branch_relpscost.c

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/*                  This file is part of the program and library             */
/*         SCIP --- Solving Constraint Integer Programs                      */
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/*  Copyright (c) 2002-2026 Zuse Institute Berlin (ZIB)                      */
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/**@file   branch_relpscost.c
 * @ingroup DEFPLUGINS_BRANCH
 * @brief  reliable pseudo costs branching rule
 * @author Tobias Achterberg
 * @author Timo Berthold
 * @author Gerald Gamrath
 * @author Gioni Mexi
 * @author Marc Pfetsch
 * @author Krunal Patel
 */
 
/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
 
#include "blockmemshell/memory.h"
#include "scip/branch_relpscost.h"
#include "scip/treemodel.h"
#include "scip/cons_and.h"
#include "scip/pub_branch.h"
#include "scip/pub_cons.h"
#include "scip/scip_exact.h"
#include "scip/pub_message.h"
#include "scip/pub_misc.h"
#include "scip/pub_sol.h"
#include "scip/pub_tree.h"
#include "scip/pub_var.h"
#include "scip/scip_branch.h"
#include "scip/scip_cons.h"
#include "scip/scip_general.h"
#include "scip/scip_lp.h"
#include "scip/scip_mem.h"
#include "scip/scip_message.h"
#include "scip/scip_nlp.h"
#include "scip/scip_numerics.h"
#include "scip/scip_param.h"
#include "scip/scip_prob.h"
#include "scip/scip_randnumgen.h"
#include "scip/scip_sol.h"
#include "scip/scip_solvingstats.h"
#include "scip/scip_tree.h"
#include "scip/scip_var.h"
#include "scip/prop_symmetry.h"
#include "scip/symmetry.h"
#include <string.h>
 
#define BRANCHRULE_NAME          "relpscost"
#define BRANCHRULE_DESC          "reliability branching on pseudo cost values"
#define BRANCHRULE_PRIORITY      10000
#define BRANCHRULE_MAXDEPTH      -1
#define BRANCHRULE_MAXBOUNDDIST  1.0
 
#define DEFAULT_CONFLICTWEIGHT   0.01        /**< weight in score calculations for conflict score */
#define DEFAULT_CONFLENGTHWEIGHT 0.0         /**< weight in score calculations for conflict length score*/
#define DEFAULT_INFERENCEWEIGHT  0.0001      /**< weight in score calculations for inference score */
#define DEFAULT_CUTOFFWEIGHT     0.0001      /**< weight in score calculations for cutoff score */
#define DEFAULT_GMIAVGEFFWEIGHT  0.0         /**< weight in score calculations of average GMI cut normed efficacies */
#define DEFAULT_GMILASTEFFWEIGHT 0.00001     /**< weight in score calculations of last GMI cut normed efficacy */
#define DEFAULT_PSCOSTWEIGHT     1.0         /**< weight in score calculations for pseudo cost score */
#define DEFAULT_NLSCOREWEIGHT    0.1         /**< weight in score calculations for nlcount score */
#define DEFAULT_MINRELIABLE      1.0         /**< minimal value for minimum pseudo cost size to regard pseudo cost value as reliable */
#define DEFAULT_MAXRELIABLE      5.0         /**< maximal value for minimum pseudo cost size to regard pseudo cost value as reliable */
#define DEFAULT_SBITERQUOT       0.5         /**< maximal fraction of strong branching LP iterations compared to normal iterations */
#define DEFAULT_DYNAMICLOOKAHEADQUOT 0.6     /**< apply dynamic lookahead after this fraction maxlookahead is reached */
#define DEFAULT_SBITEROFS        100000      /**< additional number of allowed strong branching LP iterations */
#define DEFAULT_MAXLOOKAHEAD     9           /**< maximal number of further variables evaluated without better score */
#define DEFAULT_INITCAND         100         /**< maximal number of candidates initialized with strong branching per node */
#define DEFAULT_INITITER         0           /**< iteration limit for strong branching initialization of pseudo cost entries (0: auto) */
#define DEFAULT_MAXBDCHGS        5           /**< maximal number of bound tightenings before the node is reevaluated (-1: unlimited) */
#define DEFAULT_MAXPROPROUNDS    -2          /**< maximum number of propagation rounds to be performed during strong branching
                                              *   before solving the LP (-1: no limit, -2: parameter settings) */
#define DEFAULT_PROBINGBOUNDS    TRUE        /**< should valid bounds be identified in a probing-like fashion during strong
                                              *   branching (only with propagation)? */
#define DEFAULT_USERELERRORFORRELIABILITY FALSE /**< should reliability be based on relative errors? */
#define DEFAULT_LOWERRORTOL      0.05        /**< lowest tolerance beneath which relative errors are reliable */
#define DEFAULT_HIGHERRORTOL     1.0         /**< highest tolerance beneath which relative errors are reliable */
#define DEFAULT_USEHYPTESTFORRELIABILITY FALSE /**< should the strong branching decision be based on a hypothesis test? */
#define DEFAULT_USEDYNAMICCONFIDENCE FALSE   /**< should the confidence level be adjusted dynamically? */
#define DEFAULT_STORESEMIINITCOSTS FALSE     /**< should strong branching result be considered for pseudo costs if the other direction was infeasible? */
#define DEFAULT_USESBLOCALINFO   FALSE       /**< should the scoring function use only local cutoff and inference information obtained for strong branching candidates? */
#define DEFAULT_CONFIDENCELEVEL  2           /**< The confidence level for statistical methods, between 0 (Min) and 4 (Max). */
#define DEFAULT_SKIPBADINITCANDS TRUE        /**< should branching rule skip candidates that have a low probability to be
                                              *  better than the best strong-branching or pseudo-candidate? */
#define DEFAULT_STARTRANDSEED    5           /**< start random seed for random number generation */
#define DEFAULT_DYNAMICLOOKAHEAD FALSE       /**< should we use a dynamic lookahead based on a tree size estimation of further strong branchings? */
#define DEFAULT_MINSAMPLESIZE    10          /**< minimum sample size to estimate the tree size for dynamic lookahead */
#define DEFAULT_DYNAMICLOOKDISTRIBUTION 1    /**< which distribution should be used for dynamic lookahead? 0=exponential, 1=Pareto, 2=log-normal */
#define DEFAULT_RANDINITORDER    FALSE       /**< should slight perturbation of scores be used to break ties in the prior scores? */
#define DEFAULT_USESMALLWEIGHTSITLIM FALSE   /**< should smaller weights be used for pseudo cost updates after hitting the LP iteration limit? */
#define DEFAULT_DYNAMICWEIGHTS   TRUE        /**< should the weights of the branching rule be adjusted dynamically during solving based
                                              *   infeasible and objective leaf counters? */
#define DEFAULT_DEGENERACYAWARE  1           /**< should degeneracy be taken into account to update weights and skip strong branching? (0: off, 1: after root, 2: always)*/
 
/* symmetry handling */
#define DEFAULT_FILTERCANDSSYM   FALSE       /**< Use symmetry to filter branching candidates? */
#define DEFAULT_TRANSSYMPSCOST   FALSE       /**< Transfer pscost information to symmetric variables if filtering is performed? */
 
/* distribution to use for lookahead strong branching */
#define EXPONENTIALDISTRIBUTION 0
#define PARETODISTRIBUTION 1
#define LOGNORMALDISTRIBUTION 2
 
/* shift for geometric mean of left and right gains */
#define GEOMMEANSHIFT 0.01
/* maximum gain with which we update the estimated left and right dual gains */
#define MAXGAINTHRESHOLD 1e15
/* minimum considered expected dual gain and probability for lookahead strong branching */
#define MINGAINTHRESHOLD 1e-5
 
/* discounted pseudo cost */
#define BRANCHRULE_DISCOUNTFACTOR        0.2 /**< default discount factor for discounted pseudo costs.*/
 
/** branching rule data */
struct SCIP_BranchruleData
{
   SCIP_Real             conflictweight;     /**< weight in score calculations for conflict score */
   SCIP_Real             conflengthweight;   /**< weight in score calculations for conflict length score */
   SCIP_Real             inferenceweight;    /**< weight in score calculations for inference score */
   SCIP_Real             cutoffweight;       /**< weight in score calculations for cutoff score */
   SCIP_Real             gmiavgeffweight;    /**< weight in score calculations of average GMI normed cut efficacies */
   SCIP_Real             gmilasteffweight;   /**< weight in score calculations of last GMI cut normalized efficacy */
   SCIP_Real             pscostweight;       /**< weight in score calculations for pseudo cost score */
   SCIP_Real             nlscoreweight;      /**< weight in score calculations for nlcount score */
   SCIP_Real             minreliable;        /**< minimal value for minimum pseudo cost size to regard pseudo cost value as reliable */
   SCIP_Real             maxreliable;        /**< maximal value for minimum pseudo cost size to regard pseudo cost value as reliable */
   SCIP_Real             sbiterquot;         /**< maximal fraction of strong branching LP iterations compared to normal iterations */
   SCIP_Real             meandualgain;       /**< mean dual gain of all strong branchings */
   SCIP_Real             currmeandualgain;   /**< current mean dual gain in current node */
   SCIP_Real             maxmeangain;        /**< maximal dual gain of all strong branchings */
   SCIP_Real             minmeangain;        /**< minimal dual gain of all strong branchings */
   SCIP_Real             sumlogmeangains;    /**< sum of logarithms of all means of the dual gains */
   SCIP_Real             logstdevgain;       /**< logarithm of the standard deviation of the means of the dual gains */
   SCIP_Real             nzerogains;         /**< number of zero dual gains */
   int                   currndualgains;     /**< number of dual gains used in the computation of the mean from current node */
   int                   sbiterofs;          /**< additional number of allowed strong branching LP iterations */
   int                   maxlookahead;       /**< maximal number of further variables evaluated without better score */
   int                   initcand;           /**< maximal number of candidates initialized with strong branching per node */
   int                   inititer;           /**< iteration limit for strong branching initialization of pseudo cost entries (0: auto) */
   int                   maxbdchgs;          /**< maximal number of bound tightenings before the node is reevaluated (-1: unlimited) */
   int                   maxproprounds;      /**< maximum number of propagation rounds to be performed during strong branching
                                              *   before solving the LP (-1: no limit, -2: parameter settings) */
   SCIP_Bool             probingbounds;      /**< should valid bounds be identified in a probing-like fashion during strong
                                              *   branching (only with propagation)? */
   SCIP_Bool             userelerrorforreliability; /**< should reliability be based on relative errors? */
   SCIP_Real             lowerrortol;        /**< lowest tolerance beneath which relative errors are reliable */
   SCIP_Real             higherrortol;       /**< highest tolerance beneath which relative errors are reliable */
   SCIP_Bool             usehyptestforreliability; /**< should the strong branching decision be based on a hypothesis test? */
   SCIP_Bool             usedynamicconfidence; /**< should the confidence level be adjusted dynamically? */
   SCIP_Bool             storesemiinitcosts; /**< should strong branching result be considered for pseudo costs if the
                                              *   other direction was infeasible? */
   SCIP_Bool             usesblocalinfo;     /**< should the scoring function disregard cutoffs for variable if sb-lookahead was feasible ? */
   SCIP_Bool             skipbadinitcands;   /**< should branching rule skip candidates that have a low probability to be
                                               *  better than the best strong-branching or pseudo-candidate? */
   SCIP_Bool             dynamicweights;     /**< should the weights of the branching rule be adjusted dynamically during
                                              *   solving based on objective and infeasible leaf counters? */
   SCIP_Real             dynamiclookaheadquot; /**< apply dynamic lookahead after this fraction maxlookahead is reached */
   SCIP_Bool             dynamiclookahead;   /**< should we use a dynamic lookahead based on a tree size estimation of further strong branchings? */
   int                   dynamiclookdistribution; /**< which distribution should be used for dynamic lookahead? 0=exponential, 1=Pareto, 2=log-normal */
   int                   minsamplesize;      /**< minimum sample size to estimate the tree size for dynamic lookahead */
   int                   degeneracyaware;    /**< should degeneracy be taken into account to update weights and skip strong branching? (0: off, 1: after root, 2: always) */
   int                   confidencelevel;    /**< The confidence level for statistical methods, between 0 (Min) and 4 (Max). */
   int*                  nlcount;            /**< array to store nonlinear count values */
   int                   nlcountsize;        /**< length of nlcount array */
   int                   nlcountmax;         /**< maximum entry in nlcount array or 1 if NULL */
   SCIP_Bool             randinitorder;      /**< should slight perturbation of scores be used to break ties in the prior scores? */
   SCIP_RANDNUMGEN*      randnumgen;         /**< random number generator */
   int                   startrandseed;      /**< start random seed for random number generation */
   SCIP_Bool             usesmallweightsitlim; /**< should smaller weights be used for pseudo cost updates after hitting the LP iteration limit? */
   SCIP_TREEMODEL*       treemodel;          /**< Parameters for the Treemodel branching rules */
 
   /* for symmetry */
   SCIP_Bool             filtercandssym;     /**< Use symmetry to filter branching candidates? */
   SCIP_Bool             transsympscost;     /**< Transfer pscost information to symmetric variables? */
 
   SCIP_Bool             nosymmetry;         /**< No symmetry present? */
   int*                  orbits;             /**< array of non-trivial orbits */
   int*                  orbitbegins;        /**< array containing begin positions of new orbits in orbits array */
   int                   norbits;            /**< pointer to number of orbits currently stored in orbits */
   int*                  varorbitmap;        /**< array for storing indices of the containing orbit for each variable */
   int*                  orbitrep;           /**< representative variable of each orbit */
   SCIP_VAR**            permvars;           /**< variables on which permutations act */
   int                   npermvars;          /**< number of variables for permutations */
   SCIP_HASHMAP*         permvarmap;         /**< map of variables to indices in permvars array */
 
   /* for discounted pseudo costs */
   SCIP_Real             discountfactor;     /**< discount factor for discounted pseudo costs.*/
};
 
/*
 * local methods
 */
 
/** initialize orbits */
static
SCIP_RETCODE initOrbits(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULEDATA*  branchruledata      /**< branching rule data */
   )
{
   int** permstrans = NULL;
   int* components = NULL;
   int* componentbegins = NULL;
   int* vartocomponent = NULL;
   int ncomponents = 0;
   int nperms = -1;
 
   assert( scip != NULL );
   assert( branchruledata != NULL );
   assert( branchruledata->filtercandssym );
 
   /* exit if no symmetry or orbits already available */
   if( branchruledata->nosymmetry || branchruledata->orbits != NULL )
      return SCIP_OKAY;
 
   assert( branchruledata->orbitbegins ==  NULL );
   assert( branchruledata->varorbitmap == NULL );
   assert( branchruledata->orbitrep == NULL );
 
   /* obtain symmetry including permutations */
   SCIP_CALL( SCIPgetSymmetry(scip, &branchruledata->npermvars, &branchruledata->permvars, &branchruledata->permvarmap,
         &nperms, NULL, &permstrans, NULL, NULL, &components, &componentbegins, &vartocomponent, &ncomponents) );
 
   /* turn off symmetry handling if there is no symmetry or the number of variables is not equal */
   if( nperms <= 0 || branchruledata->npermvars != SCIPgetNVars(scip) )
   {
      branchruledata->nosymmetry = TRUE;
      return SCIP_OKAY;
   }
   assert( branchruledata->permvars != NULL );
   assert( branchruledata->permvarmap != NULL );
   assert( branchruledata->npermvars > 0 );
   assert( permstrans != NULL );
   assert( components != NULL );
   assert( componentbegins != NULL );
   assert( vartocomponent != NULL );
   assert( ncomponents > 0 );
 
   SCIP_CALL( SCIPallocBlockMemoryArray(scip, &branchruledata->orbits, branchruledata->npermvars) );
   SCIP_CALL( SCIPallocBlockMemoryArray(scip, &branchruledata->orbitbegins, branchruledata->npermvars) );
   SCIP_CALL( SCIPallocBlockMemoryArray(scip, &branchruledata->varorbitmap, branchruledata->npermvars) );
   SCIP_CALL( SCIPallocBlockMemoryArray(scip, &branchruledata->orbitrep, branchruledata->npermvars) );
 
   /* Compute orbits on all variables, since this might help for branching and this computation is only done once. */
   SCIP_CALL( SCIPcomputeOrbitsComponentsSym(scip, branchruledata->npermvars, permstrans, nperms,
         components, componentbegins, vartocomponent, ncomponents,
         branchruledata->orbits, branchruledata->orbitbegins, &branchruledata->norbits, branchruledata->varorbitmap) );
   assert( branchruledata->norbits < branchruledata->npermvars );
 
   return SCIP_OKAY;
}
 
/** filter out symmetric variables from branching variables */
static
SCIP_RETCODE filterSymmetricVariables(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULEDATA*  branchruledata,     /**< branching rule data */
   SCIP_VAR**            origbranchcands,    /**< original branching candidates */
   SCIP_Real*            origbranchcandssol, /**< original solution value for the branching candidates */
   SCIP_Real*            origbranchcandsfrac,/**< original fractional part of the branching candidates */
   int                   norigbranchcands,   /**< original number of branching candidates */
   SCIP_VAR**            branchcands,        /**< branching candidates */
   SCIP_Real*            branchcandssol,     /**< solution value for the branching candidates */
   SCIP_Real*            branchcandsfrac,    /**< fractional part of the branching candidates */
   int*                  branchorbitidx,     /**< array of indices of orbit of branching candidates */
   int*                  nbranchcands        /**< pointer to store number of branching candidates */
   )
{
   int i;
 
   assert( scip != NULL );
   assert( branchruledata != NULL );
   assert( origbranchcands != NULL );
   assert( origbranchcandssol != NULL );
   assert( origbranchcandsfrac != NULL );
   assert( branchcands != NULL );
   assert( branchcandssol != NULL );
   assert( branchcandsfrac != NULL );
   assert( nbranchcands != NULL );
 
   assert( ! branchruledata->nosymmetry );
   assert( branchruledata->orbitbegins != NULL );
   assert( branchruledata->orbits != NULL );
   assert( branchruledata->permvarmap != NULL );
   assert( branchruledata->varorbitmap != NULL );
   assert( branchruledata->orbitrep != NULL );
   assert( branchruledata->norbits < branchruledata->npermvars );
 
   /* init representatives (used to see whether variable is the first in an orbit) */
   for( i = 0; i < branchruledata->norbits; ++i )
      branchruledata->orbitrep[i] = -1;
 
   /* loop through branching variables, determine orbit and whether they are the first ones */
   *nbranchcands = 0;
   for( i = 0; i < norigbranchcands; ++i )
   {
      int orbitidx = -1;
      int varidx;
 
      varidx = SCIPhashmapGetImageInt(branchruledata->permvarmap, (void*) origbranchcands[i]);
      if( varidx != INT_MAX )
      {
         assert( 0 <= varidx && varidx < branchruledata->npermvars );
         orbitidx = branchruledata->varorbitmap[varidx];
      }
      assert( -1 <= orbitidx && orbitidx < branchruledata->norbits );
 
      /* Check whether the variable is not present (can happen if variable was added after computing symmetries or is in
       * a singleton orbit). */
      if( orbitidx == -1 )
      {
         branchcands[*nbranchcands] = origbranchcands[i];
         branchcandssol[*nbranchcands] = origbranchcandssol[i];
         branchcandsfrac[*nbranchcands] = origbranchcandsfrac[i];
         branchorbitidx[*nbranchcands] = -1;
         ++(*nbranchcands);
      }
      else if( branchruledata->orbitrep[orbitidx] == -1 )
      {
         /* if variable is the first in a nontrivial orbit */
         assert( 0 <= varidx && varidx < branchruledata->npermvars );
         branchruledata->orbitrep[orbitidx] = varidx;
         branchcands[*nbranchcands] = origbranchcands[i];
         branchcandssol[*nbranchcands] = origbranchcandssol[i];
         branchcandsfrac[*nbranchcands] = origbranchcandsfrac[i];
         branchorbitidx[*nbranchcands] = orbitidx;
         ++(*nbranchcands);
      }
   }
 
   SCIPdebugMsg(scip, "Filtered out %d variables by symmetry.\n", norigbranchcands - *nbranchcands);
 
   return SCIP_OKAY;
}
 
/** updates the pseudo costs of the given variable and all its symmetric variables */
static
SCIP_RETCODE SCIPupdateVarPseudocostSymmetric(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULEDATA*  branchruledata,     /**< branching rule data */
   SCIP_VAR*             branchvar,          /**< branching variable candidate */
   int*                  branchorbitidx,     /**< array of orbit indices */
   int                   branchvaridx,       /**< index of variable in branchorbitidx */
   SCIP_Real             solvaldelta,        /**< difference of variable's new LP value - old LP value */
   SCIP_Real             objdelta,           /**< difference of new LP's objective value - old LP's objective value */
   SCIP_Real             weight              /**< weight in (0,1] of this update in pseudo cost sum */
   )
{
   int orbitidx;
   int j;
   SCIP_Bool useancpscost;
 
   assert( scip != NULL );
   assert( branchruledata != NULL );
 
   /* update the discounted pseudo cost of the current node branched variable */
   SCIP_CALL( SCIPgetBoolParam(scip, "branching/collectancpscost", &useancpscost) );
   if( useancpscost )
   {
      SCIP_NODE* currentnode;
 
      currentnode = SCIPgetFocusNode(scip);
      if( SCIPnodeGetDepth(currentnode) > 0 )
      {
         SCIP_DOMCHG* domchange;
         SCIP_BOUNDCHG* boundchg;
         int nboundchgs;
         SCIP_VAR* var;
         int i;
         SCIP_Real parentlpsolval;
         SCIP_Real parentsolvedelta;
 
         domchange = SCIPnodeGetDomchg(currentnode);
         nboundchgs = SCIPdomchgGetNBoundchgs(domchange);
         for( i = 0; i < nboundchgs; ++i )
         {
            boundchg = SCIPdomchgGetBoundchg(domchange, i);
            var = SCIPboundchgGetVar(boundchg);
            assert(var != NULL);
 
            if( SCIPboundchgGetBoundchgtype(boundchg) == SCIP_BOUNDCHGTYPE_BRANCHING && SCIPvarIsIntegral(var) )
            {
               parentlpsolval = SCIPboundchgGetLPSolVal(boundchg);
               if( parentlpsolval >= SCIP_INVALID )
                  continue;
               parentsolvedelta = SCIPboundchgGetNewbound(boundchg) - parentlpsolval;
               SCIP_CALL( SCIPupdateVarAncPseudocost(scip, var, parentsolvedelta, objdelta, weight) );
            }
         }
      }
   }
 
   if( branchruledata->nosymmetry || ! branchruledata->transsympscost || branchorbitidx == NULL )
   {
      /* use original update function */
      SCIP_CALL( SCIPupdateVarPseudocost(scip, branchvar, solvaldelta, objdelta, weight) );
      return SCIP_OKAY;
   }
 
   assert( branchruledata->orbitbegins != NULL );
   assert( branchruledata->orbits != NULL );
   assert( 0 <= branchvaridx && branchvaridx < branchruledata->npermvars );
 
   orbitidx = branchorbitidx[branchvaridx];
   if( orbitidx < 0 )
   {
      /* only update given variable */
      SCIP_CALL( SCIPupdateVarPseudocost(scip, branchvar, solvaldelta, objdelta, weight) );
      return SCIP_OKAY;
   }
   assert( 0 <= orbitidx && orbitidx < branchruledata->norbits );
 
   /* loop through orbit containing variable and update pseudo costs for all variables */
   for( j = branchruledata->orbitbegins[orbitidx]; j < branchruledata->orbitbegins[orbitidx+1]; ++j )
   {
      SCIP_VAR* var;
      int idx;
 
      idx = branchruledata->orbits[j];
      assert( 0 <= idx && idx < branchruledata->npermvars );
 
      var = branchruledata->permvars[idx];
      assert( var != NULL );
 
      if( SCIPvarIsActive(var) )
      {
         SCIP_CALL( SCIPupdateVarPseudocost(scip, var, solvaldelta, objdelta, weight) );
      }
   }
 
   return SCIP_OKAY;
}
 
/**! [SnippetCodeStyleDeclaration] */
 
/** counts number of nonlinear constraints in which each variable appears */
static
SCIP_RETCODE countNonlinearities(
   SCIP*                 scip,               /**< SCIP data structure */
   int*                  nlcount,            /**< pointer to array for storing count values */
   int                   nlcountsize,        /**< buffer for storing length of nlcount array */
   int*                  nlcountmax          /**< buffer for storing maximum value in nlcount array */
   )
{
   SCIP_CONSHDLR* andconshdlr;
   SCIP_VAR** vars;
   int nvars;
   int i;
 
/**! [SnippetCodeStyleDeclaration] */
 
   assert(scip != NULL);
   assert(nlcount != NULL);
   assert(nlcountmax != NULL);
 
   SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
   assert(nlcountsize >= nvars);
 
   /* get nonlinearity for constraints in NLP */
   if( SCIPisNLPConstructed(scip) )
   {
      assert(SCIPgetNNLPVars(scip) == nvars);
      SCIP_CALL( SCIPgetNLPVarsNonlinearity(scip, nlcount) );
   }
   else
   {
      BMSclearMemoryArray(nlcount, nvars);
   }
 
   /* increase counters for and constraints */
   andconshdlr = SCIPfindConshdlr(scip, "and");
   if( andconshdlr != NULL )
   {
      int c;
 
      for( c = 0; c < SCIPconshdlrGetNActiveConss(andconshdlr); ++c )
      {
         SCIP_CONS* andcons;
         SCIP_VAR** andvars;
         SCIP_VAR* andres;
         int probindex;
         int nandvars;
         int v;
 
         /* get constraint and variables */
         andcons = SCIPconshdlrGetConss(andconshdlr)[c];
         nandvars = SCIPgetNVarsAnd(scip, andcons);
         andvars = SCIPgetVarsAnd(scip, andcons);
         andres = SCIPgetResultantAnd(scip, andcons);
 
         /* get active index of resultant */
         probindex = SCIPvarGetProbindex(SCIPvarGetProbvar(andres));
 
         /* the resultant might be deleted */
         if( probindex >= 0 )
            ++nlcount[probindex];
 
         /**! [SnippetCodeStyleIfFor] */
         for( v = 0; v < nandvars; ++v )
         {
            /* get active index of operator */
            probindex = SCIPvarGetProbindex(SCIPvarGetProbvar(andvars[v]));
 
            /* the operator might be deleted */
            if( probindex >= 0 )
               ++nlcount[probindex];
         }
         /**! [SnippetCodeStyleIfFor] */
      }
   }
 
   /* compute maximum count value */
   *nlcountmax = 1;
   for( i = 0; i < nvars; ++i )
   {
      if( *nlcountmax < nlcount[i] )
         *nlcountmax = nlcount[i];
   }
 
   return SCIP_OKAY;
}
 
static
SCIP_RETCODE branchruledataEnsureNlcount(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULEDATA*  branchruledata      /**< branching rule data */
   )
{
   int nvars;
 
   assert(scip != NULL);
   assert(branchruledata != NULL);
 
   nvars = SCIPgetNVars(scip);
 
   /**@todo test whether we want to apply this as if problem has only and constraints */
   /**@todo update changes in and constraints */
   if( branchruledata->nlscoreweight > 0.0 ) /*  && SCIPisNLPConstructed(scip) */
   {
      if( branchruledata->nlcount == NULL )
      {
         SCIP_CALL( SCIPallocBlockMemoryArray(scip, &branchruledata->nlcount, nvars) );
         branchruledata->nlcountsize = nvars;
 
         SCIP_CALL( countNonlinearities(scip, branchruledata->nlcount, branchruledata->nlcountsize, &branchruledata->nlcountmax) );
      }
      else if( branchruledata->nlcountsize < nvars )
      {
         SCIP_CALL( SCIPreallocBlockMemoryArray(scip, &branchruledata->nlcount, branchruledata->nlcountsize, nvars) );
         /**@todo should we update nlcounts for new variables? */
         BMSclearMemoryArray(&(branchruledata->nlcount[branchruledata->nlcountsize]), nvars - branchruledata->nlcountsize); /*lint !e866*/
         branchruledata->nlcountsize = nvars;
      }
      assert(branchruledata->nlcount != NULL);
      assert(branchruledata->nlcountsize == nvars);
      assert(branchruledata->nlcountmax >= 1);
   }
   else
   {
      SCIPfreeBlockMemoryArrayNull(scip, &branchruledata->nlcount, branchruledata->nlcountsize);
      branchruledata->nlcountsize = 0;
      branchruledata->nlcountmax = 1;
   }
 
   return SCIP_OKAY;
}
 
 
/** calculates nlscore value between 0 and 1 */
static
SCIP_Real calcNlscore(
   SCIP*                 scip,               /**< SCIP data structure */
   int*                  nlcount,            /**< array to store count values */
   int                   nlcountmax,         /**< maximum value in nlcount array */
   int                   probindex           /**< index of branching candidate */
   )
{
   if( nlcountmax >= 1 && nlcount != NULL )
   {
      SCIP_Real nlscore;
 
      assert(scip != NULL);
      assert(probindex >= 0);
      assert(probindex < SCIPgetNVars(scip));
 
      nlscore = nlcount[probindex] / (SCIP_Real)nlcountmax;
 
      assert(nlscore >= 0.0);
      assert(nlscore <= 1.0);
      return nlscore;
   }
   else
      return 0.0;
}
 
/** calculates an overall score value for the given individual score values */
static
SCIP_Real calcScore(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULEDATA*  branchruledata,     /**< branching rule data */
   SCIP_Real             conflictscore,      /**< conflict score of current variable */
   SCIP_Real             avgconflictscore,   /**< average conflict score */
   SCIP_Real             conflengthscore,    /**< conflict length score of current variable */
   SCIP_Real             avgconflengthscore, /**< average conflict length score */
   SCIP_Real             inferencescore,     /**< inference score of current variable */
   SCIP_Real             avginferencescore,  /**< average inference score */
   SCIP_Real             cutoffscore,        /**< cutoff score of current variable */
   SCIP_Real             avgcutoffscore,     /**< average cutoff score */
   SCIP_Real             gmieffscore,        /**< normalized-eff of avg GMI cuts from row when var was frac and basic */
   SCIP_Real             lastgmieffscore,    /**< last normalized gmieffscore when var was frac and basic  */
   SCIP_Real             pscostscore,        /**< pscost score of current variable */
   SCIP_Real             avgpscostscore,     /**< average pscost score */
   SCIP_Real             nlscore,            /**< nonlinear score of current variable between 0 and 1 */
   SCIP_Real             frac,               /**< fractional value of variable in current solution */
   SCIP_Real             degeneracyfactor    /**< factor to apply because of degeneracy */
   )
{
   SCIP_Real score;
   SCIP_Real dynamicfactor;
 
   assert(branchruledata != NULL);
   assert(0.0 < frac && frac < 1.0);
 
   if( branchruledata->dynamicweights )
   {
      dynamicfactor = (SCIPgetNInfeasibleLeaves(scip) + 1.0) / (SCIPgetNObjlimLeaves(scip) + 1.0);
   }
   else
      dynamicfactor = 1.0;
 
   dynamicfactor *= degeneracyfactor;
 
   score = dynamicfactor * (branchruledata->conflictweight * (1.0 - 1.0/(1.0+conflictscore/avgconflictscore))
            + branchruledata->conflengthweight * (1.0 - 1.0/(1.0+conflengthscore/avgconflengthscore))
            + branchruledata->inferenceweight * (1.0 - 1.0/(1.0+inferencescore/avginferencescore))
            + branchruledata->cutoffweight * (1.0 - 1.0/(1.0+cutoffscore/avgcutoffscore))
            + branchruledata->gmiavgeffweight * gmieffscore + branchruledata->gmilasteffweight * lastgmieffscore)
         + branchruledata->pscostweight / dynamicfactor * (1.0 - 1.0/(1.0+pscostscore/avgpscostscore))
         + branchruledata->nlscoreweight * nlscore;
 
   /* avoid close to integral variables */
   if( MIN(frac, 1.0 - frac) < 10.0 * SCIPfeastol(scip) )
      score *= 1e-6;
 
   return score;
}
 
/** adds given index and direction to bound change arrays */
static
SCIP_RETCODE addBdchg(
   SCIP*                 scip,               /**< SCIP data structure */
   int**                 bdchginds,          /**< pointer to bound change index array */
   SCIP_BOUNDTYPE**      bdchgtypes,         /**< pointer to bound change types array */
   SCIP_Real**           bdchgbounds,        /**< pointer to bound change new bounds array */
   int*                  nbdchgs,            /**< pointer to number of bound changes */
   int                   ind,                /**< index to store in bound change index array */
   SCIP_BOUNDTYPE        type,               /**< type of the bound change to store in bound change type array */
   SCIP_Real             bound               /**< new bound to store in bound change new bounds array */
   )
{
   assert(bdchginds != NULL);
   assert(bdchgtypes != NULL);
   assert(bdchgbounds != NULL);
   assert(nbdchgs != NULL);
 
   SCIP_CALL( SCIPreallocBufferArray(scip, bdchginds, (*nbdchgs) + 1) );
   SCIP_CALL( SCIPreallocBufferArray(scip, bdchgtypes, (*nbdchgs) + 1) );
   SCIP_CALL( SCIPreallocBufferArray(scip, bdchgbounds, (*nbdchgs) + 1) );
   assert(*bdchginds != NULL);
   assert(*bdchgtypes != NULL);
   assert(*bdchgbounds != NULL);
 
   (*bdchginds)[*nbdchgs] = ind;
   (*bdchgtypes)[*nbdchgs] = type;
   (*bdchgbounds)[*nbdchgs] = bound;
   (*nbdchgs)++;
 
   return SCIP_OKAY;
}
 
/**! [SnippetCodeStyleStaticAsserts] */
/** frees bound change arrays */
static
void freeBdchgs(
   SCIP*                 scip,               /**< SCIP data structure */
   int**                 bdchginds,          /**< pointer to bound change index array */
   SCIP_BOUNDTYPE**      bdchgtypes,         /**< pointer to bound change types array */
   SCIP_Real**           bdchgbounds,        /**< pointer to bound change new bounds array */
   int*                  nbdchgs             /**< pointer to number of bound changes */
   )
{
   assert(scip != NULL);
   assert(bdchginds != NULL);
   assert(bdchgtypes != NULL);
   assert(bdchgbounds != NULL);
   assert(nbdchgs != NULL);
/**! [SnippetCodeStyleStaticAsserts] */
 
   SCIPfreeBufferArrayNull(scip, bdchgbounds);
   SCIPfreeBufferArrayNull(scip, bdchgtypes);
   SCIPfreeBufferArrayNull(scip, bdchginds);
   *nbdchgs = 0;
}
 
/** applies bound changes stored in bound change arrays */
static
SCIP_RETCODE applyBdchgs(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_VAR**            vars,               /**< problem variables */
   int*                  bdchginds,          /**< bound change index array */
   SCIP_BOUNDTYPE*       bdchgtypes,         /**< bound change types array */
   SCIP_Real*            bdchgbounds,        /**< bound change new bound array */
   int                   nbdchgs,            /**< number of bound changes */
   SCIP_RESULT*          result              /**< result pointer */
   )
{
#ifndef NDEBUG
   SCIP_BRANCHRULE* branchrule;
   SCIP_BRANCHRULEDATA* branchruledata;
#endif
   SCIP_Bool infeasible;
   SCIP_Bool tightened;
   int i;
 
   assert(vars != NULL);
 
#ifndef NDEBUG
   /* find branching rule */
   branchrule = SCIPfindBranchrule(scip, BRANCHRULE_NAME);
   assert(branchrule != NULL);
 
   /* get branching rule data */
   branchruledata = SCIPbranchruleGetData(branchrule);
   assert(branchruledata != NULL);
#endif
 
   SCIPdebugMsg(scip, "applying %d bound changes\n", nbdchgs);
 
   for( i = 0; i < nbdchgs; ++i )
   {
      int v;
 
      v = bdchginds[i];
 
      SCIPdebugMsg(scip, " -> <%s> [%g,%g]\n",
         SCIPvarGetName(vars[v]), SCIPvarGetLbLocal(vars[v]), SCIPvarGetUbLocal(vars[v]));
 
      if( bdchgtypes[i] == SCIP_BOUNDTYPE_LOWER )
      {
         /* change lower bound of variable to given bound */
         SCIP_CALL( SCIPtightenVarLb(scip, vars[v], bdchgbounds[i], TRUE, &infeasible, &tightened) );
         if( infeasible )
         {
            *result = SCIP_CUTOFF;
            return SCIP_OKAY;
         }
 
         /* if we did propagation, the bound change might already have been added */
         assert(tightened || (branchruledata->maxproprounds != 0));
      }
      else
      {
         assert(bdchgtypes[i] == SCIP_BOUNDTYPE_UPPER);
 
         /* change upper bound of variable to given bound */
         SCIP_CALL( SCIPtightenVarUb(scip, vars[v], bdchgbounds[i], TRUE, &infeasible, &tightened) );
         if( infeasible )
         {
            *result = SCIP_CUTOFF;
            return SCIP_OKAY;
         }
 
         /* if we did propagation, the bound change might already have been added */
         assert(tightened || (branchruledata->maxproprounds != 0));
      }
      SCIPdebugMsg(scip, "  -> [%g,%g]\n", SCIPvarGetLbLocal(vars[v]), SCIPvarGetUbLocal(vars[v]));
   }
 
   return SCIP_OKAY;
}
 
/** Update the min/max gain, and the mean of all gains computed so far.
 *
 * This mean is used in the definition of the exponential distribution.
 */
static
SCIP_RETCODE updateMinMaxMeanGain(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULE*      branchrule,         /**< branching rule */
   SCIP_Real             downgain,           /**< gain for branching downwards */
   SCIP_Real             upgain              /**< gain for branching upwards */
   )
{
   SCIP_BRANCHRULEDATA* branchruledata = SCIPbranchruleGetData(branchrule);
   SCIP_Real logmeangain;
   /* initializing to avoid linter, value never used */
   SCIP_Real oldlogstdevgain = -1.0;
   SCIP_Real oldlogmeangain = -1.0;
 
   assert(branchruledata != NULL);
 
   /* in the case of very large gains, SCIP will already prioritize this variable */
   if (SCIPisRelGE(scip, downgain, MAXGAINTHRESHOLD) || SCIPisRelGE(scip, upgain, MAXGAINTHRESHOLD))
      return SCIP_OKAY;
 
   SCIP_Real meangain = sqrt((downgain + GEOMMEANSHIFT) * (upgain + GEOMMEANSHIFT)) - GEOMMEANSHIFT;
   assert(SCIPisGE(scip, meangain, 0.0));
 
   if(meangain < MINGAINTHRESHOLD)
   {
      branchruledata->nzerogains++;
      return SCIP_OKAY;
   }
 
   branchruledata->currmeandualgain = ((SCIP_Real) branchruledata->currndualgains /  (branchruledata->currndualgains + 1) ) * branchruledata->currmeandualgain + meangain / ( branchruledata->currndualgains + 1 ) ;
 
   ++branchruledata->currndualgains;
 
   if(branchruledata->currndualgains > 1)
   {
      oldlogstdevgain = branchruledata->logstdevgain;
      oldlogmeangain = branchruledata->sumlogmeangains / (branchruledata->currndualgains - 1);
   }
 
   branchruledata->sumlogmeangains += log(meangain);
   /* update the max mean gain */
   logmeangain = branchruledata->sumlogmeangains / branchruledata->currndualgains;
 
   int ngains = branchruledata->currndualgains;
 
   if (ngains == 1)
      branchruledata->logstdevgain = pow((log(meangain) - logmeangain), 2.0 );
   else
      branchruledata->logstdevgain = ( (ngains - 2)  * oldlogstdevgain  + (ngains - 1) * pow( oldlogmeangain - logmeangain, 2.0) +  pow((log(meangain) - logmeangain), 2.0 ) ) / (ngains - 1) ;
 
   branchruledata->maxmeangain = MAX(branchruledata->maxmeangain, meangain);
   branchruledata->minmeangain = MIN(branchruledata->minmeangain, meangain);
   SCIPdebugMsg(scip, " -> downgain: %g upgain: %g minmeangain: %g maxmeangain: %g mean-of-two: %g mean-of-%d-dual-gains: %g\n",downgain, upgain, branchruledata->minmeangain, branchruledata->maxmeangain, meangain, branchruledata->currndualgains, branchruledata->currmeandualgain);
 
   return SCIP_OKAY;
}
 
/** compute the depth of the tree with the assumption that left and right dual gains are equal */
static
SCIP_Real strongBranchingDepth(
   SCIP_Real             gap,                /**< gap to be closed */
   SCIP_Real             maxmeangain         /**< maximum mean gain of the branching candidates */
   )
{
   assert(maxmeangain >= 0.0);
   /* using an epsilon value if maxmeangain is zero */
   SCIP_Real depth = ceil(gap / MAX(maxmeangain, MINGAINTHRESHOLD));
   assert(depth > 0);
   return depth;
}
 
/** compute the size of the tree with the assumption that left and right dual gains are equal */
static
SCIP_Real strongBranchingTreeSize(
   SCIP_Real             estimatedepth       /**< estimated depth of the tree */
   )
{
   return pow(2.0, estimatedepth + 1.0) - 1.0;
}
 
/** calculate the cumulative distribution function (CDF) value for a mixture of a Dirac at zero and a continuous distribution (depending on distributioncdf) */
static
SCIP_Real cdfProbability(
   SCIP_Real             rate,               /**< rate of the distribution */
   SCIP_Real             zeroprob,           /**< probability of zero gain */
   SCIP_Real             proposedgain,       /**< proposed gain */
   SCIP_Real             mingain,            /**< minimum gain */
   SCIP_Real             logmeangain,        /**< logarithm og mean gain */
   SCIP_Real             logstdevgain,       /**< logarithm of standard deviation of gain */
   int                   distributioncdf     /**< distribution type (PARETODISTRIBUTION, EXPONENTIALDISTRIBUTION, LOGNORMALDISTRIBUTION) */
   )
{
   if(distributioncdf == PARETODISTRIBUTION)
   {
      if (proposedgain < mingain)
         return 0.0;
      else
         return zeroprob + (1.0 - zeroprob) * (1.0 - pow(mingain / proposedgain, rate));
   }
   else if(distributioncdf == EXPONENTIALDISTRIBUTION)
   {
      assert(rate >= 0.0);
      return zeroprob + (1.0 - zeroprob) * (1.0 - exp(-rate * proposedgain));
   }
   else
   {
      assert(distributioncdf == LOGNORMALDISTRIBUTION);
      return zeroprob + (1.0 - zeroprob)  * 0.5 * erfc(-(log(proposedgain) - logmeangain) / (logstdevgain * sqrt(2.0)));
   }
}
 
/** calculate the expected size of a tree with one more iteration of strong branching */
static
SCIP_Real expectedTreeSize(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_Real             gap,                /**< gap to be closed */
   SCIP_Real             zeroprob,           /**< probability of zero gain */
   SCIP_Real             currentdepth,       /**< current depth of the tree */
   SCIP_Real             lambda,             /**< rate of the distribution */
   SCIP_Real             mingain,            /**< minimum gain */
   SCIP_Real             logmeangain,        /**< logarithm of mean gain */
   SCIP_Real             logstdevgain,       /**< logarithm of standard deviation of gain */
   int                   distributioncdf     /**< distribution type (PARETODISTRIBUTION, EXPONENTIALDISTRIBUTION, LOGNORMALDISTRIBUTION) */
   )
{
   SCIP_Real ptotal = 0.0;
   SCIP_Real totalimprovedtree = 0.0;
 
   int depth = (int) currentdepth;
   SCIP_Real currenttreesize = strongBranchingTreeSize(currentdepth);
   SCIP_Real nexttreesize;
   SCIP_Real improvedtree;
   if (depth == 1)
   {
      nexttreesize = currenttreesize;
   }
   /* compute the expected size with one more strong branching iteration */
   else if (depth == 2)
   {
      /* Probability of finding a better variable that would reduce the depth (smaller tree size). */
      SCIP_Real p = 1.0 - cdfProbability(lambda, zeroprob, gap, mingain, logmeangain, logstdevgain, distributioncdf);
      SCIPdebugMsg(scip, " -> Probability of finding a better variable that would reduce the depth: %g\n", p);
      /* Size of the improved tree. */
      improvedtree =  strongBranchingTreeSize(currentdepth - 1);
      nexttreesize = improvedtree * p + strongBranchingTreeSize(currentdepth) * (1.0 - p) + 2.0;
      if( p < MINGAINTHRESHOLD )
         return SCIPinfinity(scip);
   }
   else
   {
      /* Probability of not finding a better variable that would reduce the depth of the tree (size of the tree). */
      SCIP_Real pnotbetter = cdfProbability(lambda, zeroprob, (gap / (depth - 1)), mingain, logmeangain, logstdevgain, distributioncdf);
      /* Probability of finding a better variable that would reduce the depth (smaller tree size). */
      SCIP_Real pbetter = 1.0 - cdfProbability(lambda, zeroprob, gap / (depth - 1), mingain, logmeangain, logstdevgain, distributioncdf);
      SCIPdebugMsg(scip, " -> Probability of finding a better variable that would reduce the depth: %g\n", pbetter);
 
      if( pbetter < MINGAINTHRESHOLD )
         return SCIPinfinity(scip);
 
      SCIP_Real p;
      p = 0.0;
      while (pbetter >= MINGAINTHRESHOLD && ptotal + pnotbetter < 1.0)
      {
         if (depth > 2)
         {
            p = cdfProbability(lambda, zeroprob, gap / (depth - 2), mingain, logmeangain, logstdevgain, distributioncdf) - cdfProbability(lambda, zeroprob, gap / (depth - 1), mingain, logmeangain, logstdevgain, distributioncdf);
            ptotal += p;
            pbetter = 1.0 - cdfProbability(lambda, zeroprob, gap / (depth - 2), mingain, logmeangain, logstdevgain, distributioncdf);
         }
 
         else if (depth == 2)
         {
               p = 1.0 - cdfProbability(lambda, zeroprob, gap, mingain, logmeangain, logstdevgain, distributioncdf);
               ptotal += p;
               pbetter = 0.0;
         }
         if (ptotal + pnotbetter <= 1.0)
         {
            /* Compute expected size of the improved tree based on improved depth, considering the probability of this improvement. */
            improvedtree = strongBranchingTreeSize(currentdepth - 1) * p;
         }
         else
            improvedtree = 0.0;
         depth--;
         totalimprovedtree += improvedtree;
      }
      /* Compute the expectation of the next tree size with one more iteration of strong branching */
      nexttreesize = totalimprovedtree + pnotbetter * strongBranchingTreeSize(currentdepth) + 2.0;
   }
   return nexttreesize;
}
 
/** decide if we continue strong branching based based on lookahead */
static
SCIP_Bool continueStrongBranchingLookahead(
   SCIP*                 scip,               /**< SCIP data structure */
   int                   candidx,            /**< index of the branching candidate */
   int                   ninitcands,         /**< number of initial candidates */
   SCIP_Real             lookahead,          /**< lookahead value */
   SCIP_Real             maxlookahead,       /**< maximum lookahead value */
   int                   nbdchgs,            /**< number of bound changes found */
   int                   nbdconflicts,       /**< number of bound conflicts found */
   int                   maxbdchgs,          /**< maximal number of bound tightenings before the node is reevaluated */
   SCIP_Longint          maxnsblpiterations  /**< maximal number of strong branching LP iterations */
   )
{
   return candidx < ninitcands && lookahead < maxlookahead && nbdchgs + nbdconflicts < maxbdchgs
               && (candidx < (int) maxlookahead || SCIPgetNStrongbranchLPIterations(scip) < maxnsblpiterations);
}
 
/** Decide if we continue strong branching based on the estimation of the tree size given the current gains. */
static
SCIP_Bool continueStrongBranchingTreeSizeEstimation(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULEDATA*  branchruledata,     /**< branching rule data */
   SCIP_Real             lookahead,          /**< lookahead value */
   SCIP_Real             maxlookahead        /**< maximum lookahead value */
   )
{
   SCIP_Real lambda;
   SCIP_Real maxmeangain;
   SCIP_Real minmeangain;
   SCIP_Real nexttreesize;
   SCIP_Real currentdepth;
   SCIP_Real currenttreesize;
   SCIP_Real absdualgap;
   SCIP_Real gaptoclose = -1.0;
 
   /* default values never used */
   SCIP_Real logmeangain = 0.0;
   SCIP_Real logstdevgain = -1.0;
 
   if (!branchruledata->dynamiclookahead)
      return TRUE;
 
   /* compute the expected tree size after reaching a min lookahaead */
   if( lookahead < branchruledata->dynamiclookaheadquot * maxlookahead)
      return TRUE;
 
   /* if we do not have a large enough sample to estimate the tree size we continue with strong branching */
   if ( branchruledata->currndualgains < branchruledata->minsamplesize )
      return TRUE;
 
   maxmeangain = branchruledata->maxmeangain;
   minmeangain = branchruledata->minmeangain;
 
   /* Compute the absolute gap at the current node. If no upper bound available we continue strong branching as usual */
   if( !SCIPisInfinity(scip, SCIPgetUpperbound(scip)) )
      absdualgap = SCIPgetUpperbound(scip) - SCIPgetNodeLowerbound(scip, SCIPgetCurrentNode(scip));
   else
      absdualgap = SCIPinfinity(scip);
 
   if( !SCIPisInfinity(scip, absdualgap) && !SCIPisInfinity(scip, maxmeangain) && SCIPisGT(scip, maxmeangain, 0.0) )
      gaptoclose = absdualgap;
   else
      return TRUE;
 
   assert(gaptoclose >= 0.0);
   assert(!SCIPisInfinity(scip, -maxmeangain));
 
   SCIP_Real zeroprob = (SCIP_Real) branchruledata->nzerogains / (branchruledata->nzerogains + branchruledata->currndualgains);
   if(branchruledata->dynamiclookdistribution == PARETODISTRIBUTION)
   {
      assert(minmeangain > 0.0);
      assert(branchruledata->currndualgains > 0);
      SCIP_Real sumlambda = branchruledata->sumlogmeangains - branchruledata->currndualgains * log(minmeangain);
      if(SCIPisZero(scip, sumlambda))
         return TRUE;
      lambda = branchruledata->currndualgains / sumlambda;
   }
   else if (branchruledata->dynamiclookdistribution == EXPONENTIALDISTRIBUTION)
   {
      lambda = 1 / (branchruledata->currmeandualgain);
   }
   else
   {
      assert(branchruledata->dynamiclookdistribution == LOGNORMALDISTRIBUTION);
      logmeangain = branchruledata->sumlogmeangains / branchruledata->currndualgains;
      logstdevgain = branchruledata->logstdevgain;
      /* lambda value not used */
      lambda = -1.0;
   }
 
   /* Continue strong branching since we do not have good enough gains in this case */
   currentdepth = strongBranchingDepth(gaptoclose, maxmeangain); /*lint !e644*/
 
   if (currentdepth >= 50)
      return TRUE;
 
   /* Compute the tree size if we branch on the best variable so far, including the strong branching already done. */
   currenttreesize = strongBranchingTreeSize(currentdepth);
 
   /* Compute the expected size of the tree with one more strong branching */
   nexttreesize =  expectedTreeSize(scip, gaptoclose, zeroprob, currentdepth, lambda, minmeangain, logmeangain, logstdevgain, branchruledata->dynamiclookdistribution);
 
   if(SCIPisLE(scip, nexttreesize, currenttreesize - 1.0))
      return TRUE;
 
   SCIPdebugMsg(scip," -> Stopped at lookahead %g, current tree size %g, next tree size %g\n", lookahead, currenttreesize, nexttreesize);
   return FALSE;
}
 
/** determine if strong branching is needed on the given candidate variable */
static
SCIP_Bool needsStrongBranching(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULE*      branchrule,         /**< branching rule */
   SCIP_VAR*             branchcand,         /**< branching candidate */
   SCIP_Real             branchcandfrac,     /**< fractional part of the branching candidate */
   SCIP_VAR*             bestpscand,         /**< best candidate as per pscost score, must be present if usehyptestforreliability is used */
   SCIP_Real             bestpscandfrac,     /**< fractional part of the best candidate as per pscost score, must be present if usehyptestforreliability is used */
   SCIP_Real             reliable,           /**< size threshold for reliability */
   SCIP_Real             relerrorthreshold,  /**< relative error threshold for reliability */
   SCIP_CONFIDENCELEVEL  clevel,             /**< confidence level */
   SCIP_Bool             useancpscost        /**< check reliability for ancpscost as well */
   )
{  /*lint --e{715}*/
   SCIP_BRANCHRULEDATA* branchruledata;
   SCIP_Real pscostdownsize;
   SCIP_Real pscostupsize;
   SCIP_Real pscostsize;
   SCIP_Real dpscostdownsize;
   SCIP_Real dpscostupsize;
   SCIP_Real dpscostsize;
 
   /* get branching rule data */
   branchruledata = SCIPbranchruleGetData(branchrule);
   assert(branchruledata != NULL);
 
   /* check, if the pseudo cost score and the discounted pseudocost score of the variable is reliable */
   pscostdownsize = SCIPgetVarPseudocostCountCurrentRun(scip, branchcand, SCIP_BRANCHDIR_DOWNWARDS);
   pscostupsize = SCIPgetVarPseudocostCountCurrentRun(scip, branchcand, SCIP_BRANCHDIR_UPWARDS);
   pscostsize = MIN(pscostdownsize, pscostupsize);
   dpscostdownsize = SCIPgetVarAncPseudocostCountCurrentRun(scip, branchcand, SCIP_BRANCHDIR_DOWNWARDS);
   dpscostupsize = SCIPgetVarAncPseudocostCountCurrentRun(scip, branchcand, SCIP_BRANCHDIR_UPWARDS);
   dpscostsize = MIN(dpscostdownsize, dpscostupsize);
 
   /* determine if variable is considered reliable based on the current reliability setting */
   /* check fixed number threshold (aka original) reliability first */
   assert(!branchruledata->usehyptestforreliability || bestpscand != NULL );
   if( pscostsize < reliable || ( useancpscost && dpscostsize < reliable ) )
      return TRUE;
   if( branchruledata->userelerrorforreliability && branchruledata->usehyptestforreliability )
   {
      if( !SCIPisVarPscostRelerrorReliable(scip, branchcand, relerrorthreshold, clevel) &&
            !SCIPsignificantVarPscostDifference(scip, bestpscand, bestpscandfrac,
               branchcand, branchcandfrac, SCIP_BRANCHDIR_DOWNWARDS, clevel, TRUE) &&
            !SCIPsignificantVarPscostDifference(scip, bestpscand, 1 - bestpscandfrac,
               branchcand, 1 - branchcandfrac, SCIP_BRANCHDIR_UPWARDS, clevel, TRUE) )
         return TRUE;
   }
   /* check if relative error is tolerable */
   if( branchruledata->userelerrorforreliability &&
         !SCIPisVarPscostRelerrorReliable(scip, branchcand, relerrorthreshold, clevel))
      return TRUE;
   /* check if best pseudo-candidate is significantly better in both directions, use strong-branching otherwise */
   if( branchruledata->usehyptestforreliability &&
         !SCIPsignificantVarPscostDifference(scip, bestpscand, bestpscandfrac,
               branchcand, branchcandfrac, SCIP_BRANCHDIR_DOWNWARDS, clevel, TRUE) &&
         !SCIPsignificantVarPscostDifference(scip, bestpscand, 1 - bestpscandfrac,
               branchcand, 1 - branchcandfrac, SCIP_BRANCHDIR_UPWARDS, clevel, TRUE) )
      return TRUE;
   return FALSE;
}
 
/** execute reliability pseudo cost branching */
static
SCIP_RETCODE execRelpscost(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_BRANCHRULE*      branchrule,         /**< branching rule */
   SCIP_VAR**            branchcands,        /**< branching candidates */
   SCIP_Real*            branchcandssol,     /**< solution value for the branching candidates */
   SCIP_Real*            branchcandsfrac,    /**< fractional part of the branching candidates */
   int*                  branchorbitidx,     /**< indices of orbit (or NULL) */
   int                   nbranchcands,       /**< number of branching candidates */
   SCIP_Bool             executebranch,      /**< execute a branching step or run probing only */
   SCIP_RESULT*          result              /**< pointer to the result of the execution */
   )
{  /*lint --e{715}*/
   SCIP_BRANCHRULEDATA* branchruledata;
   SCIP_Real lpobjval;
   SCIP_Real bestsbdown;
   SCIP_Real bestsbup;
   SCIP_Real provedbound;
   SCIP_Bool bestsbdownvalid;
   SCIP_Bool bestsbupvalid;
   SCIP_Bool bestsbdowncutoff;
   SCIP_Bool bestsbupcutoff;
   SCIP_Bool bestisstrongbranch;
   SCIP_Bool allcolsinlp;
   SCIP_Bool exactsolve;
   int ninitcands;
   int bestcand;
 
   /* remember which variables strong branching is performed on, and the
    * recorded lp bound changes that are observed */
   SCIP_Real* sbdown = NULL;
   SCIP_Real* sbup = NULL;
   SCIP_Bool* sbdownvalid = NULL;
   SCIP_Bool* sbupvalid = NULL;
 
   *result = SCIP_DIDNOTRUN;
 
   assert(SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL);
 
   /* get branching rule data */
   branchruledata = SCIPbranchruleGetData(branchrule);
   assert(branchruledata != NULL);
 
   /* get current LP objective bound of the local sub problem and global cutoff bound */
   lpobjval = SCIPgetLPObjval(scip);
 
   /* check, if we want to solve the problem exactly, meaning that strong branching information is not useful
    * for cutting off sub problems and improving lower bounds of children
    */
   exactsolve = SCIPisExact(scip);
 
   /* check, if all existing columns are in LP, and thus the strong branching results give lower bounds */
   allcolsinlp = SCIPallColsInLP(scip);
 
   bestcand = -1;
   bestisstrongbranch = FALSE;
   bestsbdown = SCIP_INVALID;
   bestsbup = SCIP_INVALID;
   bestsbdownvalid = FALSE;
   bestsbupvalid = FALSE;
   bestsbdowncutoff = FALSE;
   bestsbupcutoff = FALSE;
   provedbound = lpobjval;
 
   /* Allocate memory to store the lp bounds of the up and down children
    * for those of the variables that we performed sb on
    */
   SCIP_CALL( SCIPallocBufferArray(scip, &sbdown, nbranchcands) );
   SCIP_CALL( SCIPallocBufferArray(scip, &sbup, nbranchcands) );
   SCIP_CALL( SCIPallocBufferArray(scip, &sbdownvalid, nbranchcands) );
   SCIP_CALL( SCIPallocBufferArray(scip, &sbupvalid, nbranchcands) );
 
   if( nbranchcands == 1 )
   {
      /* only one candidate: nothing has to be done */
      bestcand = 0;
      SCIPdebug(ninitcands = 0);
      sbdownvalid[0] = FALSE;
      sbupvalid[0] = FALSE;
   }
   else
   {
      SCIP_VAR** vars;
      int* initcands;
      SCIP_Real* initcandscores;
      SCIP_Real* newlbs = NULL;
      SCIP_Real* newubs = NULL;
      SCIP_Real* mingains = NULL;
      SCIP_Real* maxgains = NULL;
      /* scores computed from pseudocost branching */
      SCIP_Real* scores = NULL;
      SCIP_Real* scoresfrompc = NULL;
      SCIP_Real* scoresfromothers = NULL;
      int* bdchginds;
      SCIP_BOUNDTYPE* bdchgtypes;
      SCIP_Real* bdchgbounds;
      int maxninitcands;
      int nuninitcands;
      int nbdchgs;
      int nbdconflicts;
      SCIP_Real avgconflictscore;
      SCIP_Real avgconflengthscore;
      SCIP_Real avginferencescore;
      SCIP_Real avgcutoffscore;
      SCIP_Real avgpscostscore;
      SCIP_Real avgdpscostscore;
      SCIP_Real bestpsscore;
      SCIP_Real bestpsfracscore;
      SCIP_Real bestpsdomainscore;
      SCIP_Real bestsbscore;
      SCIP_Real bestuninitsbscore;
      SCIP_Real bestsbfracscore;
      SCIP_Real bestsbdomainscore;
      SCIP_Real prio;
      SCIP_Real reliable;
      SCIP_Real maxlookahead;
      SCIP_Real lookahead;
      SCIP_Real relerrorthreshold;
      SCIP_Bool initstrongbranching;
      SCIP_Bool propagate;
      SCIP_Bool probingbounds;
      SCIP_Longint nodenum;
      SCIP_Longint nlpiterationsquot;
      SCIP_Longint nsblpiterations;
      SCIP_Longint maxnsblpiterations;
      int bestsolidx;
      int maxbdchgs;
      int bestpscand;
      int bestsbcand;
      int bestuninitsbcand;
      int inititer;
      int nvars;
      int i;
      int c;
      SCIP_CONFIDENCELEVEL clevel;
      SCIP_Real degeneracyfactor = 1.0;
      SCIP_Bool useancpscost;
 
      SCIP_CALL( SCIPgetBoolParam(scip, "branching/collectancpscost", &useancpscost) );
 
      /* get LP degeneracy information and compute a factor to change weighting of pseudo cost score vs. other scores */
      if( branchruledata->degeneracyaware > 0 && (SCIPgetDepth(scip) > 0 || branchruledata->degeneracyaware > 1) )
      {
         SCIP_Real degeneracy;
         SCIP_Real varconsratio;
 
         /* get LP degeneracy information */
         SCIP_CALL( SCIPgetLPDualDegeneracy(scip, &degeneracy, &varconsratio) );
 
         assert(degeneracy >= 0.0);
         assert(degeneracy <= 1.0);
         assert(varconsratio >= 1.0);
 
         /* increase factor for a degeneracy >= 80% */
         if( degeneracy >= 0.8 )
         {
            degeneracy = 10.0 * (degeneracy - 0.7);
            degeneracyfactor = degeneracyfactor * pow(10.0,degeneracy);
         }
         /* increase factor for a variable-constraint ratio >= 2.0 */
         if( varconsratio >= 2.0 )
         {
            degeneracyfactor *= 10.0 * varconsratio;
         }
      }
 
      vars = SCIPgetVars(scip);
      nvars = SCIPgetNVars(scip);
 
      bestsolidx = SCIPgetBestSol(scip) == NULL ? -1 : SCIPsolGetIndex(SCIPgetBestSol(scip));
 
      /* get average conflict, inference, and pseudocost scores */
      avgconflictscore = SCIPgetAvgConflictScore(scip);
      avgconflictscore = MAX(avgconflictscore, 0.1);
      avgconflengthscore = SCIPgetAvgConflictlengthScore(scip);
      avgconflengthscore = MAX(avgconflengthscore, 0.1);
      avginferencescore = SCIPgetAvgInferenceScore(scip);
      avginferencescore = MAX(avginferencescore, 0.1);
      avgcutoffscore = SCIPgetAvgCutoffScore(scip);
      avgcutoffscore = MAX(avgcutoffscore, 0.1);
      avgpscostscore = SCIPgetAvgPseudocostScore(scip);
      avgpscostscore = MAX(avgpscostscore, 0.1);
      avgdpscostscore = SCIPgetAvgDPseudocostScore(scip, branchruledata->discountfactor);
      avgdpscostscore = MAX(avgdpscostscore, 0.1);
 
      /* get nonlinear counts according to parameters */
      SCIP_CALL( branchruledataEnsureNlcount(scip, branchruledata) );
 
      initstrongbranching = FALSE;
 
      /* check whether propagation should be performed */
      propagate = (branchruledata->maxproprounds != 0) && !SCIPisExact(scip);
 
      /* check whether valid bounds should be identified in probing-like fashion */
      probingbounds = propagate && branchruledata->probingbounds;
 
      /* get maximal number of candidates to initialize with strong branching; if the current solutions is not basic,
       * we cannot warmstart the simplex algorithm and therefore don't initialize any candidates
       */
      maxninitcands = MIN(nbranchcands, branchruledata->initcand);
      if( !SCIPisLPSolBasic(scip) )
         maxninitcands = 0;
 
      /* calculate maximal number of strong branching LP iterations; if we used too many, don't apply strong branching
       * any more; also, if degeneracy is too high, don't run strong branching at this node
       */
      nlpiterationsquot = (SCIP_Longint)(branchruledata->sbiterquot * SCIPgetNNodeLPIterations(scip));
      maxnsblpiterations = nlpiterationsquot + branchruledata->sbiterofs + SCIPgetNRootStrongbranchLPIterations(scip);
      nsblpiterations = SCIPgetNStrongbranchLPIterations(scip);
      if( nsblpiterations > maxnsblpiterations || degeneracyfactor >= 10.0 )
         maxninitcands = 0;
 
      /* get buffer for storing the unreliable candidates */
      SCIP_CALL( SCIPallocBufferArray(scip, &initcands, maxninitcands+1) ); /* allocate one additional slot for convenience */
      SCIP_CALL( SCIPallocBufferArray(scip, &initcandscores, maxninitcands+1) );
      ninitcands = 0;
 
      /* Allocate memory for the down and up gains, and the computed pseudocost scores */
      SCIP_CALL( SCIPallocBufferArray(scip, &mingains, nbranchcands) );
      SCIP_CALL( SCIPallocBufferArray(scip, &maxgains, nbranchcands) );
      SCIP_CALL( SCIPallocBufferArray(scip, &scores, nbranchcands) );
      SCIP_CALL( SCIPallocBufferArray(scip, &scoresfrompc, nbranchcands) );
      SCIP_CALL( SCIPallocBufferArray(scip, &scoresfromothers, nbranchcands) );
 
      /* get current node number */
      nodenum = SCIPgetNNodes(scip);
 
      /* initialize bound change arrays */
      bdchginds = NULL;
      bdchgtypes = NULL;
      bdchgbounds = NULL;
      nbdchgs = 0;
      nbdconflicts = 0;
      maxbdchgs = branchruledata->maxbdchgs;
      if( maxbdchgs == -1 )
         maxbdchgs = INT_MAX;
 
      /* calculate value used as reliability */
      prio = (maxnsblpiterations - nsblpiterations)/(nsblpiterations + 1.0);
      prio = MIN(prio, 1.0);
      prio = MAX(prio, (nlpiterationsquot - nsblpiterations)/(nsblpiterations + 1.0));
      reliable = (1.0-prio) * branchruledata->minreliable + prio * branchruledata->maxreliable;
 
      /* calculate the threshold for the relative error in the same way; low tolerance is more strict than higher tolerance */
      relerrorthreshold = (1.0 - prio) * branchruledata->higherrortol + prio * branchruledata->lowerrortol;
 
      clevel = (SCIP_CONFIDENCELEVEL)branchruledata->confidencelevel;
      /* determine the confidence level for hypothesis testing based on value of prio */
      if( branchruledata->usedynamicconfidence )
      {
         /* with decreasing priority, use a less strict confidence level */
         if( prio >= 0.9 )
            clevel = SCIP_CONFIDENCELEVEL_MAX;
         else if( prio >= 0.7 )
            clevel = SCIP_CONFIDENCELEVEL_HIGH;
         else if( prio >= 0.5 )
            clevel = SCIP_CONFIDENCELEVEL_MEDIUM;
         else if( prio >= 0.3 )
            clevel = SCIP_CONFIDENCELEVEL_LOW;
         else
            clevel = SCIP_CONFIDENCELEVEL_MIN;
      }
 
      /* search for the best pseudo cost candidate, while remembering unreliable candidates in a sorted buffer */
      nuninitcands = 0;
      bestpscand = -1;
      bestpsscore = -SCIPinfinity(scip);
      bestpsfracscore = -SCIPinfinity(scip);
      bestpsdomainscore = -SCIPinfinity(scip);
 
      /* search for the best candidate first */
      if( branchruledata->usehyptestforreliability )
      {
         for( c = 0; c < nbranchcands; ++c )
         {
            SCIP_Real conflictscore;
            SCIP_Real conflengthscore;
            SCIP_Real inferencescore;
            SCIP_Real cutoffscore;
            SCIP_Real gmieffscore;
            SCIP_Real lastgmieffscore;
            SCIP_Real pscostscore;
            SCIP_Real nlscore;
            SCIP_Real score;
 
            conflictscore = SCIPgetVarConflictScore(scip, branchcands[c]);
            conflengthscore = SCIPgetVarConflictlengthScore(scip, branchcands[c]);
            inferencescore = SCIPgetVarAvgInferenceScore(scip, branchcands[c]);
            cutoffscore = SCIPgetVarAvgCutoffScore(scip, branchcands[c]);
            gmieffscore = SCIPgetVarAvgGMIScore(scip, branchcands[c]);
            lastgmieffscore = SCIPgetVarLastGMIScore(scip, branchcands[c]);
            nlscore = calcNlscore(scip, branchruledata->nlcount, branchruledata->nlcountmax, SCIPvarGetProbindex(branchcands[c]));
            pscostscore = SCIPgetVarPseudocostScore(scip, branchcands[c], branchcandssol[c]);
 
            /* replace the pseudo cost score with the already calculated one;
             * @todo: use old data for strong branching with propagation?
             */
            if( SCIPgetVarStrongbranchNode(scip, branchcands[c]) == nodenum )
            {
               SCIP_Real down;
               SCIP_Real up;
               SCIP_Real lastlpobjval;
               SCIP_Real downgain;
               SCIP_Real upgain;
 
               /* use the score of the strong branching call at the current node */
               SCIP_CALL( SCIPgetVarStrongbranchLast(scip, branchcands[c], &down, &up, NULL, NULL, NULL, &lastlpobjval) );
               downgain = MAX(down - lastlpobjval, 0.0);
               upgain = MAX(up - lastlpobjval, 0.0);
               if( useancpscost )
               {
                  /* add discounted gains as stored in dpscost */
                  SCIP_Real downsol;
                  SCIP_Real upsol;
                  downsol = SCIPfeasCeil(scip, branchcandssol[c]-1.0);
                  upsol = SCIPfeasFloor(scip, branchcandssol[c]+1.0);
                  downgain += branchruledata->discountfactor * SCIPgetVarAncPseudocostVal(scip, branchcands[c], downsol-branchcandssol[c]);
                  upgain += branchruledata->discountfactor * SCIPgetVarAncPseudocostVal(scip, branchcands[c], upsol-branchcandssol[c]);
                  downgain /= (1 + branchruledata->discountfactor);
                  upgain /= (1 + branchruledata->discountfactor);
               }
               pscostscore = SCIPgetBranchScore(scip, branchcands[c], downgain, upgain);
 
               SCIPdebugMsg(scip, " -> strong branching on variable <%s> already performed (down=%g (%+g), up=%g (%+g), pscostscore=%g)\n",
                  SCIPvarGetName(branchcands[c]), down, downgain, up, upgain, pscostscore);
            }
 
            score = calcScore(scip, branchruledata, conflictscore, avgconflictscore, conflengthscore, avgconflengthscore,
               inferencescore, avginferencescore, cutoffscore, avgcutoffscore, gmieffscore, lastgmieffscore,
               pscostscore, avgpscostscore, nlscore, branchcandsfrac[c], degeneracyfactor);
 
            /* check for better score of candidate */
            if( SCIPisSumGE(scip, score, bestpsscore) )
            {
               SCIP_Real fracscore;
               SCIP_Real domainscore;
 
               fracscore = MIN(branchcandsfrac[c], 1.0 - branchcandsfrac[c]);
               domainscore = -(SCIPvarGetUbLocal(branchcands[c]) - SCIPvarGetLbLocal(branchcands[c]));
               if( SCIPisSumGT(scip, score, bestpsscore)
                     || SCIPisSumGT(scip, fracscore, bestpsfracscore)
                     || (SCIPisSumGE(scip, fracscore, bestpsfracscore) && domainscore > bestpsdomainscore) )
               {
                  bestpscand = c;
                  bestpsscore = score;
                  bestpsfracscore = fracscore;
                  bestpsdomainscore = domainscore;
               }
            }
         }
      }
 
      /* use discounted pseudocosts only if all candidates are reliable. */
      if( useancpscost && maxninitcands > 0 )
      {
         /* look for at least one unreliable candidate */
         for( c = 0; c < nbranchcands; ++c )
         {
            if( needsStrongBranching(scip, branchrule, branchcands[c], branchcandsfrac[c],
                  bestpscand >= 0 ? branchcands[bestpscand] : NULL,
                  bestpscand >= 0 ? branchcandsfrac[bestpscand] : 0.0,
                  reliable, relerrorthreshold, clevel, useancpscost) )
            {
               useancpscost = FALSE;
               break;
            }
         }
      }
 
      if( useancpscost )
         avgpscostscore = avgdpscostscore;
 
      for( c = 0; c < nbranchcands; ++c )
      {
         SCIP_Real conflictscore;
         SCIP_Real conflengthscore;
         SCIP_Real inferencescore;
         SCIP_Real cutoffscore;
         SCIP_Real gmieffscore;
         SCIP_Real lastgmieffscore;
         SCIP_Real pscostscore;
         SCIP_Real nlscore;
         SCIP_Real score;
         SCIP_Bool usesb;
         SCIP_Real downgain;
         SCIP_Real upgain;
         SCIP_Real fracpart;
 
         assert(branchcands[c] != NULL);
         assert(!SCIPisFeasIntegral(scip, branchcandssol[c]) || SCIPisExact(scip));
         assert(!SCIPisFeasIntegral(scip, SCIPvarGetLPSol(branchcands[c])) || SCIPisExact(scip));
 
         /* Record the variables current pseudocosts. These may be overwritten if
          * strong branching is performed.
          */
         sbdownvalid[c] = FALSE;
         sbupvalid[c] = FALSE;
         fracpart = SCIPfeasFrac(scip, SCIPvarGetLPSol(branchcands[c]));
         downgain = SCIPgetVarPseudocostVal(scip, branchcands[c], 0.0 - fracpart);
         upgain = SCIPgetVarPseudocostVal(scip, branchcands[c], 1.0 - fracpart);
         mingains[c] = MIN(downgain, upgain);
         maxgains[c] = MAX(downgain, upgain);
 
         /* get conflict, inference, cutoff, nonlinear, and pseudo cost scores for candidate */
         conflictscore = SCIPgetVarConflictScore(scip, branchcands[c]);
         conflengthscore = SCIPgetVarConflictlengthScore(scip, branchcands[c]);
         inferencescore = SCIPgetVarAvgInferenceScore(scip, branchcands[c]);
         cutoffscore = SCIPgetVarAvgCutoffScore(scip, branchcands[c]);
         gmieffscore = SCIPgetVarAvgGMIScore(scip, branchcands[c]);
         lastgmieffscore = SCIPgetVarLastGMIScore(scip, branchcands[c]);
         nlscore = calcNlscore(scip, branchruledata->nlcount, branchruledata->nlcountmax, SCIPvarGetProbindex(branchcands[c]));
         pscostscore = SCIPgetVarPseudocostScore(scip, branchcands[c], branchcandssol[c]);
         usesb = FALSE;
         if( useancpscost )
            pscostscore = SCIPgetVarDPseudocostScore(scip, branchcands[c], branchcandssol[c],branchruledata->discountfactor);
 
         /* don't use strong branching on variables that have already been initialized at the current node;
          * instead replace the pseudo cost score with the already calculated one;
          * @todo: use old data for strong branching with propagation?
          */
         if( SCIPgetVarStrongbranchNode(scip, branchcands[c]) == nodenum )
         {
            SCIP_Real down;
            SCIP_Real up;
            SCIP_Real lastlpobjval;
 
            /* use the score of the strong branching call at the current node */
            SCIP_CALL( SCIPgetVarStrongbranchLast(scip, branchcands[c], &down, &up, NULL, NULL, NULL, &lastlpobjval) );
            downgain = MAX(down - lastlpobjval, 0.0);
            upgain = MAX(up - lastlpobjval, 0.0);
            /* add discounted gains as stored in anspscost */
            if( useancpscost ) {
               /* the anspscost must be reliable here */
               SCIP_Real downsol;
               SCIP_Real upsol;
               downsol = SCIPfeasCeil(scip, branchcandssol[c]-1.0);
               upsol = SCIPfeasFloor(scip, branchcandssol[c]+1.0);
               downgain += branchruledata->discountfactor * SCIPgetVarAncPseudocostVal(scip, branchcands[c], downsol-branchcandssol[c]);
               upgain += branchruledata->discountfactor * SCIPgetVarAncPseudocostVal(scip, branchcands[c], upsol-branchcandssol[c]);
               downgain /= (1 + branchruledata->discountfactor);
               upgain /= (1 + branchruledata->discountfactor);
            }
            pscostscore = SCIPgetBranchScore(scip, branchcands[c], downgain, upgain);
 
            mingains[c] = MIN(downgain, upgain);
            maxgains[c] = MAX(downgain, upgain);
 
            if( branchruledata->dynamiclookahead )
               SCIP_CALL( updateMinMaxMeanGain(scip, branchrule, downgain, upgain) );
 
            SCIPdebugMsg(scip, " -> strong branching on variable <%s> already performed (down=%g (%+g), up=%g (%+g), pscostscore=%g)\n",
               SCIPvarGetName(branchcands[c]), down, downgain, up, upgain, pscostscore);
         }
         else if( maxninitcands > 0 )
         {
            SCIP_Real downsize;
            SCIP_Real upsize;
            SCIP_Real size;
 
            /* check, if the pseudo cost score of the variable is reliable */
            downsize = SCIPgetVarPseudocostCountCurrentRun(scip, branchcands[c], SCIP_BRANCHDIR_DOWNWARDS);
            upsize = SCIPgetVarPseudocostCountCurrentRun(scip, branchcands[c], SCIP_BRANCHDIR_UPWARDS);
            size = MIN(downsize, upsize);
 
            usesb = needsStrongBranching(scip, branchrule, branchcands[c], branchcandsfrac[c],
                  bestpscand >= 0 ? branchcands[bestpscand] : NULL,
                  bestpscand >= 0 ? branchcandsfrac[bestpscand] : 0.0,
                  reliable, relerrorthreshold, clevel, FALSE);
 
            /* count the number of variables that are completely uninitialized */
            if( size < 0.1 )
               nuninitcands++;
         }
 
         /* combine the five score values */
         scoresfrompc[c] =  calcScore(scip, branchruledata, 0.0, avgconflictscore, 0.0, avgconflengthscore,
                                      0.0, avginferencescore, 0.0, avgcutoffscore, 0.0, 0.0,
                                      pscostscore, avgpscostscore, 0.0, branchcandsfrac[c], degeneracyfactor);
         scoresfromothers[c] = calcScore(scip, branchruledata, conflictscore, avgconflictscore, conflengthscore, avgconflengthscore,
                                         inferencescore, avginferencescore, cutoffscore, avgcutoffscore, gmieffscore, lastgmieffscore,
                                         0.0, avgpscostscore, nlscore, branchcandsfrac[c], degeneracyfactor);
         score = scoresfrompc[c] + scoresfromothers[c];
         scores[c] = score;
         /*score = calcScore(scip, branchruledata, conflictscore, avgconflictscore, conflengthscore, avgconflengthscore,
            inferencescore, avginferencescore, cutoffscore, avgcutoffscore, gmieffscore, lastgmieffscore,
            pscostscore, avgpscostscore, nlscore, branchcandsfrac[c], degeneracyfactor);*/
         if( usesb )
         {
            int j;
 
            mingains[c] = 0;
            maxgains[c] = 0;
            scoresfrompc[c] = 0;
            scoresfromothers[c] = 0;
 
            /* assign a random score to this uninitialized candidate */
            if( branchruledata->randinitorder )
               score += SCIPrandomGetReal(branchruledata->randnumgen, 0.0, 1e-4);
 
            /* pseudo cost of variable is not reliable: insert candidate in initcands buffer */
            for( j = ninitcands; j > 0 && score > initcandscores[j-1]; --j )
            {
               initcands[j] = initcands[j-1];
               initcandscores[j] = initcandscores[j-1];
            }
            initcands[j] = c;
            initcandscores[j] = score;
            ninitcands++;
            ninitcands = MIN(ninitcands, maxninitcands);
         }
         /* in the case of hypothesis reliability, the best pseudo candidate has been determined already */
         else if( !branchruledata->usehyptestforreliability )
         {
            /* variable will keep its pseudo cost value: check for better score of candidate */
            if( SCIPisSumGE(scip, score, bestpsscore) )
            {
               SCIP_Real fracscore;
               SCIP_Real domainscore;
 
               fracscore = MIN(branchcandsfrac[c], 1.0 - branchcandsfrac[c]);
               domainscore = -(SCIPvarGetUbLocal(branchcands[c]) - SCIPvarGetLbLocal(branchcands[c]));
               if( SCIPisSumGT(scip, score, bestpsscore)
                  || SCIPisSumGT(scip, fracscore, bestpsfracscore)
                  || (SCIPisSumGE(scip, fracscore, bestpsfracscore) && domainscore > bestpsdomainscore) )
               {
                  bestpscand = c;
                  bestpsscore = score;
                  bestpsfracscore = fracscore;
                  bestpsdomainscore = domainscore;
               }
            }
         }
      }
 
      /* in the special case that only the best pseudo candidate was selected for strong branching, skip the strong branching */
      if( branchruledata->usehyptestforreliability && ninitcands == 1 )
      {
         ninitcands = 0;
         SCIPdebugMsg(scip, "Only one single candidate for initialization-->Skipping strong branching\n");
      }
 
      /* initialize unreliable candidates with strong branching until maxlookahead is reached,
       * search best strong branching candidate
       */
      maxlookahead = (SCIP_Real)branchruledata->maxlookahead * (1.0 + (SCIP_Real)nuninitcands/(SCIP_Real)nbranchcands);
      inititer = branchruledata->inititer;
      if( inititer == 0 )
      {
         SCIP_Longint nlpiterations;
         SCIP_Longint nlps;
 
         /* iteration limit is set to twice the average number of iterations spent to resolve a dual feasible SCIP_LP;
          * at the first few nodes, this average is not very exact, so we better increase the iteration limit on
          * these very important nodes
          */
         nlpiterations = SCIPgetNDualResolveLPIterations(scip);
         nlps = SCIPgetNDualResolveLPs(scip);
         if( nlps == 0 )
         {
            nlpiterations = SCIPgetNNodeInitLPIterations(scip);
            nlps = SCIPgetNNodeInitLPs(scip);
            if( nlps == 0 )
            {
               nlpiterations = 1000;
               nlps = 1;
            }
         }
         assert(nlps >= 1);
         inititer = (int)(2*nlpiterations / nlps);
         inititer = (int)((SCIP_Real)inititer * (1.0 + 20.0/nodenum));
         inititer = MAX(inititer, 10);
         inititer = MIN(inititer, 500);
      }
 
      SCIPdebugMsg(scip, "strong branching (reliable=%g, %d/%d cands, %d uninit, maxcands=%d, maxlookahead=%g, maxbdchgs=%d, inititer=%d, iterations:%" SCIP_LONGINT_FORMAT "/%" SCIP_LONGINT_FORMAT ", basic:%u)\n",
         reliable, ninitcands, nbranchcands, nuninitcands, maxninitcands, maxlookahead, maxbdchgs, inititer,
         SCIPgetNStrongbranchLPIterations(scip), maxnsblpiterations, SCIPisLPSolBasic(scip));
 
      bestsbcand = -1;
      bestsbscore = -SCIPinfinity(scip);
      bestsbfracscore = -SCIPinfinity(scip);
      bestsbdomainscore = -SCIPinfinity(scip);
      bestuninitsbscore = -SCIPinfinity(scip);
      bestuninitsbcand = -1;
      lookahead = 0.0;
 
      for( i = 0; continueStrongBranchingLookahead(scip, i, ninitcands, lookahead, maxlookahead, nbdchgs, nbdconflicts, maxbdchgs, maxnsblpiterations) && continueStrongBranchingTreeSizeEstimation(scip, branchruledata, lookahead, maxlookahead); ++i )
      {
         SCIP_Real down;
         SCIP_Real up;
         SCIP_Real downgain;
         SCIP_Real upgain;
         SCIP_Bool downvalid;
         SCIP_Bool upvalid;
         SCIP_Longint ndomredsdown;
         SCIP_Longint ndomredsup;
         SCIP_Bool lperror;
         SCIP_Bool downinf;
         SCIP_Bool upinf;
         SCIP_Bool downconflict;
         SCIP_Bool upconflict;
 
         /* get candidate number to initialize */
         c = initcands[i];
         assert(!SCIPisFeasIntegral(scip, branchcandssol[c]) || SCIPisExact(scip));
 
         if( branchruledata->skipbadinitcands )
         {
            SCIP_Bool skipsb = FALSE;
            /* if the current best candidate is a candidate found by strong branching, determine if candidate pseudo-costs are
             * significantly smaller in at least one direction, in which case we safe the execution of strong-branching for now
             */
            if( bestsbscore > bestpsscore && bestsbscore > bestuninitsbscore && bestsbupvalid && bestsbdownvalid )
            {
               assert(bestsbcand != -1);
               assert(bestsbup != SCIP_INVALID && bestsbdown != SCIP_INVALID); /*lint !e777 lint doesn't like comparing floats */
 
               /* test if the variable is unlikely to produce a better gain than the currently best one. Skip strong-branching
                * in such a case
                */
               if( SCIPpscostThresholdProbabilityTest(scip, branchcands[c], branchcandsfrac[c], bestsbdown,
                     SCIP_BRANCHDIR_DOWNWARDS, clevel)
                     || SCIPpscostThresholdProbabilityTest(scip, branchcands[c], 1.0 - branchcandsfrac[c], bestsbup,
                           SCIP_BRANCHDIR_UPWARDS, clevel) )
                  skipsb = TRUE;
            }
            /* the currently best candidate is also a pseudo-candidate; apply significance test and skip candidate if it
             * is significantly worse in at least one direction
             */
            else if( bestpscand != -1 && bestpsscore > bestuninitsbscore )
            {
               if( SCIPsignificantVarPscostDifference(scip, branchcands[bestpscand], branchcandsfrac[bestpscand],
                     branchcands[c], branchcandsfrac[c], SCIP_BRANCHDIR_DOWNWARDS, clevel, TRUE)
                     || SCIPsignificantVarPscostDifference(scip, branchcands[bestpscand], 1.0 - branchcandsfrac[bestpscand],
                           branchcands[c], 1.0 - branchcandsfrac[c], SCIP_BRANCHDIR_UPWARDS, clevel, TRUE) )
                  skipsb = TRUE;
            }
            /* compare against the best init cand that has been skipped already */
            else if( bestuninitsbcand != -1 )
            {
               if( SCIPsignificantVarPscostDifference(scip, branchcands[bestuninitsbcand], branchcandsfrac[bestuninitsbcand],
                     branchcands[c], branchcandsfrac[c], SCIP_BRANCHDIR_DOWNWARDS, clevel, TRUE)
                     || SCIPsignificantVarPscostDifference(scip, branchcands[bestuninitsbcand], 1.0 - branchcandsfrac[bestuninitsbcand],
                           branchcands[c], 1.0 - branchcandsfrac[c], SCIP_BRANCHDIR_UPWARDS, clevel, TRUE) )
                  skipsb = TRUE;
            }
 
            /* skip candidate, update the best score of an unitialized candidate */
            if( skipsb )
            {
               if( bestuninitsbcand == -1 )
               {
                  bestuninitsbcand = c;
                  bestuninitsbscore = initcandscores[i];
               }
               continue;
            }
         }
         SCIPdebugMsg(scip, "init pseudo cost (%g/%g) of <%s> at %g (score:%g) with strong branching (%d iterations) -- %" SCIP_LONGINT_FORMAT "/%" SCIP_LONGINT_FORMAT " iterations\n",
            SCIPgetVarPseudocostCountCurrentRun(scip, branchcands[c], SCIP_BRANCHDIR_DOWNWARDS),
            SCIPgetVarPseudocostCountCurrentRun(scip, branchcands[c], SCIP_BRANCHDIR_UPWARDS),
            SCIPvarGetName(branchcands[c]), branchcandssol[c], initcandscores[i],
            inititer, SCIPgetNStrongbranchLPIterations(scip), maxnsblpiterations);
 
         /* use strong branching on candidate */
         if( !initstrongbranching )
         {
            initstrongbranching = TRUE;
 
            SCIP_CALL( SCIPstartStrongbranch(scip, propagate) );
 
            /* create arrays for probing-like bound tightening */
            if( probingbounds )
            {
               SCIP_CALL( SCIPallocBlockMemoryArray(scip, &newlbs, nvars) );
               SCIP_CALL( SCIPallocBlockMemoryArray(scip, &newubs, nvars) );
            }
         }
 
         if( propagate )
         {
            /* apply strong branching */
            SCIP_CALL( SCIPgetVarStrongbranchWithPropagation(scip, branchcands[c], branchcandssol[c], lpobjval, inititer,
                  branchruledata->maxproprounds, &down, &up, &downvalid, &upvalid, &ndomredsdown, &ndomredsup, &downinf, &upinf,
                  &downconflict, &upconflict, &lperror, newlbs, newubs) );
         }
         else
         {
            /* apply strong branching */
            SCIP_CALL( SCIPgetVarStrongbranchFrac(scip, branchcands[c], inititer, FALSE,
                  &down, &up, &downvalid, &upvalid, &downinf, &upinf, &downconflict, &upconflict, &lperror) );
 
            ndomredsdown = ndomredsup = 0;
         }
 
         /* check for an error in strong branching */
         if( lperror )
         {
            if( !SCIPisStopped(scip) )
            {
               SCIPverbMessage(scip, SCIP_VERBLEVEL_HIGH, NULL,
                  "(node %" SCIP_LONGINT_FORMAT ") error in strong branching call for variable <%s> with solution %g\n",
                  SCIPgetNNodes(scip), SCIPvarGetName(branchcands[c]), branchcandssol[c]);
            }
            break;
         }
 
         /* Strong branching might have found a new primal solution which updated the cutoff bound. In this case, the
          * provedbound computed before can be higher than the cutoffbound and the current node can be cut off.
          * Additionally, also if the value for the current best candidate is valid and exceeds the new cutoff bound,
          * we want to change the domain of this variable rather than branching on it.
          */
         if( SCIPgetBestSol(scip) != NULL && SCIPsolGetIndex(SCIPgetBestSol(scip)) != bestsolidx )
         {
            bestsolidx = SCIPsolGetIndex(SCIPgetBestSol(scip));
 
            SCIPdebugMsg(scip, " -> strong branching on variable <%s> lead to a new incumbent\n",
               SCIPvarGetName(branchcands[c]));
 
            /* proved bound for current node is larger than new cutoff bound -> cut off current node */
            if( SCIPisGE(scip, provedbound, SCIPgetCutoffbound(scip)) )
            {
               SCIPdebugMsg(scip, " -> node can be cut off (provedbound=%g, cutoff=%g)\n", provedbound, SCIPgetCutoffbound(scip));
 
               *result = SCIP_CUTOFF;
               break; /* terminate initialization loop, because node is infeasible */
            }
            /* proved bound for down child of best candidate is larger than cutoff bound
             *  -> increase lower bound of best candidate
             * we must only do this if the LP is complete, i.e., we are not doing column generation
             */
 
            else if( bestsbcand != -1  && allcolsinlp )
            {
               if( !bestsbdowncutoff && bestsbdownvalid && SCIPisGE(scip, bestsbdown, SCIPgetCutoffbound(scip)) )
               {
                  bestsbdowncutoff = TRUE;
 
                  SCIPdebugMsg(scip, " -> valid dual bound for down child of best candidate <%s> is higher than new cutoff bound (valid=%u, bestsbdown=%g, cutoff=%g)\n",
                     SCIPvarGetName(branchcands[bestsbcand]), bestsbdownvalid, bestsbdown, SCIPgetCutoffbound(scip));
 
                  SCIPdebugMsg(scip, " -> increase lower bound of best candidate <%s> to %g\n",
                     SCIPvarGetName(branchcands[bestsbcand]), SCIPfeasCeil(scip, branchcandssol[bestsbcand]));
 
                  SCIP_CALL( addBdchg(scip, &bdchginds, &bdchgtypes, &bdchgbounds, &nbdchgs, SCIPvarGetProbindex(branchcands[bestsbcand]),
                        SCIP_BOUNDTYPE_LOWER, SCIPfeasCeil(scip, branchcandssol[bestsbcand])) );
               }
               /* proved bound for up child of best candidate is larger than cutoff bound -> decrease upper bound of best candidate */
               else if( !bestsbupcutoff && bestsbupvalid && SCIPisGE(scip, bestsbup, SCIPgetCutoffbound(scip)) )
               {
                  bestsbupcutoff = TRUE;
 
                  SCIPdebugMsg(scip, " -> valid dual bound for up child of best candidate <%s> is higher than new cutoff bound (valid=%u, bestsbup=%g, cutoff=%g)\n",
                     SCIPvarGetName(branchcands[bestsbcand]), bestsbupvalid, bestsbup, SCIPgetCutoffbound(scip));
 
                  SCIPdebugMsg(scip, " -> decrease upper bound of best candidate <%s> to %g\n",
                     SCIPvarGetName(branchcands[bestsbcand]), SCIPfeasFloor(scip, branchcandssol[bestsbcand]));
 
                  SCIP_CALL( addBdchg(scip, &bdchginds, &bdchgtypes, &bdchgbounds, &nbdchgs, SCIPvarGetProbindex(branchcands[bestsbcand]),
                        SCIP_BOUNDTYPE_UPPER, SCIPfeasFloor(scip, branchcandssol[bestsbcand])) );
               }
            }
         }
 
         /* evaluate strong branching */
         down = MAX(down, lpobjval);
         up = MAX(up, lpobjval);
         downgain = down - lpobjval;
         upgain = up - lpobjval;
         assert(!useancpscost);
         assert(!allcolsinlp || exactsolve || !downvalid || downinf == SCIPisGE(scip, down, SCIPgetCutoffbound(scip)));
         assert(!allcolsinlp || exactsolve || !upvalid || upinf == SCIPisGE(scip, up, SCIPgetCutoffbound(scip)));
         assert(downinf || !downconflict);
         assert(upinf || !upconflict);
 
         if( branchruledata->dynamiclookahead )
            SCIP_CALL( updateMinMaxMeanGain(scip, branchrule, downgain, upgain) );
 
         /* @todo: store pseudo cost only for valid bounds?
          * depending on the user parameter choice of storesemiinitcosts, pseudo costs are also updated in single directions,
          * if the node in the other direction was infeasible or cut off
          */
         if( !downinf
#ifdef WITH_LPSOLSTAT
               && SCIPgetLastStrongbranchLPSolStat(scip, SCIP_BRANCHDIR_DOWNWARDS) != SCIP_LPSOLSTAT_ITERLIMIT
#endif
               && (!upinf || branchruledata->storesemiinitcosts) )
         {
            SCIP_Real weight;
 
            /* smaller weights are given if the strong branching hit the time limit in the corresponding direction */
            if( branchruledata->usesmallweightsitlim )
               weight = SCIPgetLastStrongbranchLPSolStat(scip, SCIP_BRANCHDIR_DOWNWARDS) != SCIP_LPSOLSTAT_ITERLIMIT ? 1.0 : 0.5;
            else
               weight = 1.0;
 
            /* update pseudo cost values */
            SCIP_CALL( SCIPupdateVarPseudocostSymmetric(scip, branchruledata, branchcands[c], branchorbitidx, c, 0.0 - branchcandsfrac[c], downgain, weight) );
         }
         if( !upinf
#ifdef WITH_LPSOLSTAT
               && SCIPgetLastStrongbranchLPSolStat(scip, SCIP_BRANCHDIR_UPWARDS) != SCIP_LPSOLSTAT_ITERLIMIT
#endif
               && (!downinf || branchruledata->storesemiinitcosts)  )
         {
            SCIP_Real weight;
 
            /* smaller weights are given if the strong branching hit the time limit in the corresponding direction */
            if( branchruledata->usesmallweightsitlim )
               weight = SCIPgetLastStrongbranchLPSolStat(scip, SCIP_BRANCHDIR_UPWARDS) != SCIP_LPSOLSTAT_ITERLIMIT ? 1.0 : 0.5;
            else
               weight = 1.0;
 
            /* update pseudo cost values */
            SCIP_CALL( SCIPupdateVarPseudocostSymmetric(scip, branchruledata, branchcands[c], branchorbitidx, c, 1.0 - branchcandsfrac[c], upgain, weight) );
         }
 
         /* the minimal lower bound of both children is a proved lower bound of the current subtree */
         if( allcolsinlp && !exactsolve && downvalid && upvalid )
         {
            SCIP_Real minbound;
 
            minbound = MIN(down, up);
            provedbound = MAX(provedbound, minbound);
            assert((downinf && upinf) || SCIPisLT(scip, provedbound, SCIPgetCutoffbound(scip)));
 
            /* save probing-like bounds detected during strong branching */
            if( probingbounds && ( !downinf || !upinf ) )
            {
               int v;
 
               assert(newlbs != NULL);
               assert(newubs != NULL);
 
               for( v = 0; v < nvars; ++v )
               {
                  if( SCIPisGT(scip, newlbs[v], SCIPvarGetLbLocal(vars[v])) )
                  {
                     SCIPdebugMsg(scip, "better lower bound for variable <%s>: %.9g -> %.9g (by strongbranching on <%s>)\n",
                        SCIPvarGetName(vars[v]), SCIPvarGetLbLocal(vars[v]), newlbs[v], SCIPvarGetName(branchcands[c]));
 
                     SCIP_CALL( addBdchg(scip, &bdchginds, &bdchgtypes, &bdchgbounds, &nbdchgs, v,
                           SCIP_BOUNDTYPE_LOWER, newlbs[v]) );
                  }
                  if( SCIPisLT(scip, newubs[v], SCIPvarGetUbLocal(vars[v])) )
                  {
                     SCIPdebugMsg(scip, "better upper bound for variable <%s>: %.9g -> %.9g (by strongbranching on <%s>)\n",
                        SCIPvarGetName(vars[v]), SCIPvarGetUbLocal(vars[v]), newubs[v], SCIPvarGetName(branchcands[c]));
 
                     SCIP_CALL( addBdchg(scip, &bdchginds, &bdchgtypes, &bdchgbounds, &nbdchgs, v,
                           SCIP_BOUNDTYPE_UPPER, newubs[v]) );
                  }
               }
            }
         }
 
         /* check if there are infeasible roundings */
         if( (downinf || upinf) && !exactsolve )
         {
            assert(allcolsinlp || propagate);
 
            if( downinf && upinf )
            {
               /* both roundings are infeasible -> node is infeasible */
               SCIPdebugMsg(scip, " -> variable <%s> is infeasible in both directions (conflict: %u/%u)\n",
                  SCIPvarGetName(branchcands[c]), downconflict, upconflict);
               *result = SCIP_CUTOFF;
               break; /* terminate initialization loop, because node is infeasible */
            }
            else
            {
               /* rounding is infeasible in one direction -> round variable in other direction */
               SCIPdebugMsg(scip, " -> variable <%s> is infeasible in %s branch (conflict: %u/%u)\n",
                  SCIPvarGetName(branchcands[c]), downinf ? "downward" : "upward", downconflict, upconflict);
               SCIP_CALL( addBdchg(scip, &bdchginds, &bdchgtypes, &bdchgbounds, &nbdchgs, SCIPvarGetProbindex(branchcands[c]),
                     (downinf ? SCIP_BOUNDTYPE_LOWER : SCIP_BOUNDTYPE_UPPER),
                     (downinf ? SCIPfeasCeil(scip, branchcandssol[c]) : SCIPfeasFloor(scip, branchcandssol[c]))) );
               if( nbdchgs + nbdconflicts >= maxbdchgs )
                  break; /* terminate initialization loop, because enough roundings are performed */
            }
         }
         else
         {
            SCIP_Real conflictscore;
            SCIP_Real conflengthscore;
            SCIP_Real inferencescore;
            SCIP_Real cutoffscore;
            SCIP_Real gmieffscore;
            SCIP_Real lastgmieffscore;
            SCIP_Real pscostscore;
            SCIP_Real nlscore;
            SCIP_Real score;
 
            mingains[c] = MIN(downgain, upgain);
            maxgains[c] = MAX(downgain, upgain);
 
            sbdown[c] = down;
            sbup[c] = up;
            sbdownvalid[c] = downvalid;
            sbupvalid[c] = upvalid;
 
            /* check for a better score */
            conflictscore = SCIPgetVarConflictScore(scip, branchcands[c]);
            conflengthscore = SCIPgetVarConflictlengthScore(scip, branchcands[c]);
            nlscore = calcNlscore(scip, branchruledata->nlcount, branchruledata->nlcountmax, SCIPvarGetProbindex(branchcands[c]));
 
            /* optionally, use only local information obtained via strong branching for this candidate, i.e., local
             * domain reductions and no cutoff score
             */
            inferencescore = branchruledata->usesblocalinfo ? SCIPgetBranchScore(scip, branchcands[c], (SCIP_Real)ndomredsdown, (SCIP_Real)ndomredsup)
                  : SCIPgetVarAvgInferenceScore(scip, branchcands[c]);
            cutoffscore = branchruledata->usesblocalinfo ? 0.0 : SCIPgetVarAvgCutoffScore(scip, branchcands[c]);
            gmieffscore = branchruledata->usesblocalinfo ? 0.0 : SCIPgetVarAvgGMIScore(scip, branchcands[c]);
            lastgmieffscore = branchruledata->usesblocalinfo ? 0.0 : SCIPgetVarLastGMIScore(scip, branchcands[c]);
            pscostscore = SCIPgetBranchScore(scip, branchcands[c], downgain, upgain);
 
            scoresfrompc[c] =  calcScore(scip, branchruledata, 0.0, avgconflictscore, 0.0, avgconflengthscore,
                                         0.0, avginferencescore, 0.0, avgcutoffscore, 0.0, 0.0, pscostscore,
                                         avgpscostscore, 0.0, branchcandsfrac[c], degeneracyfactor);
            scoresfromothers[c] = calcScore(scip, branchruledata, conflictscore, avgconflictscore, conflengthscore, avgconflengthscore,
                                            inferencescore, avginferencescore, cutoffscore, avgcutoffscore, gmieffscore,
                                            lastgmieffscore, 0.0, avgpscostscore, nlscore, branchcandsfrac[c],
                                            degeneracyfactor);
            score = scoresfrompc[c] + scoresfromothers[c];
            scores[c] = score;
            /*score = calcScore(scip, branchruledata, conflictscore, avgconflictscore, conflengthscore, avgconflengthscore,
               inferencescore, avginferencescore, cutoffscore, avgcutoffscore, gmieffscore, lastgmieffscore,
               pscostscore, avgpscostscore, nlscore, branchcandsfrac[c], degeneracyfactor);*/
 
            if( SCIPisSumGE(scip, score, bestsbscore) )
            {
               SCIP_Real fracscore;
               SCIP_Real domainscore;
 
               fracscore = MIN(branchcandsfrac[c], 1.0 - branchcandsfrac[c]);
               domainscore = -(SCIPvarGetUbLocal(branchcands[c]) - SCIPvarGetLbLocal(branchcands[c]));
               if( SCIPisSumGT(scip, score, bestsbscore)
                  || SCIPisSumGT(scip, fracscore, bestsbfracscore)
                  || (SCIPisSumGE(scip, fracscore, bestsbfracscore) && domainscore > bestsbdomainscore) )
               {
                  bestsbcand = c;
                  bestsbscore = score;
                  bestsbdown = down;
                  bestsbup = up;
                  bestsbdownvalid = downvalid;
                  bestsbupvalid = upvalid;
                  bestsbdowncutoff = FALSE;
                  bestsbupcutoff = FALSE;
                  bestsbfracscore = fracscore;
                  bestsbdomainscore = domainscore;
                  lookahead = 0.0;
               }
               else
                  lookahead += 0.5;
            }
            else
               lookahead += 1.0;
 
            SCIPdebugMsg(scip, " -> variable <%s> (solval=%g, down=%g (%+g,valid=%u), up=%g (%+g,valid=%u), score=%g/ %g/%g %g/%g %g -> %g)\n",
               SCIPvarGetName(branchcands[c]), branchcandssol[c], down, downgain, downvalid, up, upgain, upvalid,
               pscostscore, conflictscore, conflengthscore, inferencescore, cutoffscore, gmieffscore, score);
         }
      }
#ifdef SCIP_DEBUG
      if( bestsbcand >= 0 )
      {
         SCIPdebugMsg(scip, " -> best: <%s> (%g / %g / %g), lookahead=%g/%g\n",
            SCIPvarGetName(branchcands[bestsbcand]), bestsbscore, bestsbfracscore, bestsbdomainscore,
            lookahead, maxlookahead);
      }
#endif
 
      if( initstrongbranching )
      {
         if( probingbounds )
         {
            assert(newlbs != NULL);
            assert(newubs != NULL);
 
            SCIPfreeBlockMemoryArray(scip, &newubs, nvars);
            SCIPfreeBlockMemoryArray(scip, &newlbs, nvars);
         }
 
         SCIP_CALL( SCIPendStrongbranch(scip) );
 
         if( SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OBJLIMIT || SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_INFEASIBLE )
         {
            assert(SCIPhasCurrentNodeLP(scip));
            *result = SCIP_CUTOFF;
         }
      }
 
      if( *result != SCIP_CUTOFF )
      {
         /* get the score of the best uninitialized strong branching candidate */
         if( i < ninitcands && bestuninitsbcand == -1 )
            bestuninitsbscore = initcandscores[i];
 
         /* if the best pseudo cost candidate is better than the best uninitialized strong branching candidate,
          * compare it to the best initialized strong branching candidate
          */
         if( bestpsscore > bestuninitsbscore && SCIPisSumGT(scip, bestpsscore, bestsbscore) )
         {
            bestcand = bestpscand;
            bestisstrongbranch = FALSE;
         }
         else if( bestsbcand >= 0 )
         {
            bestcand = bestsbcand;
            bestisstrongbranch = TRUE;
         }
         else
         {
            /* no candidate was initialized, and the best score is the one of the first candidate in the initialization
             * queue
             */
            assert(ninitcands >= 1);
            bestcand = initcands[0];
            bestisstrongbranch = FALSE;
         }
 
         /* update lower bound of current node */
         if( allcolsinlp && !exactsolve )
         {
            assert(SCIPisLT(scip, provedbound, SCIPgetCutoffbound(scip)));
            SCIP_CALL( SCIPupdateLocalLowerbound(scip, provedbound) );
            assert(SCIPisGE(scip, SCIPgetLocalLowerbound(scip), provedbound));
         }
      }
 
      /* apply domain reductions */
      if( nbdchgs > 0 )
      {
         if( *result != SCIP_CUTOFF )
         {
            SCIP_CALL( applyBdchgs(scip, vars, bdchginds, bdchgtypes, bdchgbounds, nbdchgs, result) );
            if( *result != SCIP_CUTOFF )
               *result = SCIP_REDUCEDDOM;
         }
         freeBdchgs(scip, &bdchginds, &bdchgtypes, &bdchgbounds, &nbdchgs);
      }
 
      /* Apply the Treemodel branching rule to potentially select a better branching candidate than the current one. */
      if( *result != SCIP_CUTOFF && *result != SCIP_REDUCEDDOM && *result != SCIP_CONSADDED && SCIPtreemodelIsEnabled(scip, branchruledata->treemodel) )
      {
         SCIP_Real smallpscost;
         SCIP_Bool usetreemodel;
 
         usetreemodel = TRUE;
 
         /* If the pseudocosts are zero, use SCIPs best variable since the Treemodel is not applicable */
         if( SCIPisZero(scip, maxgains[bestcand]))
         {
             usetreemodel = FALSE;
         }
 
         /* If SCIPs best candidate was selected due to hybrid branching scores
          * rather than because of pseudocosts, then we keep it.
          */
         SCIP_CALL( SCIPgetRealParam(scip, "branching/treemodel/smallpscost", &smallpscost) );
         if( usetreemodel == TRUE && avgpscostscore <= smallpscost )
         {
            int cand;
            for( cand = 0; cand < nbranchcands; ++cand )
            {
               if( scoresfrompc[cand] > scoresfrompc[bestcand] )
               {
                  usetreemodel = FALSE;
                  break;
               }
            }
         }
 
         if( usetreemodel == TRUE )
         {
            SCIP_CALL( SCIPtreemodelSelectCandidate(
               scip,                        /* SCIP data structure */
               branchruledata->treemodel,   /* branching rule */
               branchcands,                 /* branching candidate storage */
               mingains,                    /* minimum gain of rounding downwards or upwards */
               maxgains,                    /* maximum gain of rounding downwards or upwards */
               scoresfromothers,            /* scores from other branching methods */
               nbranchcands,                /* the number of branching candidates */
               &bestcand                    /* the best branching candidate found by SCIP */
            ) );
         }
      }
 
      /* free buffer for the lp gains and pseudocost scores */
      SCIPfreeBufferArray(scip, &scoresfromothers);
      SCIPfreeBufferArray(scip, &scoresfrompc);
      SCIPfreeBufferArray(scip, &scores);
      SCIPfreeBufferArray(scip, &maxgains);
      SCIPfreeBufferArray(scip, &mingains);
 
      /* free buffer for the unreliable candidates */
      SCIPfreeBufferArray(scip, &initcandscores);
      SCIPfreeBufferArray(scip, &initcands);
   }
 
   /* if no domain could be reduced, create the branching */
   if( *result != SCIP_CUTOFF && *result != SCIP_REDUCEDDOM && *result != SCIP_CONSADDED && executebranch )
   {
      SCIP_NODE* downchild;
      SCIP_NODE* upchild;
      SCIP_VAR* var;
      SCIP_Real val;
 
      assert(*result == SCIP_DIDNOTRUN);
      assert(0 <= bestcand && bestcand < nbranchcands);
      assert(!SCIPisFeasIntegral(scip, branchcandssol[bestcand]));
      assert(!allcolsinlp || SCIPisLT(scip, provedbound, SCIPgetCutoffbound(scip)) || exactsolve);
      assert(!bestsbdowncutoff && !bestsbupcutoff);
 
      var = branchcands[bestcand];
      val = branchcandssol[bestcand];
 
      /* perform the branching */
      SCIPdebugMsg(scip, " -> %d (%d) cands, sel cand %d: var <%s> (sol=%g, down=%g (%+g), up=%g (%+g), sb=%u, psc=%g/%g [%g])\n",
         nbranchcands, ninitcands, bestcand, SCIPvarGetName(var), branchcandssol[bestcand],
         bestsbdown, bestsbdown - lpobjval, bestsbup, bestsbup - lpobjval, bestisstrongbranch,
         SCIPgetVarPseudocostCurrentRun(scip, var, SCIP_BRANCHDIR_DOWNWARDS),
         SCIPgetVarPseudocostCurrentRun(scip, var, SCIP_BRANCHDIR_UPWARDS),
         SCIPgetVarPseudocostScoreCurrentRun(scip, var, branchcandssol[bestcand]));
      SCIP_UNUSED(bestisstrongbranch);
      SCIP_CALL( SCIPbranchVarVal(scip, var, val, &downchild, NULL, &upchild) );
      assert(downchild != NULL);
      assert(upchild != NULL);
 
      /* update the lower bounds in the children */
      if( allcolsinlp && !exactsolve )
      {
         if( sbdownvalid[bestcand] )
         {
            assert(SCIPisLT(scip, sbdown[bestcand], SCIPgetCutoffbound(scip)));
            SCIP_CALL( SCIPupdateNodeLowerbound(scip, downchild, sbdown[bestcand]) );
            assert(SCIPisGE(scip, SCIPgetNodeLowerbound(scip, downchild), provedbound));
         }
         if( sbupvalid[bestcand] )
         {
            assert(SCIPisLT(scip, sbup[bestcand], SCIPgetCutoffbound(scip)));
            SCIP_CALL( SCIPupdateNodeLowerbound(scip, upchild, sbup[bestcand]) );
            assert(SCIPisGE(scip, SCIPgetNodeLowerbound(scip, upchild), provedbound));
         }
      }
 
      SCIPdebugMsg(scip, " -> down child's lowerbound: %g\n", SCIPnodeGetLowerbound(downchild));
      SCIPdebugMsg(scip, " -> up child's lowerbound  : %g\n", SCIPnodeGetLowerbound(upchild));
 
      assert(SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_INFEASIBLE && SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OBJLIMIT);
 
      *result = SCIP_BRANCHED;
   }
 
   /* free buffer for the strong branching lp gains */
   SCIPfreeBufferArray(scip, &sbupvalid);
   SCIPfreeBufferArray(scip, &sbdownvalid);
   SCIPfreeBufferArray(scip, &sbup);
   SCIPfreeBufferArray(scip, &sbdown);
 
   return SCIP_OKAY;
}
 
 
/*
 * Callback methods
 */
 
/** copy method for branchrule plugins (called when SCIP copies plugins) */
static
SCIP_DECL_BRANCHCOPY(branchCopyRelpscost)
{  /*lint --e{715}*/
   assert(scip != NULL);
   assert(branchrule != NULL);
   assert(strcmp(SCIPbranchruleGetName(branchrule), BRANCHRULE_NAME) == 0);
 
   /* call inclusion method of branchrule */
   SCIP_CALL( SCIPincludeBranchruleRelpscost(scip) );
 
   return SCIP_OKAY;
}
 
/** destructor of branching rule to free user data (called when SCIP is exiting) */
static
SCIP_DECL_BRANCHFREE(branchFreeRelpscost)
{  /*lint --e{715}*/
   SCIP_BRANCHRULEDATA* branchruledata;
   branchruledata = SCIPbranchruleGetData(branchrule);
 
   /* free Treemodel parameter data structure */
   SCIP_CALL( SCIPtreemodelFree(scip, &branchruledata->treemodel) );
 
   /* free branching rule data */
   SCIPfreeBlockMemory(scip, &branchruledata);
   SCIPbranchruleSetData(branchrule, NULL);
 
   return SCIP_OKAY;
}
 
 
/** solving process initialization method of branching rule (called when branch and bound process is about to begin) */
static
SCIP_DECL_BRANCHINITSOL(branchInitsolRelpscost)
{  /*lint --e{715}*/
   SCIP_BRANCHRULEDATA* branchruledata;
 
   /* initialize branching rule data */
   branchruledata = SCIPbranchruleGetData(branchrule);
   branchruledata->nlcount = NULL;
   branchruledata->nlcountsize = 0;
   branchruledata->nlcountmax = 1;
   assert(branchruledata->startrandseed >= 0);
 
   /* create a random number generator */
   SCIP_CALL( SCIPcreateRandom(scip, &branchruledata->randnumgen,
         (unsigned int)branchruledata->startrandseed, TRUE) );
 
   return SCIP_OKAY;
}
 
 
/** solving process deinitialization method of branching rule (called before branch and bound process data is freed) */
static
SCIP_DECL_BRANCHEXITSOL(branchExitsolRelpscost)
{  /*lint --e{715}*/
   SCIP_BRANCHRULEDATA* branchruledata;
 
   /* free memory in branching rule data */
   branchruledata = SCIPbranchruleGetData(branchrule);
   SCIPfreeBlockMemoryArrayNull(scip, &branchruledata->nlcount, branchruledata->nlcountsize);
 
   /* free random number generator */
   SCIPfreeRandom(scip, &branchruledata->randnumgen);
 
   SCIPfreeBlockMemoryArrayNull(scip, &branchruledata->orbitrep, branchruledata->npermvars);
   SCIPfreeBlockMemoryArrayNull(scip, &branchruledata->varorbitmap, branchruledata->npermvars);
   SCIPfreeBlockMemoryArrayNull(scip, &branchruledata->orbitbegins, branchruledata->npermvars);
   SCIPfreeBlockMemoryArrayNull(scip, &branchruledata->orbits, branchruledata->npermvars);
   branchruledata->nosymmetry = FALSE;
   branchruledata->norbits = 0;
   branchruledata->permvars = NULL;
   branchruledata->permvarmap = NULL;
   branchruledata->npermvars = 0;
 
   return SCIP_OKAY;
}
 
 
/** branching execution method for fractional LP solutions */
static
SCIP_DECL_BRANCHEXECLP(branchExeclpRelpscost)
{  /*lint --e{715}*/
   SCIP_BRANCHRULEDATA* branchruledata;
   SCIP_VAR** lpcands;
   SCIP_Real* lpcandssol;
   SCIP_Real* lpcandsfrac;
   SCIP_VAR** filteredlpcands;
   SCIP_Real* filteredlpcandssol;
   SCIP_Real* filteredlpcandsfrac;
   SCIP_Bool runfiltering;
   int* filteredlpcandsorbitidx = NULL;
   int nfilteredlpcands;
   int nlpcands;
 
   assert(branchrule != NULL);
   assert(strcmp(SCIPbranchruleGetName(branchrule), BRANCHRULE_NAME) == 0);
   assert(scip != NULL);
   assert(result != NULL);
 
   SCIPdebugMsg(scip, "Execlp method of relpscost branching in node %" SCIP_LONGINT_FORMAT "\n", SCIPnodeGetNumber(SCIPgetCurrentNode(scip)));
 
   if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
   {
      *result = SCIP_DIDNOTRUN;
      SCIPdebugMsg(scip, "Could not apply relpscost branching, as the current LP was not solved to optimality.\n");
 
      return SCIP_OKAY;
   }
 
   /* get branching candidates */
   SCIP_CALL( SCIPgetLPBranchCands(scip, &lpcands, &lpcandssol, &lpcandsfrac, NULL, &nlpcands, NULL) );
   assert(nlpcands > 0);
 
   branchruledata = SCIPbranchruleGetData(branchrule);
   assert(branchruledata != NULL);
 
   branchruledata->maxmeangain = -SCIPinfinity(scip);
   branchruledata->minmeangain = SCIPinfinity(scip);
   branchruledata->sumlogmeangains = 0.0;
   branchruledata->logstdevgain = 0.0;
   branchruledata->nzerogains = 0;
 
   /* determine whether we should run filtering */
   runfiltering = ! branchruledata->nosymmetry && branchruledata->filtercandssym && SCIPgetSubscipDepth(scip) == 0 && ! SCIPinDive(scip) && ! SCIPinProbing(scip);
 
   /* init orbits if necessary */
   if( runfiltering )
   {
      SCIP_CALL( initOrbits(scip, branchruledata) );
   }
 
   /* determine fractional variables (possibly filter by using symmetries) */
   if( runfiltering && branchruledata->norbits != 0 )
   {
      SCIP_CALL( SCIPallocBufferArray(scip, &filteredlpcands, nlpcands) );
      SCIP_CALL( SCIPallocBufferArray(scip, &filteredlpcandssol, nlpcands) );
      SCIP_CALL( SCIPallocBufferArray(scip, &filteredlpcandsfrac, nlpcands) );
      SCIP_CALL( SCIPallocBufferArray(scip, &filteredlpcandsorbitidx, nlpcands) );
 
      /* determine filtered fractional variables */
      SCIP_CALL( filterSymmetricVariables(scip, branchruledata, lpcands, lpcandssol, lpcandsfrac, nlpcands,
            filteredlpcands, filteredlpcandssol, filteredlpcandsfrac, filteredlpcandsorbitidx, &nfilteredlpcands) );
   }
   else
   {
      /* No orbits available. Copy all (unfiltered) branching candidates, because they will be updated w.r.t. the strong branching LP solution */
      SCIP_CALL( SCIPduplicateBufferArray(scip, &filteredlpcands, lpcands, nlpcands) );
      SCIP_CALL( SCIPduplicateBufferArray(scip, &filteredlpcandssol, lpcandssol, nlpcands) );
      SCIP_CALL( SCIPduplicateBufferArray(scip, &filteredlpcandsfrac, lpcandsfrac, nlpcands) );
      nfilteredlpcands = nlpcands;
   }
 
   /* execute branching rule */
   SCIP_CALL( execRelpscost(scip, branchrule, filteredlpcands, filteredlpcandssol, filteredlpcandsfrac, filteredlpcandsorbitidx, nfilteredlpcands, TRUE, result) );
 
   SCIPfreeBufferArrayNull(scip, &filteredlpcandsorbitidx);
   SCIPfreeBufferArray(scip, &filteredlpcandsfrac);
   SCIPfreeBufferArray(scip, &filteredlpcandssol);
   SCIPfreeBufferArray(scip, &filteredlpcands);
 
   return SCIP_OKAY;
}
 
 
/*
 * branching specific interface methods
 */
 
/** creates the reliable pseudo cost branching rule and includes it in SCIP */
SCIP_RETCODE SCIPincludeBranchruleRelpscost(
   SCIP*                 scip                /**< SCIP data structure */
   )
{
   SCIP_BRANCHRULEDATA* branchruledata;
   SCIP_BRANCHRULE* branchrule;
 
   /* create relpscost branching rule data */
   SCIP_CALL( SCIPallocBlockMemory(scip, &branchruledata) );
 
   branchruledata->nosymmetry = FALSE;
   branchruledata->orbits = NULL;
   branchruledata->orbitbegins = NULL;
   branchruledata->orbitrep = NULL;
   branchruledata->varorbitmap = NULL;
   branchruledata->norbits = 0;
   branchruledata->permvars = NULL;
   branchruledata->npermvars = 0;
   branchruledata->permvarmap = NULL;
   branchruledata->meandualgain = 0.0;
   branchruledata->currmeandualgain = 0.0;
   branchruledata->currndualgains = 0;
   branchruledata->maxmeangain = -SCIPinfinity(scip);
   branchruledata->minmeangain = SCIPinfinity(scip);
   branchruledata->sumlogmeangains = 0.0;
   branchruledata->logstdevgain = 0.0;
   branchruledata->nzerogains = 0;
 
   /* include branching rule */
   SCIP_CALL( SCIPincludeBranchruleBasic(scip, &branchrule, BRANCHRULE_NAME, BRANCHRULE_DESC, BRANCHRULE_PRIORITY,
         BRANCHRULE_MAXDEPTH, BRANCHRULE_MAXBOUNDDIST, branchruledata) );
 
   assert(branchrule != NULL);
 
   /* set non-fundamental callbacks via specific setter functions*/
   SCIP_CALL( SCIPsetBranchruleCopy(scip, branchrule, branchCopyRelpscost) );
   SCIP_CALL( SCIPsetBranchruleFree(scip, branchrule, branchFreeRelpscost) );
   SCIP_CALL( SCIPsetBranchruleInitsol(scip, branchrule, branchInitsolRelpscost) );
   SCIP_CALL( SCIPsetBranchruleExitsol(scip, branchrule, branchExitsolRelpscost) );
   SCIP_CALL( SCIPsetBranchruleExecLp(scip, branchrule, branchExeclpRelpscost) );
 
   /* relpscost branching rule parameters */
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/conflictweight",
         "weight in score calculations for conflict score",
         &branchruledata->conflictweight, TRUE, DEFAULT_CONFLICTWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/conflictlengthweight",
         "weight in score calculations for conflict length score",
         &branchruledata->conflengthweight, TRUE, DEFAULT_CONFLENGTHWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/inferenceweight",
         "weight in score calculations for inference score",
         &branchruledata->inferenceweight, TRUE, DEFAULT_INFERENCEWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/cutoffweight",
         "weight in score calculations for cutoff score",
         &branchruledata->cutoffweight, TRUE, DEFAULT_CUTOFFWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
          "branching/relpscost/gmiavgeffweight",
          "weight in score calculations for average GMI cuts normalized efficacy",
          &branchruledata->gmiavgeffweight, TRUE, DEFAULT_GMIAVGEFFWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
          "branching/relpscost/gmilasteffweight",
          "weight in score calculations for last GMI cuts normalized efficacy",
          &branchruledata->gmilasteffweight, TRUE, DEFAULT_GMILASTEFFWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/pscostweight",
         "weight in score calculations for pseudo cost score",
         &branchruledata->pscostweight, TRUE, DEFAULT_PSCOSTWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/nlscoreweight",
         "weight in score calculations for nlcount score",
         &branchruledata->nlscoreweight, TRUE, DEFAULT_NLSCOREWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/minreliable",
         "minimal value for minimum pseudo cost size to regard pseudo cost value as reliable",
         &branchruledata->minreliable, TRUE, DEFAULT_MINRELIABLE, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/maxreliable",
         "maximal value for minimum pseudo cost size to regard pseudo cost value as reliable",
         &branchruledata->maxreliable, TRUE, DEFAULT_MAXRELIABLE, 0.0, SCIP_REAL_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/sbiterquot",
         "maximal fraction of strong branching LP iterations compared to node relaxation LP iterations",
         &branchruledata->sbiterquot, FALSE, DEFAULT_SBITERQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
  SCIP_CALL( SCIPaddRealParam(scip,
         "branching/relpscost/dynamiclookaheadquot",
         "apply dynamic lookahead after this fraction maxlookahead is reached",
         &branchruledata->dynamiclookaheadquot, TRUE, DEFAULT_DYNAMICLOOKAHEADQUOT , 0.0, 1.0, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "branching/relpscost/sbiterofs",
         "additional number of allowed strong branching LP iterations",
         &branchruledata->sbiterofs, FALSE, DEFAULT_SBITEROFS, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "branching/relpscost/maxlookahead",
         "maximal number of further variables evaluated without better score",
         &branchruledata->maxlookahead, TRUE, DEFAULT_MAXLOOKAHEAD, 1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/dynamiclookahead",
         "should we use a dynamic lookahead based on a tree size estimation of further strong branchings?",
         &branchruledata->dynamiclookahead, TRUE, DEFAULT_DYNAMICLOOKAHEAD, NULL, NULL) );
  SCIP_CALL( SCIPaddIntParam(scip,
         "branching/relpscost/initcand",
         "maximal number of candidates initialized with strong branching per node",
         &branchruledata->initcand, FALSE, DEFAULT_INITCAND, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip, "branching/relpscost/dynamiclookdistribution",
         "which distribution should be used for dynamic lookahead? 0=exponential, 1=Pareto, 2=log-normal?",
         &branchruledata->dynamiclookdistribution, TRUE, DEFAULT_DYNAMICLOOKDISTRIBUTION, EXPONENTIALDISTRIBUTION, LOGNORMALDISTRIBUTION, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "branching/relpscost/inititer",
         "iteration limit for strong branching initializations of pseudo cost entries (0: auto)",
         &branchruledata->inititer, FALSE, DEFAULT_INITITER, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "branching/relpscost/maxbdchgs",
         "maximal number of bound tightenings before the node is reevaluated (-1: unlimited)",
         &branchruledata->maxbdchgs, TRUE, DEFAULT_MAXBDCHGS, -1, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "branching/relpscost/maxproprounds",
         "maximum number of propagation rounds to be performed during strong branching before solving the LP (-1: no limit, -2: parameter settings)",
         &branchruledata->maxproprounds, TRUE, SCIPisExact(scip) ? 0 : DEFAULT_MAXPROPROUNDS, -2, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddBoolParam(scip,
         "branching/relpscost/probingbounds",
         "should valid bounds be identified in a probing-like fashion during strong branching (only with propagation)?",
         &branchruledata->probingbounds, TRUE, DEFAULT_PROBINGBOUNDS, NULL, NULL) );
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/userelerrorreliability",
         "should reliability be based on relative errors?", &branchruledata->userelerrorforreliability, TRUE, DEFAULT_USERELERRORFORRELIABILITY,
         NULL, NULL) );
 
   SCIP_CALL( SCIPaddRealParam(scip, "branching/relpscost/lowerrortol", "low relative error tolerance for reliability",
         &branchruledata->lowerrortol, TRUE, DEFAULT_LOWERRORTOL, 0.0, SCIP_REAL_MAX, NULL, NULL) );
 
   SCIP_CALL( SCIPaddRealParam(scip, "branching/relpscost/higherrortol", "high relative error tolerance for reliability",
         &branchruledata->higherrortol, TRUE, DEFAULT_HIGHERRORTOL, 0.0, SCIP_REAL_MAX, NULL, NULL) );
 
/**! [SnippetCodeStyleParenIndent] */
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/storesemiinitcosts",
         "should strong branching result be considered for pseudo costs if the other direction was infeasible?",
         &branchruledata->storesemiinitcosts, TRUE, DEFAULT_STORESEMIINITCOSTS,
         NULL, NULL) );
 
/**! [SnippetCodeStyleParenIndent] */
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/usesblocalinfo",
         "should the scoring function use only local cutoff and inference information obtained for strong branching candidates?",
         &branchruledata->usesblocalinfo, TRUE, DEFAULT_USESBLOCALINFO,
         NULL, NULL) );
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/usehyptestforreliability",
         "should the strong branching decision be based on a hypothesis test?",
         &branchruledata->usehyptestforreliability, TRUE, DEFAULT_USEHYPTESTFORRELIABILITY,
         NULL, NULL) );
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/usedynamicconfidence",
         "should the confidence level be adjusted dynamically?",
         &branchruledata->usedynamicconfidence, TRUE, DEFAULT_USEDYNAMICCONFIDENCE,
         NULL, NULL) );
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/skipbadinitcands",
         "should branching rule skip candidates that have a low probability to "
         "be better than the best strong-branching or pseudo-candidate?",
         &branchruledata->skipbadinitcands, TRUE, DEFAULT_SKIPBADINITCANDS,
         NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip,
         "branching/relpscost/confidencelevel",
         "the confidence level for statistical methods, between 0 (Min) and 4 (Max).",
         &branchruledata->confidencelevel, TRUE, DEFAULT_CONFIDENCELEVEL, 0, 4, NULL, NULL) );
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/randinitorder",
         "should candidates be initialized in randomized order?",
         &branchruledata->randinitorder, TRUE, DEFAULT_RANDINITORDER,
         NULL, NULL) );
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/usesmallweightsitlim",
         "should smaller weights be used for pseudo cost updates after hitting the LP iteration limit?",
         &branchruledata->usesmallweightsitlim, TRUE, DEFAULT_USESMALLWEIGHTSITLIM,
         NULL, NULL) );
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/dynamicweights",
         "should the weights of the branching rule be adjusted dynamically during solving based on objective and infeasible leaf counters?",
         &branchruledata->dynamicweights, TRUE, DEFAULT_DYNAMICWEIGHTS,
         NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip, "branching/relpscost/degeneracyaware",
         "should degeneracy be taken into account to update weights and skip strong branching? (0: off, 1: after root, 2: always)",
         &branchruledata->degeneracyaware, TRUE, DEFAULT_DEGENERACYAWARE, 0, 2,
         NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip, "branching/relpscost/startrandseed", "start seed for random number generation",
         &branchruledata->startrandseed, TRUE, DEFAULT_STARTRANDSEED, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddIntParam(scip, "branching/relpscost/minsamplesize",
         "minimum sample size to estimate the tree size for dynamic lookahead",
         &branchruledata->minsamplesize, TRUE, DEFAULT_MINSAMPLESIZE, 0, INT_MAX, NULL, NULL) );
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/filtercandssym",
         "Use symmetry to filter branching candidates?",
         &branchruledata->filtercandssym, TRUE, DEFAULT_FILTERCANDSSYM, NULL, NULL) );
 
   SCIP_CALL( SCIPaddBoolParam(scip, "branching/relpscost/transsympscost",
         "Transfer pscost information to symmetric variables?",
         &branchruledata->transsympscost, TRUE, DEFAULT_TRANSSYMPSCOST, NULL, NULL) );
 
   SCIP_CALL( SCIPaddRealParam(scip, "branching/" BRANCHRULE_NAME "/discountfactor",
         "discount factor for ancestral pseudo costs (0.0: disable discounted pseudo costs)",
         &branchruledata->discountfactor, FALSE, BRANCHRULE_DISCOUNTFACTOR, 0.0, 1.0, NULL, NULL) );
 
   /* relpcost is safe to use in exact solving mode */
   SCIPbranchruleMarkExact(branchrule);
 
   /* initialise the Treemodel parameters */
   SCIP_CALL( SCIPtreemodelInit(scip, &branchruledata->treemodel) );
 
   return SCIP_OKAY;
}
 
/** execution reliability pseudo cost branching with the given branching candidates */
SCIP_RETCODE SCIPexecRelpscostBranching(
   SCIP*                 scip,               /**< SCIP data structure */
   SCIP_VAR**            branchcands,        /**< branching candidates */
   SCIP_Real*            branchcandssol,     /**< solution value for the branching candidates */
   SCIP_Real*            branchcandsfrac,    /**< fractional part of the branching candidates */
   int                   nbranchcands,       /**< number of branching candidates */
   SCIP_Bool             executebranching,   /**< perform a branching step after probing */
   SCIP_RESULT*          result              /**< pointer to the result of the execution */
   )
{
   SCIP_BRANCHRULE* branchrule;
 
   assert(scip != NULL);
   assert(result != NULL);
 
   /* find branching rule */
   branchrule = SCIPfindBranchrule(scip, BRANCHRULE_NAME);
   assert(branchrule != NULL);
 
   /* execute branching rule */
   SCIP_CALL( execRelpscost(scip, branchrule, branchcands, branchcandssol, branchcandsfrac, NULL, nbranchcands, executebranching, result) );
 
   return SCIP_OKAY;
}