts )
   {
      SCIPdebugMessage(" -> abort aggregation: %d/%d active continuous variables\n", nactiveconts, maxconts);
   }
   if( naggrcontnonzs + naggrintnonzs > maxaggrnonzs )
   {
      SCIPdebugMessage(" -> abort aggregation: %d/%d nonzeros\n", naggrcontnonzs + naggrintnonzs, maxaggrnonzs);
   }
#endif

   /* free datastructures */
   SCIPfreeBufferArray(scip, &weightinds);
   SCIPfreeBufferArray(scip, &rowweights);
   SCIPfreeBufferArray(scip, &aggrcontnonzbounddists);
   SCIPfreeBufferArray(scip, &aggrcontnonzposs);
   SCIPfreeBufferArray(scip, &aggrcoefs);

   return SCIP_OKAY;
}

/** searches and adds c-MIR cuts that separate the given primal solution */
static
SCIP_RETCODE separateCuts(
   SCIP*                 scip,               /**< SCIP data structure */ 
   SCIP_SEPA*            sepa,               /**< the c-MIR separator */
   SCIP_SOL*             sol,                /**< the solution that should be separated, or NULL for LP solution */
   SCIP_RESULT*          result              /**< pointer to store the result */
   )
{
   SCIP_SEPADATA* sepadata;
   SCIP_VAR** vars;
   SCIP_Real* varsolvals;
   SCIP_Real* bestcontlbs;
   SCIP_Real* bestcontubs;
   SCIP_Real* contvarscorebounds;
   SCIP_ROW** rows;     
   SCIP_Real* rowlhsscores;
   SCIP_Real* rowrhsscores;
   SCIP_Real* rowscores;
   int* roworder;
   SCIP_Real maxslack;
   SCIP_Real objnorm;
   SCIP_Bool cutoff = FALSE;
   int nvars;
   int nintvars;
   int ncontvars;
   int nrows;
   int nnonzrows;
   int zerorows;
   int ntries;
   int nfails;
   int depth;
   int ncalls;
   int maxtries;
   int maxfails;
   int maxaggrs;
   int maxsepacuts;
   int maxconts;
   int ncuts;
   int r;
   int v;

   assert(result != NULL);
   assert(*result == SCIP_DIDNOTRUN);

   sepadata = SCIPsepaGetData(sepa);
   assert(sepadata != NULL);

   depth = SCIPgetDepth(scip);
   ncalls = SCIPsepaGetNCallsAtNode(sepa);

   /* only call the cmir cut separator a given number of times at each node */
   if( (depth == 0 && sepadata->maxroundsroot >= 0 && ncalls >= sepadata->maxroundsroot)
      || (depth > 0 && sepadata->maxrounds >= 0 && ncalls >= sepadata->maxrounds) )
      return SCIP_OKAY;

   /* get all rows and number of columns */
   SCIP_CALL( SCIPgetLPRowsData(scip, &rows, &nrows) ); 
   assert(nrows == 0 || rows != NULL);

   /* nothing to do, if LP is empty */
   if( nrows == 0 )
      return SCIP_OKAY;

   /* check whether SCIP was stopped in the meantime */
   if( SCIPisStopped(scip) )
      return SCIP_OKAY;

   /* get active problem variables */
   vars = SCIPgetVars(scip);
   nvars = SCIPgetNVars(scip);
   ncontvars = SCIPgetNContVars(scip);
#ifdef IMPLINTSARECONT
   ncontvars += SCIPgetNImplVars(scip); /* also aggregate out implicit integers */
#endif
   nintvars = nvars-ncontvars;
   assert(nvars == 0 || vars != NULL);

   /* nothing to do, if problem has no variables */
   if( nvars == 0 )
      return SCIP_OKAY;

   SCIPdebugMessage("separating c-MIR cuts\n");

   *result = SCIP_DIDNOTFIND;

   /* get data structure */
   SCIP_CALL( SCIPallocBufferArray(scip, &rowlhsscores, nrows) );
   SCIP_CALL( SCIPallocBufferArray(scip, &rowrhsscores, nrows) );
   SCIP_CALL( SCIPallocBufferArray(scip, &rowscores, nrows) );
   SCIP_CALL( SCIPallocBufferArray(scip, &roworder, nrows) );
   SCIP_CALL( SCIPallocBufferArray(scip, &varsolvals, nvars) );
   SCIP_CALL( SCIPallocBufferArray(scip, &bestcontlbs, ncontvars) );
   SCIP_CALL( SCIPallocBufferArray(scip, &bestcontubs, ncontvars) );
   SCIP_CALL( SCIPallocBufferArray(scip, &contvarscorebounds, ncontvars) );

   /* get the solution values for all active variables */
   SCIP_CALL( SCIPgetSolVals(scip, sol, nvars, vars, varsolvals) );

   /* calculate the tightest bounds w.r.t. current solution for the continuous variables */
   for( v = nintvars; v < nvars; ++v )
   {
      SCIP_Real bestlb;
      SCIP_Real bestub;
      SCIP_Real bestvlb;
      SCIP_Real bestvub;
      int bestvlbidx;
      int bestvubidx;

#if ALLOWLOCAL == 1
      bestlb = SCIPvarGetLbLocal(vars[v]);
      bestub = SCIPvarGetUbLocal(vars[v]);
#else
      bestlb = SCIPvarGetLbGlobal(vars[v]);
      bestub = SCIPvarGetUbGlobal(vars[v]);
#endif
      SCIP_CALL( SCIPgetVarClosestVlb(scip, vars[v], sol, &bestvlb, &bestvlbidx) );
      SCIP_CALL( SCIPgetVarClosestVub(scip, vars[v], sol, &bestvub, &bestvubidx) );
      if( bestvlbidx >= 0 )
         bestlb = MAX(bestlb, bestvlb);
      if( bestvubidx >= 0 )
         bestub = MIN(bestub, bestvub);

      bestcontlbs[v-nintvars] = bestlb;
      bestcontubs[v-nintvars] = bestub;

      /* initialize row score bounds for continuous variables */
      contvarscorebounds[v-nintvars] = SCIP_REAL_MAX;
   }

   /* get the maximal number of cuts allowed in a separation round */
   if( depth == 0 )
   {
      maxtries = sepadata->maxtriesroot;
      maxfails = sepadata->maxfailsroot;
      maxaggrs = sepadata->maxaggrsroot;
      maxsepacuts = sepadata->maxsepacutsroot;
      maxslack = sepadata->maxslackroot;
      maxconts = sepadata->maxcontsroot;
   }   
   else
   {
      maxtries = sepadata->maxtries;
      maxfails = sepadata->maxfails;
      maxaggrs = sepadata->maxaggrs;
      maxsepacuts = sepadata->maxsepacuts;
      maxslack = sepadata->maxslack;
      maxconts = sepadata->maxconts;
   }

   /* calculate aggregation scores for both sides of all rows, and sort rows by nonincreasing maximal score */
   objnorm = SCIPgetObjNorm(scip);
   objnorm = MAX(objnorm, 1.0);

   /* count the number of non-zero rows and zero rows.
    * these values are used for the sorting of the rowscores.
    * only the non-zero rows need to be sorted. */
   nnonzrows = 0;
   zerorows = 0;
   for( r = 0; r < nrows; r++ )
   {
      int nnonz;
      int i;

      assert(SCIProwGetLPPos(rows[r]) == r);

      nnonz = SCIProwGetNLPNonz(rows[r]);
      if( nnonz == 0 )
      {
         /* ignore empty rows */
         rowlhsscores[r] = 0.0;
         rowrhsscores[r] = 0.0;

         /* adding the row number to the back of the roworder
          * for the zero rows  */
         zerorows++;
         rowscores[r] = 0.0;
         roworder[nrows - zerorows] = r;
      }
      else
      {
         SCIP_Real activity;
         SCIP_Real lhs;
         SCIP_Real rhs;
         SCIP_Real dualsol;
         SCIP_Real dualscore;
         SCIP_Real rowdensity;
         SCIP_Real rownorm;
         SCIP_Real slack;

         dualsol = (sol == NULL ? SCIProwGetDualsol(rows[r]) : 1.0);
         activity = SCIPgetRowSolActivity(scip, rows[r], sol);
         lhs = SCIProwGetLhs(rows[r]);
         rhs = SCIProwGetRhs(rows[r]);
         rownorm = SCIProwGetNorm(rows[r]);
         rownorm = MAX(rownorm, 0.1);
         rowdensity = (SCIP_Real)(nnonz - sepadata->densityoffset)/(SCIP_Real)nvars;
         assert(SCIPisPositive(scip, rownorm));

         slack = (activity - lhs)/rownorm;
         dualscore = MAX(dualsol/objnorm, 0.0001);
         if( !SCIPisInfinity(scip, -lhs) && SCIPisLE(scip, slack, maxslack)
            && (ALLOWLOCAL || !SCIProwIsLocal(rows[r])) /*lint !e506 !e774*/
            && rowdensity <= sepadata->maxrowdensity
            && rowdensity <= sepadata->maxaggdensity )  /*lint !e774*/
         {
            rowlhsscores[r] = dualscore + sepadata->densityscore * (1.0-rowdensity)
               + sepadata->slackscore * MAX(1.0 - slack, 0.0);
            assert(rowlhsscores[r] > 0.0);
         }
         else
            rowlhsscores[r] = 0.0;

         slack = (rhs - activity)/rownorm;
         dualscore = MAX(-dualsol/objnorm, 0.0001);
         if( !SCIPisInfinity(scip, rhs) && SCIPisLE(scip, slack, maxslack)
            && (ALLOWLOCAL || !SCIProwIsLocal(rows[r])) /*lint !e506 !e774*/
            && rowdensity <= sepadata->maxrowdensity
            && rowdensity <= sepadata->maxaggdensity )  /*lint !e774*/
         {
            rowrhsscores[r] = dualscore + sepadata->densityscore * (1.0-rowdensity)
               + sepadata->slackscore * MAX(1.0 - slack, 0.0);
            assert(rowrhsscores[r] > 0.0);
         }
         else
            rowrhsscores[r] = 0.0;

         rowscores[r] = MAX(rowlhsscores[r], rowrhsscores[r]);
         if( rowscores[r] == 0.0 )
         {
            /* adding the row number to the back of the roworder
             * for the zero rows  */
            zerorows++;
            roworder[nrows - zerorows] = r;
         }
         else
         {
            /* adding and sorting the row number to the next index
             * in roworder <= nnonzrows  */
            for( i = nnonzrows; i > 0 && rowscores[r] > rowscores[roworder[i - 1]]; --i )
               roworder[i] = roworder[i - 1];
            roworder[i] = r;

            nnonzrows++;
         }
      }

      SCIPdebugMessage(" -> row %d <%s>: lhsscore=%g rhsscore=%g maxscore=%g\n", r, SCIProwGetName(rows[r]),
                       rowlhsscores[r], rowrhsscores[r], rowscores[r]);
   }
   assert(nrows == nnonzrows + zerorows);

   /* start aggregation heuristic for each row in the LP */
   ncuts = 0;
   if( maxtries < 0 )
      maxtries = INT_MAX;
   if( maxfails < 0 )
      maxfails = INT_MAX;
   else if( depth == 0 && 2*SCIPgetNSepaRounds(scip) < maxfails )
      maxfails += maxfails - 2*SCIPgetNSepaRounds(scip); /* allow up to double as many fails in early separounds of root node */
   ntries = 0;
   nfails = 0;
   for( r = 0; r < nrows && ntries < maxtries && ncuts < maxsepacuts && rowscores[roworder[r]] > 0.0
           && !SCIPisStopped(scip); r++ )
   {
      SCIP_Bool wastried;
      int oldncuts;

      oldncuts = ncuts;
      SCIP_CALL( aggregation(scip, sepa, sepadata, sol, varsolvals, bestcontlbs, bestcontubs, contvarscorebounds,
            rowlhsscores, rowrhsscores, roworder[r], maxaggrs, maxslack, maxconts, &wastried, &cutoff, &ncuts) );
      if ( cutoff )
         break;

      if( !wastried )
         continue;
      ntries++;

      if( ncuts == oldncuts )
      {
         nfails++;
         if( nfails >= maxfails )
            break;
      }
      else
         nfails = 0;
   }

   /* free data structure */
   SCIPfreeBufferArray(scip, &contvarscorebounds);
   SCIPfreeBufferArray(scip, &bestcontubs);
   SCIPfreeBufferArray(scip, &bestcontlbs);
   SCIPfreeBufferArray(scip, &varsolvals);
   SCIPfreeBufferArray(scip, &roworder);
   SCIPfreeBufferArray(scip, &rowscores);
   SCIPfreeBufferArray(scip, &rowrhsscores);
   SCIPfreeBufferArray(scip, &rowlhsscores);

   if ( cutoff )
      *result = SCIP_CUTOFF;
   else if ( ncuts > 0 )
      *result = SCIP_SEPARATED;

   return SCIP_OKAY;
}


/*
 * Callback methods of separator
 */

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

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

   return SCIP_OKAY;
}

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

   /* free separator data */
   sepadata = SCIPsepaGetData(sepa);
   assert(sepadata != NULL);

   SCIPfreeMemory(scip, &sepadata);

   SCIPsepaSetData(sepa, NULL);

   return SCIP_OKAY;
}


/** LP solution separation method of separator */
static
SCIP_DECL_SEPAEXECLP(sepaExeclpCmir)
{  /*lint --e{715}*/

   *result = SCIP_DIDNOTRUN;

   /* only call separator, if we are not close to terminating */
   if( SCIPisStopped(scip) )
      return SCIP_OKAY;

   /* only call separator, if an optimal LP solution is at hand */
   if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
      return SCIP_OKAY;

   /* only call separator, if there are fractional variables */
   if( SCIPgetNLPBranchCands(scip) == 0 )
      return SCIP_OKAY;

   SCIP_CALL( separateCuts(scip, sepa, NULL, result) );

   return SCIP_OKAY;
}


/** arbitrary primal solution separation method of separator */
static
SCIP_DECL_SEPAEXECSOL(sepaExecsolCmir)
{  /*lint --e{715}*/

   *result = SCIP_DIDNOTRUN;

   SCIP_CALL( separateCuts(scip, sepa, sol, result) );

   return SCIP_OKAY;
}


/*
 * separator specific interface methods
 */

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

   /* create cmir separator data */
   SCIP_CALL( SCIPallocMemory(scip, &sepadata) );

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

   assert(sepa != NULL);

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

   /* add cmir separator parameters */
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxrounds",
         "maximal number of cmir separation rounds per node (-1: unlimited)",
         &sepadata->maxrounds, FALSE, DEFAULT_MAXROUNDS, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxroundsroot",
         "maximal number of cmir separation rounds in the root node (-1: unlimited)",
         &sepadata->maxroundsroot, FALSE, DEFAULT_MAXROUNDSROOT, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxtries",
         "maximal number of rows to start aggregation with per separation round (-1: unlimited)",
         &sepadata->maxtries, TRUE, DEFAULT_MAXTRIES, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxtriesroot",
         "maximal number of rows to start aggregation with per separation round in the root node (-1: unlimited)",
         &sepadata->maxtriesroot, TRUE, DEFAULT_MAXTRIESROOT, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxfails",
         "maximal number of consecutive unsuccessful aggregation tries (-1: unlimited)",
         &sepadata->maxfails, TRUE, DEFAULT_MAXFAILS, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxfailsroot",
         "maximal number of consecutive unsuccessful aggregation tries in the root node (-1: unlimited)",
         &sepadata->maxfailsroot, TRUE, DEFAULT_MAXFAILSROOT, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxaggrs",
         "maximal number of aggregations for each row per separation round",
         &sepadata->maxaggrs, TRUE, DEFAULT_MAXAGGRS, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxaggrsroot",
         "maximal number of aggregations for each row per separation round in the root node",
         &sepadata->maxaggrsroot, TRUE, DEFAULT_MAXAGGRSROOT, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxsepacuts",
         "maximal number of cmir cuts separated per separation round",
         &sepadata->maxsepacuts, FALSE, DEFAULT_MAXSEPACUTS, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxsepacutsroot",
         "maximal number of cmir cuts separated per separation round in the root node",
         &sepadata->maxsepacutsroot, FALSE, DEFAULT_MAXSEPACUTSROOT, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/maxslack",
         "maximal slack of rows to be used in aggregation",
         &sepadata->maxslack, TRUE, DEFAULT_MAXSLACK, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/maxslackroot",
         "maximal slack of rows to be used in aggregation in the root node",
         &sepadata->maxslackroot, TRUE, DEFAULT_MAXSLACKROOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/densityscore",
         "weight of row density in the aggregation scoring of the rows",
         &sepadata->densityscore, TRUE, DEFAULT_DENSITYSCORE, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/slackscore",
         "weight of slack in the aggregation scoring of the rows",
         &sepadata->slackscore, TRUE, DEFAULT_SLACKSCORE, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/maxaggdensity",
         "maximal density of aggregated row",
         &sepadata->maxaggdensity, TRUE, DEFAULT_MAXAGGDENSITY, 0.0, 1.0, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/maxrowdensity",
         "maximal density of row to be used in aggregation",
         &sepadata->maxrowdensity, TRUE, DEFAULT_MAXROWDENSITY, 0.0, 1.0, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/densityoffset",
         "additional number of variables allowed in row on top of density",
         &sepadata->densityoffset, TRUE, DEFAULT_DENSITYOFFSET, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/maxrowfac",
         "maximal row aggregation factor",
         &sepadata->maxrowfac, TRUE, DEFAULT_MAXROWFAC, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxtestdelta",
         "maximal number of different deltas to try (-1: unlimited)",
         &sepadata->maxtestdelta, TRUE, DEFAULT_MAXTESTDELTA, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxconts",
         "maximal number of active continuous variables in aggregated row", 
         &sepadata->maxconts, TRUE, DEFAULT_MAXCONTS, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "separating/cmir/maxcontsroot",
         "maximal number of active continuous variables in aggregated row in the root node", 
         &sepadata->maxcontsroot, TRUE, DEFAULT_MAXCONTSROOT, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "separating/cmir/aggrtol",
         "tolerance for bound distances used to select continuous variable in current aggregated constraint to be eliminated",
         &sepadata->aggrtol, TRUE, DEFAULT_AGGRTOL, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddBoolParam(scip,
         "separating/cmir/trynegscaling",
         "should negative values also be tested in scaling?",
         &sepadata->trynegscaling, TRUE, DEFAULT_TRYNEGSCALING, NULL, NULL) );
   SCIP_CALL( SCIPaddBoolParam(scip,
         "separating/cmir/fixintegralrhs",
         "should an additional variable be complemented if f0 = 0?",
         &sepadata->fixintegralrhs, TRUE, DEFAULT_FIXINTEGRALRHS, NULL, NULL) );
   SCIP_CALL( SCIPaddBoolParam(scip,
         "separating/cmir/dynamiccuts",
         "should generated cuts be removed from the LP if they are no longer tight?",
         &sepadata->dynamiccuts, FALSE, DEFAULT_DYNAMICCUTS, NULL, NULL) );

   return SCIP_OKAY;
}