source: trunk/Cbc/src/CbcModel.cpp @ 367

// Copyright (C) 2002, International Business Machines
// Corporation and others.  All Rights Reserved.
#if defined(_MSC_VER)
// Turn off compiler warning about long names
#  pragma warning(disable:4786)
#endif

#include "CbcConfig.h"

#include <string>
//#define CBC_DEBUG 1
//#define CHECK_CUT_COUNTS
//#define CHECK_NODE_FULL
//#define NODE_LOG
#include <cassert>
#include <cmath>
#include <cfloat>

#ifdef COIN_HAS_CLP
// include Presolve from Clp
#include "ClpPresolve.hpp"
#include "OsiClpSolverInterface.hpp"
#endif

#include "CbcEventHandler.hpp"

#include "OsiSolverInterface.hpp"
#include "OsiAuxInfo.hpp"
#include "OsiSolverBranch.hpp"
#include "CoinWarmStartBasis.hpp"
#include "CoinPackedMatrix.hpp"
#include "CoinHelperFunctions.hpp"
#include "CbcBranchActual.hpp"
#include "CbcBranchDynamic.hpp"
#include "CbcHeuristic.hpp"
#include "CbcModel.hpp"
#include "CbcTreeLocal.hpp"
#include "CbcStatistics.hpp"
#include "CbcStrategy.hpp"
#include "CbcMessage.hpp"
#include "OsiRowCut.hpp"
#include "OsiColCut.hpp"
#include "OsiRowCutDebugger.hpp"
#include "OsiCuts.hpp"
#include "CbcCountRowCut.hpp"
#include "CbcCutGenerator.hpp"
#include "CbcFeasibilityBase.hpp"
// include Probing
#include "CglProbing.hpp"
// include preprocessing
#include "CglPreProcess.hpp"

#include "CoinTime.hpp"

#include "CbcCompareActual.hpp"
#include "CbcTree.hpp"
/* Various functions local to CbcModel.cpp */

namespace {

//-------------------------------------------------------------------
// Returns the greatest common denominator of two 
// positive integers, a and b, found using Euclid's algorithm 
//-------------------------------------------------------------------
static int gcd(int a, int b) 
{
  int remainder = -1;
  // make sure a<=b (will always remain so)
  if(a > b) {
    // Swap a and b
    int temp = a;
    a = b;
    b = temp;
  }
  // if zero then gcd is nonzero (zero may occur in rhs of packed)
  if (!a) {
    if (b) {
      return b;
    } else {
      printf("**** gcd given two zeros!!\n");
      abort();
    }
  }
  while (remainder) {
    remainder = b % a;
    b = a;
    a = remainder;
  }
  return b;
}



#ifdef CHECK_NODE_FULL

/*
  Routine to verify that tree linkage is correct. The invariant that is tested
  is

  reference count = (number of actual references) + (number of branches left)

  The routine builds a set of paired arrays, info and count, by traversing the
  tree. Each CbcNodeInfo is recorded in info, and the number of times it is
  referenced (via the parent field) is recorded in count. Then a final check is
  made to see if the numberPointingToThis_ field agrees.
*/

void verifyTreeNodes (const CbcTree * branchingTree, const CbcModel &model)

{ printf("*** CHECKING tree after %d nodes\n",model.getNodeCount()) ;

  int j ;
  int nNodes = branchingTree->size() ;
# define MAXINFO 1000
  int *count = new int [MAXINFO] ;
  CbcNodeInfo **info = new CbcNodeInfo*[MAXINFO] ;
  int nInfo = 0 ;
/*
  Collect all CbcNodeInfo objects in info, by starting from each live node and
  traversing back to the root. Nodes in the live set should have unexplored
  branches remaining.

  TODO: The `while (nodeInfo)' loop could be made to break on reaching a
        common ancester (nodeInfo is found in info[k]). Alternatively, the
        check could change to signal an error if nodeInfo is not found above a
        common ancestor.
*/
  for (j = 0 ; j < nNodes ; j++)
  { CbcNode *node = branchingTree->nodePointer(j) ;
  if (!node)
    continue;
    CbcNodeInfo *nodeInfo = node->nodeInfo() ; 
    int change = node->nodeInfo()->numberBranchesLeft() ;
    assert(change) ;
    while (nodeInfo)
    { int k ;
      for (k = 0 ; k < nInfo ; k++)
      { if (nodeInfo == info[k]) break ; }
      if (k == nInfo)
      { assert(nInfo < MAXINFO) ;
        nInfo++ ;
        info[k] = nodeInfo ;
        count[k] = 0 ; }
      nodeInfo = nodeInfo->parent() ; } }
/*
  Walk the info array. For each nodeInfo, look up its parent in info and
  increment the corresponding count.
*/
  for (j = 0 ; j < nInfo ; j++)
  { CbcNodeInfo *nodeInfo = info[j] ;
    nodeInfo = nodeInfo->parent() ;
    if (nodeInfo)
    { int k ;
      for (k = 0 ; k < nInfo ; k++)
      { if (nodeInfo == info[k]) break ; }
      assert (k < nInfo) ;
      count[k]++ ; } }
/*
  Walk the info array one more time and check that the invariant holds. The
  number of references (numberPointingToThis()) should equal the sum of the
  number of actual references (held in count[]) plus the number of potential
  references (unexplored branches, numberBranchesLeft()).
*/
  for (j = 0;j < nInfo;j++) {
    CbcNodeInfo * nodeInfo = info[j] ;
    if (nodeInfo) {
      int k ;
      for (k = 0;k < nInfo;k++)
        if (nodeInfo == info[k])
          break ;
      printf("Nodeinfo %x - %d left, %d count\n",
             nodeInfo,
             nodeInfo->numberBranchesLeft(),
             nodeInfo->numberPointingToThis()) ;
      assert(nodeInfo->numberPointingToThis() ==
             count[k]+nodeInfo->numberBranchesLeft()) ; } }

  delete [] count ;
  delete [] info ;
  
  return ; }

#endif  /* CHECK_NODE_FULL */



#ifdef CHECK_CUT_COUNTS

/*
  Routine to verify that cut reference counts are correct.
*/
void verifyCutCounts (const CbcTree * branchingTree, CbcModel &model)

{ printf("*** CHECKING cuts after %d nodes\n",model.getNodeCount()) ;

  int j ;
  int nNodes = branchingTree->size() ;

/*
  cut.tempNumber_ exists for the purpose of doing this verification. Clear it
  in all cuts. We traverse the tree by starting from each live node and working
  back to the root. At each CbcNodeInfo, check for cuts.
*/
  for (j = 0 ; j < nNodes ; j++)
  { CbcNode *node = branchingTree->nodePointer(j) ;
    CbcNodeInfo * nodeInfo = node->nodeInfo() ;
    assert (node->nodeInfo()->numberBranchesLeft()) ;
    while (nodeInfo)
    { int k ;
      for (k = 0 ; k < nodeInfo->numberCuts() ; k++)
      { CbcCountRowCut *cut = nodeInfo->cuts()[k] ;
        if (cut) cut->tempNumber_ = 0; }
      nodeInfo = nodeInfo->parent() ; } }
/*
  Walk the live set again, this time collecting the list of cuts in use at each
  node. addCuts1 will collect the cuts in model.addedCuts_. Take into account
  that when we recreate the basis for a node, we compress out the slack cuts.
*/
  for (j = 0 ; j < nNodes ; j++)
  { CoinWarmStartBasis *debugws = model.getEmptyBasis() ;
    CbcNode *node = branchingTree->nodePointer(j) ;
    CbcNodeInfo *nodeInfo = node->nodeInfo(); 
    int change = node->nodeInfo()->numberBranchesLeft() ;
    printf("Node %d %x (info %x) var %d way %d obj %g",j,node,
           node->nodeInfo(),node->variable(),node->way(),
           node->objectiveValue()) ;

    model.addCuts1(node,debugws) ;

    int i ;
    int numberRowsAtContinuous = model.numberRowsAtContinuous() ;
    CbcCountRowCut **addedCuts = model.addedCuts() ;
    for (i = 0 ; i < model.currentNumberCuts() ; i++)
    { CoinWarmStartBasis::Status status = 
        debugws->getArtifStatus(i+numberRowsAtContinuous) ;
      if (status != CoinWarmStartBasis::basic && addedCuts[i])
      { addedCuts[i]->tempNumber_ += change ; } }

    while (nodeInfo)
    { nodeInfo = nodeInfo->parent() ;
      if (nodeInfo) printf(" -> %x",nodeInfo); }
    printf("\n") ;
    delete debugws ; }
/*
  The moment of truth: We've tallied up the references by direct scan of the
  search tree. Check for agreement with the count in the cut.

  TODO: Rewrite to check and print mismatch only when tempNumber_ == 0?
*/
  for (j = 0 ; j < nNodes ; j++)
  { CbcNode *node = branchingTree->nodePointer(j) ;
    CbcNodeInfo *nodeInfo = node->nodeInfo(); 
    while (nodeInfo)
    { int k ;
      for (k = 0 ; k < nodeInfo->numberCuts() ; k++)
      { CbcCountRowCut *cut = nodeInfo->cuts()[k] ;
        if (cut && cut->tempNumber_ >= 0)
        { if (cut->tempNumber_ != cut->numberPointingToThis()) 
            printf("mismatch %x %d %x %d %d\n",nodeInfo,k,
                    cut,cut->tempNumber_,cut->numberPointingToThis()) ;
          else
            printf("   match %x %d %x %d %d\n", nodeInfo,k,
                   cut,cut->tempNumber_,cut->numberPointingToThis()) ;
          cut->tempNumber_ = -1 ; } }
      nodeInfo = nodeInfo->parent() ; } }

  return ; }

#endif /* CHECK_CUT_COUNTS */

}

 /* End unnamed namespace for CbcModel.cpp */



void 
CbcModel::analyzeObjective ()
/*
  Try to find a minimum change in the objective function. The first scan
  checks that there are no continuous variables with non-zero coefficients,
  and grabs the largest objective coefficient associated with an unfixed
  integer variable. The second scan attempts to scale up the objective
  coefficients to a point where they are sufficiently close to integer that
  we can pretend they are integer, and calculate a gcd over the coefficients
  of interest. This will be the minimum increment for the scaled coefficients.
  The final action is to scale the increment back for the original coefficients
  and install it, if it's better than the existing value.

  John's note: We could do better than this.

  John's second note - apologies for changing s to z
*/
{ const double *objective = getObjCoefficients() ;
  const double *lower = getColLower() ;
  const double *upper = getColUpper() ;
/*
  Take a first scan to see if there are unfixed continuous variables in the
  objective.  If so, the minimum objective change could be arbitrarily small.
  Also pick off the maximum coefficient of an unfixed integer variable.

  If the objective is found to contain only integer variables, set the
  fathoming discipline to strict.
*/
  double maximumCost = 0.0 ;
  bool possibleMultiple = true ;
  int iColumn ;
  int numberColumns = getNumCols() ;
  for (iColumn = 0 ; iColumn < numberColumns ; iColumn++)
  { if (upper[iColumn] > lower[iColumn]+1.0e-8)
    { if (isInteger(iColumn)) 
        maximumCost = CoinMax(maximumCost,fabs(objective[iColumn])) ;
      else if (objective[iColumn]) 
        possibleMultiple = false ; } }
  setIntParam(CbcModel::CbcFathomDiscipline,possibleMultiple) ;
/*
  If a nontrivial increment is possible, try and figure it out. We're looking
  for gcd(c<j>) for all c<j> that are coefficients of unfixed integer
  variables. Since the c<j> might not be integers, try and inflate them
  sufficiently that they look like integers (and we'll deflate the gcd
  later).

  2520.0 is used as it is a nice multiple of 2,3,5,7
*/
    if (possibleMultiple&&maximumCost)
    { int increment = 0 ;
      double multiplier = 2520.0 ;
      while (10.0*multiplier*maximumCost < 1.0e8)
        multiplier *= 10.0 ;

      for (iColumn = 0 ; iColumn < numberColumns ; iColumn++)
      { if (upper[iColumn] > lower[iColumn]+1.0e-8)
        { if (isInteger(iColumn)&&objective[iColumn])
          { double value = fabs(objective[iColumn])*multiplier ;
            int nearest = (int) floor(value+0.5) ;
            if (fabs(value-floor(value+0.5)) > 1.0e-8)
            { increment = 0 ;
              break ; }
            else if (!increment)
            { increment = nearest ; }
            else
            { increment = gcd(increment,nearest) ; } } } }
/*
  If the increment beats the current value for objective change, install it.
*/
      if (increment)
      { double value = increment ;
        double cutoff = getDblParam(CbcModel::CbcCutoffIncrement) ;
        value /= multiplier ;
        if (value*0.999 > cutoff)
        { messageHandler()->message(CBC_INTEGERINCREMENT,
                                          messages())
            << value << CoinMessageEol ;
          setDblParam(CbcModel::CbcCutoffIncrement,value*0.999) ; } } }

  return ; 
}


/**
  \todo
  Normally, it looks like we enter here from command dispatch in the main
  routine, after calling the solver for an initial solution
  (CbcModel::initialSolve, which simply calls the solver's initialSolve
  routine.) The first thing we do is call resolve. Presumably there are
  circumstances where this is nontrivial? There's also a call from
  CbcModel::originalModel (tied up with integer presolve), which should be
  checked.

*/

/*
  The overall flow can be divided into three stages:
    * Prep: Check that the lp relaxation remains feasible at the root. If so,
      do all the setup for B&C.
    * Process the root node: Generate cuts, apply heuristics, and in general do
      the best we can to resolve the problem without B&C.
    * Do B&C search until we hit a limit or exhaust the search tree.
  
  Keep in mind that in general there is no node in the search tree that
  corresponds to the active subproblem. The active subproblem is represented
  by the current state of the model,  of the solver, and of the constraint
  system held by the solver.
*/

void CbcModel::branchAndBound(int doStatistics) 

{
/*
  Capture a time stamp before we start.
*/
  dblParam_[CbcStartSeconds] = CoinCpuTime();
  strongInfo_[0]=0;
  strongInfo_[1]=0;
  strongInfo_[2]=0;
  numberStrongIterations_ = 0;
  // original solver (only set if pre-processing)
  OsiSolverInterface * originalSolver=NULL;
  int numberOriginalObjects=numberObjects_;
  CbcObject ** originalObject = NULL;
  // Set up strategies
  if (strategy_) {
    // May do preprocessing
    originalSolver = solver_;
    strategy_->setupOther(*this);
    if (strategy_->preProcessState()) {
      // pre-processing done
      if (strategy_->preProcessState()<0) {
        // infeasible
        handler_->message(CBC_INFEAS,messages_)<< CoinMessageEol ;
        status_ = 0 ;
        secondaryStatus_ = 1;
        originalContinuousObjective_ = COIN_DBL_MAX;
        return ; 
      } else if (numberObjects_&&object_) {
        numberOriginalObjects=numberObjects_;
        // redo sequence
        numberIntegers_=0;
        int numberColumns = getNumCols();
        int nOrig = originalSolver->getNumCols();
        CglPreProcess * process = strategy_->process();
        assert (process);
        const int * originalColumns = process->originalColumns();
        // allow for cliques etc
        nOrig = CoinMax(nOrig,originalColumns[numberColumns-1]+1);
        originalObject = object_;
        // object number or -1
        int * temp = new int[nOrig];
        int iColumn;
        for (iColumn=0;iColumn<nOrig;iColumn++) 
          temp[iColumn]=-1;
        int iObject;
        int nNonInt=0;
        for (iObject=0;iObject<numberOriginalObjects;iObject++) {
          iColumn = originalObject[iObject]->columnNumber();
          if (iColumn<0) {
            nNonInt++;
          } else {
            temp[iColumn]=iObject;
          }
        }
        int numberNewIntegers=0;
        int numberOldIntegers=0;
        int numberOldOther=0;
        for (iColumn=0;iColumn<numberColumns;iColumn++) {
          int jColumn = originalColumns[iColumn];
          if (temp[jColumn]>=0) {
            int iObject= temp[jColumn];
            CbcSimpleInteger * obj =
              dynamic_cast <CbcSimpleInteger *>(originalObject[iObject]) ;
            if (obj) 
              numberOldIntegers++;
            else
              numberOldOther++;
          } else if (isInteger(iColumn)) {
            numberNewIntegers++;
          }
        }
        /*
          Allocate an array to hold the indices of the integer variables.
          Make a large enough array for all objects
        */
        numberObjects_= numberNewIntegers+numberOldIntegers+numberOldOther+nNonInt;
        object_ = new CbcObject * [numberObjects_];
        integerVariable_ = new int [numberNewIntegers+numberOldIntegers];
        /*
          Walk the variables again, filling in the indices and creating objects for
          the integer variables. Initially, the objects hold the index and upper &
          lower bounds.
        */
        numberIntegers_=0;
        for (iColumn=0;iColumn<numberColumns;iColumn++) {
          int jColumn = originalColumns[iColumn];
          if (temp[jColumn]>=0) {
            int iObject= temp[jColumn];
            CbcSimpleInteger * obj =
              dynamic_cast <CbcSimpleInteger *>(originalObject[iObject]) ;
            if (obj) {
              object_[numberIntegers_] = originalObject[iObject]->clone();
              // redo ids etc
              object_[numberIntegers_]->redoSequenceEtc(this,numberColumns,originalColumns);
              integerVariable_[numberIntegers_++]=iColumn;
            }
          } else if (isInteger(iColumn)) {
            object_[numberIntegers_] =
              new CbcSimpleInteger(this,numberIntegers_,iColumn);
            integerVariable_[numberIntegers_++]=iColumn;
          }
        }
        numberObjects_=numberIntegers_;
        // Now append other column stuff
        for (iColumn=0;iColumn<numberColumns;iColumn++) {
          int jColumn = originalColumns[iColumn];
          if (temp[jColumn]>=0) {
            int iObject= temp[jColumn];
            CbcSimpleInteger * obj =
              dynamic_cast <CbcSimpleInteger *>(originalObject[iObject]) ;
            if (!obj) {
              object_[numberObjects_] = originalObject[iObject]->clone();
              // redo ids etc
              object_[numberObjects_]->redoSequenceEtc(this,numberColumns,originalColumns);
              numberObjects_++;
            }
          }
        }
        // now append non column stuff
        for (iObject=0;iObject<numberOriginalObjects;iObject++) {
          iColumn = originalObject[iObject]->columnNumber();
          if (iColumn<0) {
            object_[numberObjects_] = originalObject[iObject]->clone();
            // redo ids etc
            object_[numberObjects_]->redoSequenceEtc(this,numberColumns,originalColumns);
            numberObjects_++;
          }
        }
        delete [] temp;
        if (!numberObjects_)
          handler_->message(CBC_NOINT,messages_) << CoinMessageEol ;
      }
    } else {
      //no preprocessing
      originalSolver=NULL;
    }
    strategy_->setupCutGenerators(*this);
    strategy_->setupHeuristics(*this);
    // Set strategy print level to models
    strategy_->setupPrinting(*this,handler_->logLevel());
  }
  eventHappened_=false;
  CbcEventHandler *eventHandler = getEventHandler() ;
  
  if (!nodeCompare_)
    nodeCompare_=new CbcCompareDefault();;
  // See if hot start wanted
  CbcCompareBase * saveCompare = NULL;
  if (hotstartSolution_) {
    if (strategy_&&strategy_->preProcessState()>0) {
      CglPreProcess * process = strategy_->process();
      assert (process);
      int n = solver_->getNumCols();
      const int * originalColumns = process->originalColumns();
      // columns should be in order ... but
      double * tempS = new double[n];
      for (int i=0;i<n;i++) {
        int iColumn = originalColumns[i];
        tempS[i]=hotstartSolution_[iColumn];
      }
      delete [] hotstartSolution_;
      hotstartSolution_=tempS;
      if (hotstartPriorities_) {
        int * tempP = new int [n];
        for (int i=0;i<n;i++) {
          int iColumn = originalColumns[i];
          tempP[i]=hotstartPriorities_[iColumn];
        }
        delete [] hotstartPriorities_;
        hotstartPriorities_=tempP;
      }
    }
    saveCompare = nodeCompare_;
    // depth first
    nodeCompare_ = new CbcCompareDepth();
  }
  if (!problemFeasibility_)
    problemFeasibility_=new CbcFeasibilityBase();
# ifdef CBC_DEBUG
  std::string problemName ;
  solver_->getStrParam(OsiProbName,problemName) ;
  printf("Problem name - %s\n",problemName.c_str()) ;
  solver_->setHintParam(OsiDoReducePrint,false,OsiHintDo,0) ;
# endif
/*
  Assume we're done, and see if we're proven wrong.
*/
  status_ = 0 ;
  secondaryStatus_ = 0;
  phase_=0;
/*
  Scan the variables, noting the integer variables. Create an
  CbcSimpleInteger object for each integer variable.
*/
  findIntegers(false) ;
  // If dynamic pseudo costs then do
  if (numberBeforeTrust_)
    convertToDynamic();
  // Set up char array to say if integer
  delete [] integerInfo_;
  {
    int n = solver_->getNumCols();
    integerInfo_ = new char [n];
    for (int i=0;i<n;i++) {
      if (solver_->isInteger(i))
        integerInfo_[i]=1;
      else
        integerInfo_[i]=0;
    }
  }
    
/*
  Ensure that objects on the lists of CbcObjects, heuristics, and cut
  generators attached to this model all refer to this model.
*/
  synchronizeModel() ;
  assert (!solverCharacteristics_);
  OsiBabSolver * solverCharacteristics = dynamic_cast<OsiBabSolver *> (solver_->getAuxiliaryInfo());
  if (solverCharacteristics) {
    solverCharacteristics_ = solverCharacteristics;
  } else {
    // replace in solver
    OsiBabSolver defaultC;
    solver_->setAuxiliaryInfo(&defaultC);
    solverCharacteristics_ = dynamic_cast<OsiBabSolver *> (solver_->getAuxiliaryInfo());
  }
  solverCharacteristics_->setSolver(solver_);
  // Set so we can tell we are in initial phase in resolve
  continuousObjective_ = -COIN_DBL_MAX ;
/*
  Solve the relaxation.

  Apparently there are circumstances where this will be non-trivial --- i.e.,
  we've done something since initialSolve that's trashed the solution to the
  continuous relaxation.
*/
  bool feasible;
  // If NLP then we assume already solved outside branchAndbound
  if (!solverCharacteristics_->solverType()) {
    feasible=resolve(NULL,0) != 0 ;
  } else {
    // pick up given status
    feasible = (solver_->isProvenOptimal() &&
                !solver_->isDualObjectiveLimitReached()) ;
  }
  if (problemFeasibility_->feasible(this,0)<0) {
    feasible=false; // pretend infeasible
  }
/*
  If the linear relaxation of the root is infeasible, bail out now. Otherwise,
  continue with processing the root node.
*/
  if (!feasible)
  { handler_->message(CBC_INFEAS,messages_)<< CoinMessageEol ;
    status_ = 0 ;
    secondaryStatus_ = 1;
    originalContinuousObjective_ = COIN_DBL_MAX;
    solverCharacteristics_ = NULL;
    return ; }
  // Save objective (just so user can access it)
  originalContinuousObjective_ = solver_->getObjValue();
  bestPossibleObjective_=originalContinuousObjective_;
  sumChangeObjective1_=0.0;
  sumChangeObjective2_=0.0;
/*
  OsiRowCutDebugger knows an optimal answer for a subset of MIP problems.
  Assuming it recognises the problem, when called upon it will check a cut to
  see if it cuts off the optimal answer.
*/
  // If debugger exists set specialOptions_ bit
  if (solver_->getRowCutDebuggerAlways())
    specialOptions_ |= 1;

# ifdef CBC_DEBUG
  if ((specialOptions_&1)==0)
    solver_->activateRowCutDebugger(problemName.c_str()) ;
  if (solver_->getRowCutDebuggerAlways())
    specialOptions_ |= 1;
# endif

/*
  Begin setup to process a feasible root node.
*/
  bestObjective_ = CoinMin(bestObjective_,1.0e50) ;
  numberSolutions_ = 0 ;
  stateOfSearch_=0;
  numberHeuristicSolutions_ = 0 ;
  // Everything is minimization
  { 
    // needed to sync cutoffs
    double value ;
    solver_->getDblParam(OsiDualObjectiveLimit,value) ;
    dblParam_[CbcCurrentCutoff]= value * solver_->getObjSense();
  }
  double cutoff=getCutoff() ;
  double direction = solver_->getObjSense() ;
  dblParam_[CbcOptimizationDirection]=direction;
  if (cutoff < 1.0e20&&direction<0.0)
    messageHandler()->message(CBC_CUTOFF_WARNING1,
                                    messages())
                                      << cutoff << -cutoff << CoinMessageEol ;
  if (cutoff > bestObjective_)
    cutoff = bestObjective_ ;
  setCutoff(cutoff) ;
/*
  We probably already have a current solution, but just in case ...
*/
  int numberColumns = getNumCols() ;
  if (!currentSolution_)
    currentSolution_ = new double[numberColumns] ;
  testSolution_ = currentSolution_;
  /* Tell solver we are in Branch and Cut
     Could use last parameter for subtle differences */
  solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,NULL) ;
/*
  Create a copy of the solver, thus capturing the original (root node)
  constraint system (aka the continuous system).
*/
  continuousSolver_ = solver_->clone() ;
#ifdef COIN_HAS_CLP
  if ((specialOptions_&32)==0) {
    OsiClpSolverInterface * clpSolver 
      = dynamic_cast<OsiClpSolverInterface *> (solver_);
    if (clpSolver) {
      ClpSimplex * clpSimplex = clpSolver->getModelPtr();
      // take off names
      clpSimplex->dropNames();
    }
  }
#endif

  numberRowsAtContinuous_ = getNumRows() ;
/*
  Check the objective to see if we can deduce a nontrivial increment. If
  it's better than the current value for CbcCutoffIncrement, it'll be
  installed.
*/
  if(solverCharacteristics_->reducedCostsAccurate())
    analyzeObjective() ;
/*
  Set up for cut generation. addedCuts_ holds the cuts which are relevant for
  the active subproblem. whichGenerator will be used to record the generator
  that produced a given cut.
*/
  maximumWhich_ = 1000 ;
  delete [] whichGenerator_;
  whichGenerator_ = new int[maximumWhich_] ;
  memset(whichGenerator_,0,maximumWhich_*sizeof(int));
  int currentNumberCuts = 0 ;
  maximumNumberCuts_ = 0 ;
  currentNumberCuts_ = 0 ;
  delete [] addedCuts_ ;
  addedCuts_ = NULL ;
/*
  Set up an empty heap and associated data structures to hold the live set
  (problems which require further exploration).
*/
  tree_->setComparison(*nodeCompare_) ;
/*
  Used to record the path from a node to the root of the search tree, so that
  we can then traverse from the root to the node when restoring a subproblem.
*/
  maximumDepth_ = 10 ;
  delete [] walkback_ ;
  walkback_ = new CbcNodeInfo * [maximumDepth_] ;
/*
  Used to generate bound edits for CbcPartialNodeInfo.
*/
  double * lowerBefore = new double [numberColumns] ;
  double * upperBefore = new double [numberColumns] ;
/*
  
  Generate cuts at the root node and reoptimise. solveWithCuts does the heavy
  lifting. It will iterate a generate/reoptimise loop (including reduced cost
  fixing) until no cuts are generated, the change in objective falls off,  or
  the limit on the number of rounds of cut generation is exceeded.

  At the end of all this, any cuts will be recorded in cuts and also
  installed in the solver's constraint system. We'll have reoptimised, and
  removed any slack cuts (numberOldActiveCuts_ and numberNewCuts_ have been
  adjusted accordingly).

  Tell cut generators they can be a bit more aggressive at root node

  TODO: Why don't we make a copy of the solution after solveWithCuts?
  TODO: If numberUnsatisfied == 0, don't we have a solution?
*/
  phase_=1;
  int iCutGenerator;
  for (iCutGenerator = 0;iCutGenerator<numberCutGenerators_;iCutGenerator++) {
    CglCutGenerator * generator = generator_[iCutGenerator]->generator();
    generator->setAggressiveness(generator->getAggressiveness()+100);
  }
  OsiCuts cuts ;
  int anyAction = -1 ;
  numberOldActiveCuts_ = 0 ;
  numberNewCuts_ = 0 ;
  // Array to mark solution
  delete [] usedInSolution_;
  usedInSolution_ = new int[numberColumns];
  CoinZeroN(usedInSolution_,numberColumns);
/*
  For printing totals and for CbcNode (numberNodes_)
*/
  numberIterations_ = 0 ;
  numberNodes_ = 0 ;
  numberNodes2_ = 0 ;
  maximumStatistics_=0;
  statistics_ = NULL;
  // Do on switch
  if (doStatistics) {
    maximumStatistics_=10000;
    statistics_ = new CbcStatistics * [maximumStatistics_];
    memset(statistics_,0,maximumStatistics_*sizeof(CbcStatistics *));
  }

  { int iObject ;
    int preferredWay ;
    int numberUnsatisfied = 0 ;
    memcpy(currentSolution_,solver_->getColSolution(),
           numberColumns*sizeof(double)) ;

    for (iObject = 0 ; iObject < numberObjects_ ; iObject++)
    { double infeasibility =
          object_[iObject]->infeasibility(preferredWay) ;
      if (infeasibility ) numberUnsatisfied++ ; }
    // replace solverType 
    if(solverCharacteristics_->tryCuts())  {
      if (numberUnsatisfied)   {
        feasible = solveWithCuts(cuts,maximumCutPassesAtRoot_,
                                 NULL);
      } else if (solverCharacteristics_->solutionAddsCuts()) {
        // may generate cuts and turn the solution
        //to an infeasible one
        feasible = solveWithCuts(cuts, 1,
                                 NULL);
#if 0
        currentNumberCuts_ = cuts.sizeRowCuts();
        if (currentNumberCuts_ >= maximumNumberCuts_) {
          maximumNumberCuts_ = currentNumberCuts;
          delete [] addedCuts_;
          addedCuts_ = new CbcCountRowCut * [maximumNumberCuts_];
        }
#endif
      }
    }
    // check extra info on feasibility
    if (!solverCharacteristics_->mipFeasible())
      feasible = false;
  }
  // make cut generators less aggressive
  for (iCutGenerator = 0;iCutGenerator<numberCutGenerators_;iCutGenerator++) {
    CglCutGenerator * generator = generator_[iCutGenerator]->generator();
    generator->setAggressiveness(generator->getAggressiveness()-100);
  }
  currentNumberCuts_ = numberNewCuts_ ;
/*
  We've taken the continuous relaxation as far as we can. Time to branch.
  The first order of business is to actually create a node. chooseBranch
  currently uses strong branching to evaluate branch object candidates,
  unless forced back to simple branching. If chooseBranch concludes that a
  branching candidate is monotone (anyAction == -1) or infeasible (anyAction
  == -2) when forced to integer values, it returns here immediately.

  Monotone variables trigger a call to resolve(). If the problem remains
  feasible, try again to choose a branching variable. At the end of the loop,
  resolved == true indicates that some variables were fixed.

  Loss of feasibility will result in the deletion of newNode.
*/

  bool resolved = false ;
  CbcNode *newNode = NULL ;
  numberFixedAtRoot_=0;
  numberFixedNow_=0;
  int numberIterationsAtContinuous = numberIterations_;
  //solverCharacteristics_->setSolver(solver_);
  if (feasible) {
    newNode = new CbcNode ;
    // Set objective value (not so obvious if NLP etc)
    setObjectiveValue(newNode,NULL);
    anyAction = -1 ;
    // To make depth available we may need a fake node
    CbcNode fakeNode;
    if (!currentNode_) {
      // Not true if sub trees assert (!numberNodes_);
      currentNode_=&fakeNode;
    }
    phase_=3;
    // only allow 1000 passes
    int numberPassesLeft=1000;
    // This is first crude step
    if (numberAnalyzeIterations_) {
      delete [] analyzeResults_;
      analyzeResults_ = new double [4*numberIntegers_];
      numberFixedAtRoot_=newNode->analyze(this,analyzeResults_);
      if (numberFixedAtRoot_>0) {
        printf("%d fixed by analysis\n",numberFixedAtRoot_);
        setPointers(solver_);
        numberFixedNow_ = numberFixedAtRoot_;
      } else if (numberFixedAtRoot_<0) {
        printf("analysis found to be infeasible\n");
        anyAction=-2;
        delete newNode ;
        newNode = NULL ;
        feasible = false ;
      }
    }
    while (anyAction == -1)
    {
      // Set objective value (not so obvious if NLP etc)
      setObjectiveValue(newNode,NULL);
      if (numberBeforeTrust_==0 ) {
        anyAction = newNode->chooseBranch(this,NULL,numberPassesLeft) ;
      } else {
        OsiSolverBranch * branches=NULL;
        anyAction = newNode->chooseDynamicBranch(this,NULL,branches,numberPassesLeft) ;
        if (anyAction==-3) 
          anyAction = newNode->chooseBranch(this,NULL,numberPassesLeft) ; // dynamic did nothing
      }
      numberPassesLeft--;
      if (anyAction == -1)
      { feasible = resolve(NULL,10) != 0 ;
      if (problemFeasibility_->feasible(this,0)<0) {
        feasible=false; // pretend infeasible
      }
      reducedCostFix() ;
      resolved = true ;
#       ifdef CBC_DEBUG
      printf("Resolve (root) as something fixed, Obj value %g %d rows\n",
             solver_->getObjValue(),
             solver_->getNumRows()) ;
#       endif
      if (!feasible) anyAction = -2 ; }
      if (anyAction == -2||newNode->objectiveValue() >= cutoff)
      { delete newNode ;
        newNode = NULL ;
        feasible = false ; } } }
/*
  At this point, the root subproblem is infeasible or fathomed by bound
  (newNode == NULL), or we're live with an objective value that satisfies the
  current objective cutoff.
*/
  assert (!newNode || newNode->objectiveValue() <= cutoff) ;
  // Save address of root node as we don't want to delete it
  CbcNode * rootNode = newNode;
/*
  The common case is that the lp relaxation is feasible but doesn't satisfy
  integrality (i.e., newNode->variable() >= 0, indicating we've been able to
  select a branching variable). Remove any cuts that have gone slack due to
  forcing monotone variables. Then tack on an CbcFullNodeInfo object and full
  basis (via createInfo()) and stash the new cuts in the nodeInfo (via
  addCuts()). If, by some miracle, we have an integral solution at the root
  (newNode->variable() < 0), takeOffCuts() will ensure that the solver holds
  a valid solution for use by setBestSolution().
*/
  CoinWarmStartBasis *lastws = 0 ;
  if (feasible && newNode->variable() >= 0)
  { if (resolved)
    { bool needValidSolution = (newNode->variable() < 0) ;
      takeOffCuts(cuts,needValidSolution,NULL) ;
#     ifdef CHECK_CUT_COUNTS
      { printf("Number of rows after chooseBranch fix (root)"
               "(active only) %d\n",
                numberRowsAtContinuous_+numberNewCuts_+numberOldActiveCuts_) ;
        const CoinWarmStartBasis* debugws =
          dynamic_cast <const CoinWarmStartBasis*>(solver_->getWarmStart()) ;
        debugws->print() ;
        delete debugws ; }
#     endif
    }
    newNode->createInfo(this,NULL,NULL,NULL,NULL,0,0) ;
    newNode->nodeInfo()->addCuts(cuts,
                                 newNode->numberBranches(),whichGenerator_) ;
    if (lastws) delete lastws ;
    lastws = dynamic_cast<CoinWarmStartBasis*>(solver_->getWarmStart()) ;
  }
/*
  Continuous data to be used later
*/
  continuousObjective_ = solver_->getObjValue()*solver_->getObjSense();
  continuousInfeasibilities_ = 0 ;
  if (newNode)
  { continuousObjective_ = newNode->objectiveValue() ;
    delete [] continuousSolution_;
    continuousSolution_ = CoinCopyOfArray(solver_->getColSolution(),
                                             numberColumns);
    continuousInfeasibilities_ = newNode->numberUnsatisfied() ; }
/*
  Bound may have changed so reset in objects
*/
  { int i ;
    for (i = 0;i < numberObjects_;i++)
      object_[i]->resetBounds() ; }
  stoppedOnGap_ = false ;
/*
  Feasible? Then we should have either a live node prepped for future
  expansion (indicated by variable() >= 0), or (miracle of miracles) an
  integral solution at the root node.

  initializeInfo sets the reference counts in the nodeInfo object.  Since
  this node is still live, push it onto the heap that holds the live set.
*/
  double bestValue = 0.0 ;
  if (newNode) {
    bestValue = newNode->objectiveValue();
    if (newNode->variable() >= 0) {
      newNode->initializeInfo() ;
      tree_->push(newNode) ;
      if (statistics_) {
        if (numberNodes2_==maximumStatistics_) {
          maximumStatistics_ = 2*maximumStatistics_;
          CbcStatistics ** temp = new CbcStatistics * [maximumStatistics_];
          memset(temp,0,maximumStatistics_*sizeof(CbcStatistics *));
          memcpy(temp,statistics_,numberNodes2_*sizeof(CbcStatistics *));
          delete [] statistics_;
          statistics_=temp;
        }
        assert (!statistics_[numberNodes2_]);
        statistics_[numberNodes2_]=new CbcStatistics(newNode);
      }
      numberNodes2_++;
#     ifdef CHECK_NODE
      printf("Node %x on tree\n",newNode) ;
#     endif
    } else {
      // continuous is integer
      double objectiveValue = newNode->objectiveValue();
      setBestSolution(CBC_SOLUTION,objectiveValue,
                      solver_->getColSolution()) ;
      delete newNode ;
      newNode = NULL ;
    }
  }

  if (printFrequency_ <= 0) {
    printFrequency_ = 1000 ;
    if (getNumCols() > 2000)
      printFrequency_ = 100 ;
  }
  /*
    It is possible that strong branching fixes one variable and then the code goes round
    again and again.  This can take too long.  So we need to warn user - just once.
  */
  numberLongStrong_=0;
/*
  At last, the actual branch-and-cut search loop, which will iterate until
  the live set is empty or we hit some limit (integrality gap, time, node
  count, etc.). The overall flow is to rebuild a subproblem, reoptimise using
  solveWithCuts(), choose a branching pattern with chooseBranch(), and finally
  add the node to the live set.

  The first action is to winnow the live set to remove nodes which are worse
  than the current objective cutoff.
*/
  while (!tree_->empty()) {
#ifdef BONMIN
    assert(!solverCharacteristics_->solutionAddsCuts() || solverCharacteristics_->mipFeasible());
#endif
    if (cutoff > getCutoff()) {
      double newCutoff = getCutoff();
      if (analyzeResults_) {
        // see if we could fix any (more)
        int n=0;
        double * newLower = analyzeResults_;
        double * objLower = newLower+numberIntegers_;
        double * newUpper = objLower+numberIntegers_;
        double * objUpper = newUpper+numberIntegers_;
        for (int i=0;i<numberIntegers_;i++) {
          if (objLower[i]>newCutoff) {
            n++;
            if (objUpper[i]>newCutoff) {
              newCutoff = -COIN_DBL_MAX;
              break;
            }
          } else if (objUpper[i]>newCutoff) {
            n++;
          }
        }
        if (newCutoff==-COIN_DBL_MAX) {
          printf("Root analysis says finished\n");
        } else if (n>numberFixedNow_) {
          printf("%d more fixed by analysis - now %d\n",n-numberFixedNow_,n);
          numberFixedNow_=n;
        }
      }
      if (eventHandler) {
        if (!eventHandler->event(CbcEventHandler::solution)) {
          eventHappened_=true; // exit
        }
      }
      // Do from deepest
      tree_->cleanTree(this, newCutoff,bestPossibleObjective_) ;
      nodeCompare_->newSolution(this) ;
      nodeCompare_->newSolution(this,continuousObjective_,
                                continuousInfeasibilities_) ;
      tree_->setComparison(*nodeCompare_) ;
      if (tree_->empty())
        break; // finished
    }
    cutoff = getCutoff() ;
/*
  Periodic activities: Opportunities to
    + tweak the nodeCompare criteria,
    + check if we've closed the integrality gap enough to quit, 
    + print a summary line to let the user know we're working
*/
    if ((numberNodes_%1000) == 0) {
      bool redoTree=nodeCompare_->every1000Nodes(this, numberNodes_) ;
      // redo tree if wanted
      if (redoTree)
        tree_->setComparison(*nodeCompare_) ;
    }
    if (saveCompare&&!hotstartSolution_) {
      // hotstart switched off
      delete nodeCompare_; // off depth first
      nodeCompare_=saveCompare;
      saveCompare=NULL;
      // redo tree
      tree_->setComparison(*nodeCompare_) ;
    }
    if ((numberNodes_%printFrequency_) == 0) {
      int j ;
      int nNodes = tree_->size() ;
      bestPossibleObjective_ = 1.0e100 ;
      for (j = 0;j < nNodes;j++) {
        CbcNode * node = tree_->nodePointer(j) ;
        if (node&&node->objectiveValue() < bestPossibleObjective_)
          bestPossibleObjective_ = node->objectiveValue() ;
      }
      messageHandler()->message(CBC_STATUS,messages())
        << numberNodes_<< nNodes<< bestObjective_<< bestPossibleObjective_
        <<getCurrentSeconds()
        << CoinMessageEol ;
      if (!eventHandler->event(CbcEventHandler::treeStatus)) {
        eventHappened_=true; // exit
      }
    }
    // If no solution but many nodes - signal change in strategy
    if (numberNodes_>2*numberObjects_+1000&&stateOfSearch_!=2)
      stateOfSearch_=3;
    // See if can stop on gap
    double testGap = CoinMax(dblParam_[CbcAllowableGap],
                             CoinMax(fabs(bestObjective_),fabs(bestPossibleObjective_))
                             *dblParam_[CbcAllowableFractionGap]);
    if (bestObjective_-bestPossibleObjective_ < testGap && getCutoffIncrement()>=0.0) {
      stoppedOnGap_ = true ;
    }

#   ifdef CHECK_NODE_FULL
    verifyTreeNodes(tree_,*this) ;
#   endif
#   ifdef CHECK_CUT_COUNTS
    verifyCutCounts(tree_,*this) ;
#   endif

/*
  Now we come to the meat of the loop. To create the active subproblem, we'll
  pop the most promising node in the live set, rebuild the subproblem it
  represents, and then execute the current arm of the branch to create the
  active subproblem.
*/
    CbcNode *node = tree_->bestNode(cutoff) ;
    // Possible one on tree worse than cutoff
    if (!node)
      continue;
    currentNode_=node; // so can be accessed elsewhere
#ifdef CBC_DEBUG
    printf("%d unsat, way %d, obj %g est %g\n",
           node->numberUnsatisfied(),node->way(),node->objectiveValue(),
           node->guessedObjectiveValue());
#endif
    // Save clone in branching decision
    if(branchingMethod_)
      branchingMethod_->saveBranchingObject(node->modifiableBranchingObject());
    bool nodeOnTree=false; // Node has been popped
    // Say not on optimal path
    bool onOptimalPath=false;
#   ifdef CHECK_NODE
/*
  WARNING: The use of integerVariable_[*] here will break as soon as the
           branching object is something other than an integer variable.
           This needs some thought.
*/
    printf("Node %x popped from tree - %d left, %d count\n",node,
           node->nodeInfo()->numberBranchesLeft(),
           node->nodeInfo()->numberPointingToThis()) ;
    printf("\tdepth = %d, z =  %g, unsat = %d, var = %d.\n",
           node->depth(),node->objectiveValue(),
           node->numberUnsatisfied(),
           integerVariable_[node->variable()]) ;
#   endif

/*
  Rebuild the subproblem for this node:  Call addCuts() to adjust the model
  to recreate the subproblem for this node (set proper variable bounds, add
  cuts, create a basis).  This may result in the problem being fathomed by
  bound or infeasibility. Returns 1 if node is fathomed.
  Execute the current arm of the branch: If the problem survives, save the
  resulting variable bounds and call branch() to modify variable bounds
  according to the current arm of the branching object. If we're processing
  the final arm of the branching object, flag the node for removal from the
  live set.
*/
    CbcNodeInfo * nodeInfo = node->nodeInfo() ;
    newNode = NULL ;
    if (!addCuts(node,lastws,numberFixedNow_>numberFixedAtRoot_))
    { int i ;
      const double * lower = getColLower() ;
      const double * upper = getColUpper() ;
      for (i = 0 ; i < numberColumns ; i++)
      { lowerBefore[i]= lower[i] ;
        upperBefore[i]= upper[i] ; }
      bool deleteNode ;
      if (node->branch())
      { 
        // set nodenumber correctly
        node->nodeInfo()->setNodeNumber(numberNodes2_);
        tree_->push(node) ;
        if (statistics_) {
          if (numberNodes2_==maximumStatistics_) {
            maximumStatistics_ = 2*maximumStatistics_;
            CbcStatistics ** temp = new CbcStatistics * [maximumStatistics_];
            memset(temp,0,maximumStatistics_*sizeof(CbcStatistics *));
            memcpy(temp,statistics_,numberNodes2_*sizeof(CbcStatistics *));
            delete [] statistics_;
            statistics_=temp;
          }
          assert (!statistics_[numberNodes2_]);
          statistics_[numberNodes2_]=new CbcStatistics(node);
        }
        numberNodes2_++;
        nodeOnTree=true; // back on tree
        deleteNode = false ;
#       ifdef CHECK_NODE
        printf("Node %x pushed back on tree - %d left, %d count\n",node,
               nodeInfo->numberBranchesLeft(),
               nodeInfo->numberPointingToThis()) ;
#       endif
      }
      else
      { deleteNode = true ;
      if (!nodeInfo->numberBranchesLeft())
        nodeInfo->allBranchesGone(); // can clean up
      }

      if ((specialOptions_&1)!=0) {
        /*
          This doesn't work as intended --- getRowCutDebugger will return null
          unless the current feasible solution region includes the optimal solution
          that RowCutDebugger knows. There's no way to tell inactive from off the
          optimal path.
        */
        const OsiRowCutDebugger *debugger = solver_->getRowCutDebugger() ;
        if (debugger) {
          onOptimalPath=true;
          printf("On optimal path\n") ;
        }
      }
/*
  Reoptimize, possibly generating cuts and/or using heuristics to find
  solutions.  Cut reference counts are unaffected unless we lose feasibility,
  in which case solveWithCuts() will make the adjustment.
*/
      phase_=2;
      cuts = OsiCuts() ;
      currentNumberCuts = solver_->getNumRows()-numberRowsAtContinuous_ ;
      int saveNumber = numberIterations_;
      if(solverCharacteristics_->solutionAddsCuts()) {
        int returnCode=resolve(node ? node->nodeInfo() : NULL,1);
        feasible = returnCode != 0;
        if (feasible) {
          int iObject ;
          int preferredWay ;
          int numberUnsatisfied = 0 ;
          memcpy(currentSolution_,solver_->getColSolution(),
                 numberColumns*sizeof(double)) ;
          
          for (iObject = 0 ; iObject < numberObjects_ ; iObject++) {
            double infeasibility =
              object_[iObject]->infeasibility(preferredWay) ;
            if (infeasibility ) numberUnsatisfied++ ;
          }
          if (returnCode>0) {
            if (numberUnsatisfied)   {
              feasible = solveWithCuts(cuts,maximumCutPasses_,node);
            } else {
              // may generate cuts and turn the solution
              //to an infeasible one
              feasible = solveWithCuts(cuts, 1,
                                       node);
#if 0
              currentNumberCuts_ = cuts.sizeRowCuts();
              if (currentNumberCuts_ >= maximumNumberCuts_) {
                maximumNumberCuts_ = currentNumberCuts;
                delete [] addedCuts_;
                addedCuts_ = new CbcCountRowCut * [maximumNumberCuts_];
              }
#endif
            }
          }
          // check extra info on feasibility
          if (!solverCharacteristics_->mipFeasible())
            feasible = false;
        }
      } else {
        // normal
        feasible = solveWithCuts(cuts,maximumCutPasses_,node);
      }
      if ((specialOptions_&1)!=0&&onOptimalPath) {
        assert (solver_->getRowCutDebugger()) ;
      }
      if (statistics_) {
        assert (numberNodes2_);
        assert (statistics_[numberNodes2_-1]);
        assert (statistics_[numberNodes2_-1]->node()==numberNodes2_-1);
        statistics_[numberNodes2_-1]->endOfBranch(numberIterations_-saveNumber,
                                               feasible ? solver_->getObjValue()
                                               : COIN_DBL_MAX);
      }
/*
  Check for abort on limits: node count, solution count, time, integrality gap.
*/
      double totalTime = CoinCpuTime()-dblParam_[CbcStartSeconds] ;
      if (numberNodes_ < intParam_[CbcMaxNumNode] &&
          numberSolutions_ < intParam_[CbcMaxNumSol] &&
          totalTime < dblParam_[CbcMaximumSeconds] &&
          !stoppedOnGap_&&!eventHappened_) 
      {
/*
  Are we still feasible? If so, create a node and do the work to attach a
  branching object, reoptimising as needed if chooseBranch() identifies
  monotone objects.

  Finally, attach a partial nodeInfo object and store away any cuts that we
  created back in solveWithCuts. addCuts() will initialise the reference
  counts for these new cuts.

  TODO: (lh) I'm confused. We create a nodeInfo without checking whether we
        have a solution or not. Then we use numberUnsatisfied() to decide
        whether to stash the cuts and bump reference counts. Other places we
        use variable() (i.e., presence of a branching variable). Equivalent?
*/
        if (onOptimalPath) {
          if (!feasible) {
            printf("infeas2\n");
            solver_->writeMps("infeas");
            CoinWarmStartBasis *slack =
              dynamic_cast<CoinWarmStartBasis *>(solver_->getEmptyWarmStart()) ;
            solver_->setWarmStart(slack);
            delete slack ;
            solver_->setHintParam(OsiDoReducePrint,false,OsiHintDo,0) ;
            solver_->initialSolve();
            assert (!solver_->isProvenOptimal());
          }
          assert (feasible);
        }
        bool checkingNode=false;
        if (feasible) {
          newNode = new CbcNode ;
          // Set objective value (not so obvious if NLP etc)
          setObjectiveValue(newNode,node);
          anyAction =-1 ;
          resolved = false ;
          if (newNode->objectiveValue() >= getCutoff()) 
            anyAction=-2;
          // only allow twenty passes
          int numberPassesLeft=20;
          checkingNode=true;
          while (anyAction == -1)
          { 
            // Set objective value (not so obvious if NLP etc)
            setObjectiveValue(newNode,node);
            if (numberBeforeTrust_==0 ) {
              anyAction = newNode->chooseBranch(this,node,numberPassesLeft) ;
            } else {
              OsiSolverBranch * branches=NULL;
              anyAction = newNode->chooseDynamicBranch(this,node,branches,numberPassesLeft) ;
              if (anyAction==-3) 
                anyAction = newNode->chooseBranch(this,node,numberPassesLeft) ; // dynamic did nothing
            }
            if (solverCharacteristics_ && 
                solverCharacteristics_->solutionAddsCuts() && // we are in some OA based bab
                feasible && (newNode->numberUnsatisfied()==0) //solution has become integer feasible during strong branching
                )
              { 
                //in the present case we need to check here integer infeasibility if the node is not fathomed we will have to do the loop
                // again
                std::cout<<solver_<<std::endl;
                solver_->resolve();
                double objval = solver_->getObjValue();
                setBestSolution(CBC_SOLUTION, objval,
                                solver_->getColSolution()) ;
                lastHeuristic_ = NULL;
                int easy=2;
                if (!solverCharacteristics_->mipFeasible())//did we prove that the node could be pruned?
                  feasible = false;
                // Reset the bound now
                solverCharacteristics_->setMipBound(-COIN_DBL_MAX);
                
                
                //numberPassesLeft++;
                solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,&easy) ;
                feasible &= resolve(node ? node->nodeInfo() : NULL,11) != 0 ;
                solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,NULL) ;
                resolved = true ;
                if (problemFeasibility_->feasible(this,0)<0) {
                  feasible=false; // pretend infeasible
                }
                if(feasible)
                  anyAction = -1;
                else
                  anyAction = -2;
              }
/*
  Yep, false positives for sure. And no easy way to distinguish honest
  infeasibility from `found a solution and tightened objective target.'

            if (onOptimalPath)
              assert (anyAction!=-2); // can be useful but gives false positives on strong
*/
            numberPassesLeft--;
            if (numberPassesLeft<=-1) {
              if (!numberLongStrong_)
                messageHandler()->message(CBC_WARNING_STRONG,
                                          messages()) << CoinMessageEol ;
              numberLongStrong_++;
            }
            if (anyAction == -1)
            {
              // can do quick optimality check
              int easy=2;
              solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,&easy) ;
              feasible = resolve(node ? node->nodeInfo() : NULL,11) != 0 ;
              solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,NULL) ;
              resolved = true ;
              if (problemFeasibility_->feasible(this,0)<0) {
                feasible=false; // pretend infeasible
              }
              if (feasible)
              { 
                // Set objective value (not so obvious if NLP etc)
                setObjectiveValue(newNode,node);
                reducedCostFix() ;
                if (newNode->objectiveValue() >= getCutoff()) 
                  anyAction=-2;
              }
              else
              { anyAction = -2 ; } } }
          if (anyAction >= 0)
          { if (resolved)
            { bool needValidSolution = (newNode->variable() < 0) ;
              takeOffCuts(cuts,needValidSolution,NULL) ; 
#             ifdef CHECK_CUT_COUNTS
              { printf("Number of rows after chooseBranch fix (node)"
                       "(active only) %d\n",
                        numberRowsAtContinuous_+numberNewCuts_+
                        numberOldActiveCuts_) ;
                const CoinWarmStartBasis* debugws =
                  dynamic_cast<const CoinWarmStartBasis*>
                    (solver_->getWarmStart()) ;
                debugws->print() ;
                delete debugws ; }
#             endif
            }
            newNode->createInfo(this,node,lastws,lowerBefore,upperBefore,
                                numberOldActiveCuts_,numberNewCuts_) ;
            if (newNode->numberUnsatisfied()) {
              newNode->initializeInfo() ;
              newNode->nodeInfo()->addCuts(cuts,newNode->numberBranches(),
                                           whichGenerator_) ; } } }
        else {
          anyAction = -2 ; 
          // Reset bound anyway (no harm if not odd)
          solverCharacteristics_->setMipBound(-COIN_DBL_MAX);
        }
        // May have slipped through i.e. anyAction == 0 and objective above cutoff
        // I think this will screw up cut reference counts if executed.
        // We executed addCuts just above. (lh)
        if ( anyAction >=0 ) {
          assert (newNode);
          if (newNode->objectiveValue() >= getCutoff()) 
            anyAction = -2; // say bad after all
        }
/*
  If we end up infeasible, we can delete the new node immediately. Since this
  node won't be needing the cuts we collected, decrement the reference counts.
  If we are feasible, then we'll be placing this node into the live set, so
  increment the reference count in the current (parent) nodeInfo.
*/
        if (anyAction == -2)
        { delete newNode ;
          newNode = NULL ;
          // say strong doing well
          if (checkingNode)
            setSpecialOptions(specialOptions_|8);
          for (i = 0 ; i < currentNumberCuts_ ; i++)
          { if (addedCuts_[i])
            { if (!addedCuts_[i]->decrement(1))
                delete addedCuts_[i] ; } } }
        else
        { nodeInfo->increment() ;
        if ((numberNodes_%20)==0) {
          // say strong not doing as well
          setSpecialOptions(specialOptions_&~8);
        }
        }
/*
  At this point, there are three possibilities:
    * newNode is live and will require further branching to resolve
      (variable() >= 0). Increment the cut reference counts by
      numberBranches() to allow for use by children of this node, and
      decrement by 1 because we've executed one arm of the branch of our
      parent (consuming one reference). Before we push newNode onto the
      search tree, try for a heuristic solution.
    * We have a solution, in which case newNode is non-null but we have no
      branching variable. Decrement the cut counts and save the solution.
    * The node was found to be infeasible, in which case it's already been
      deleted, and newNode is null.
*/
        if (!eventHandler->event(CbcEventHandler::node)) {
          eventHappened_=true; // exit
        }
        assert (!newNode || newNode->objectiveValue() <= getCutoff()) ;
        if (statistics_) {
          assert (numberNodes2_);
          assert (statistics_[numberNodes2_-1]);
          assert (statistics_[numberNodes2_-1]->node()==numberNodes2_-1);
          if (newNode)
            statistics_[numberNodes2_-1]->updateInfeasibility(newNode->numberUnsatisfied());
          else
            statistics_[numberNodes2_-1]->sayInfeasible();
        }
        if (newNode)
        { if (newNode->variable() >= 0)
          { handler_->message(CBC_BRANCH,messages_)
               << numberNodes_<< newNode->objectiveValue()
               << newNode->numberUnsatisfied()<< newNode->depth()
               << CoinMessageEol ;
            // Increment cut counts (taking off current)
            int numberLeft = newNode->numberBranches() ;
            for (i = 0;i < currentNumberCuts_;i++)
            { if (addedCuts_[i])
              {
#               ifdef CHECK_CUT_COUNTS
                printf("Count on cut %x increased by %d\n",addedCuts_[i],
                        numberLeft-1) ;
#               endif
                addedCuts_[i]->increment(numberLeft-1) ; } }

            double estValue = newNode->guessedObjectiveValue() ;
            int found = -1 ;
            // no - overhead on small problems solver_->resolve() ;     // double check current optimal
            // assert (!solver_->getIterationCount());
            double * newSolution = new double [numberColumns] ;
            double heurValue = getCutoff() ;
            int iHeur ;
            for (iHeur = 0 ; iHeur < numberHeuristics_ ; iHeur++)
            { double saveValue = heurValue ;
              int ifSol = heuristic_[iHeur]->solution(heurValue,newSolution) ;
              if (ifSol > 0) {
                // new solution found
                found = iHeur ;
                incrementUsed(newSolution);
              }
              else
              if (ifSol < 0)    // just returning an estimate
              { estValue = CoinMin(heurValue,estValue) ;
                heurValue = saveValue ; } }
            if (found >= 0) {
              setBestSolution(CBC_ROUNDING,heurValue,newSolution) ;
              lastHeuristic_ = heuristic_[found];
            }
            delete [] newSolution ;
            newNode->setGuessedObjectiveValue(estValue) ;
            tree_->push(newNode) ;
            if (statistics_) {
              if (numberNodes2_==maximumStatistics_) {
                maximumStatistics_ = 2*maximumStatistics_;
                CbcStatistics ** temp = new CbcStatistics * [maximumStatistics_];
                memset(temp,0,maximumStatistics_*sizeof(CbcStatistics *));
                memcpy(temp,statistics_,numberNodes2_*sizeof(CbcStatistics *));
                delete [] statistics_;
                statistics_=temp;
              }
              assert (!statistics_[numberNodes2_]);
              statistics_[numberNodes2_]=new CbcStatistics(newNode);
            }
            numberNodes2_++;
#           ifdef CHECK_NODE
            printf("Node %x pushed on tree c\n",newNode) ;
#           endif
          }
          else
          { 
            if(solverCharacteristics_ && //we may be in a non standard bab
               solverCharacteristics_->solutionAddsCuts()// we are in some kind of OA based bab.
               )
              {
                std::cerr<<"You should never get here"<<std::endl;
                throw CoinError("Nodes should not be fathomed on integer infeasibility in this setting",
                                "branchAndBound","CbcModel") ;
              }
            for (i = 0 ; i < currentNumberCuts_ ; i++)
            { if (addedCuts_[i])
              { if (!addedCuts_[i]->decrement(1))
                  delete addedCuts_[i] ; } }
          double objectiveValue = newNode->objectiveValue();
            setBestSolution(CBC_SOLUTION,objectiveValue,
                            solver_->getColSolution()) ;
            lastHeuristic_ = NULL;
            incrementUsed(solver_->getColSolution());
            //assert(nodeInfo->numberPointingToThis() <= 2) ;
            // avoid accidental pruning, if newNode was final branch arm
            nodeInfo->increment();
            delete newNode ;
            nodeInfo->decrement() ; } }
/*
  This node has been completely expanded and can be removed from the live
  set.
*/
        if (deleteNode) 
          delete node ; }
/*
  End of the non-abort actions. The next block of code is executed if we've
  aborted because we hit one of the limits. Clean up by deleting the live set
  and break out of the node processing loop. Note that on an abort, node may
  have been pushed back onto the tree for further processing, in which case
  it'll be deleted in cleanTree. We need to check.
*/
      else
      { 
        tree_->cleanTree(this,-COIN_DBL_MAX,bestPossibleObjective_) ;
        delete nextRowCut_;
        // We need to get rid of node if is has already been popped from tree
        if (!nodeOnTree&&!stoppedOnGap_&&node!=rootNode)
          delete node;
        if (stoppedOnGap_)
        { messageHandler()->message(CBC_GAP,messages())
            << bestObjective_-bestPossibleObjective_
            << dblParam_[CbcAllowableGap]
            << dblParam_[CbcAllowableFractionGap]*100.0
            << CoinMessageEol ;
        secondaryStatus_ = 2;
          status_ = 0 ; }
        else
        if (isNodeLimitReached())
        { handler_->message(CBC_MAXNODES,messages_) << CoinMessageEol ;
        secondaryStatus_ = 3;
          status_ = 1 ; }
        else
        if (totalTime >= dblParam_[CbcMaximumSeconds])
        { handler_->message(CBC_MAXTIME,messages_) << CoinMessageEol ; 
        secondaryStatus_ = 4;
          status_ = 1 ; }
        else
        if (eventHappened_)
        { handler_->message(CBC_EVENT,messages_) << CoinMessageEol ; 
        secondaryStatus_ = 5;
          status_ = 5 ; }
        else
        { handler_->message(CBC_MAXSOLS,messages_) << CoinMessageEol ;
        secondaryStatus_ = 6;
          status_ = 1 ; }
        break ; }
/*
  Delete cuts to get back to the original system.

  I'm thinking this is redundant --- the call to addCuts that conditions entry
  to this code block also performs this action.
*/
      int numberToDelete = getNumRows()-numberRowsAtContinuous_ ;
      if (numberToDelete)
      { int * delRows = new int[numberToDelete] ;
        int i ;
        for (i = 0 ; i < numberToDelete ; i++)
        { delRows[i] = i+numberRowsAtContinuous_ ; }
        solver_->deleteRows(numberToDelete,delRows) ;
        delete [] delRows ; } }
/*
  This node fathomed when addCuts atttempted to revive it. Toss it.
*/
    else
      { delete node ; } }
/*
  That's it, we've exhausted the search tree, or broken out of the loop because
  we hit some limit on evaluation.

  We may have got an intelligent tree so give it one more chance
*/
  // Tell solver we are not in Branch and Cut
  solver_->setHintParam(OsiDoInBranchAndCut,false,OsiHintDo,NULL) ;
  tree_->endSearch();
  //  If we did any sub trees - did we give up on any?
  if ( numberStoppedSubTrees_)
    status_=1;
  if (!status_) {
    bestPossibleObjective_=bestObjective_;
    handler_->message(CBC_END_GOOD,messages_)
      << bestObjective_ << numberIterations_ << numberNodes_<<getCurrentSeconds()
      << CoinMessageEol ;
  } else {
    handler_->message(CBC_END,messages_)
      << bestObjective_ <<bestPossibleObjective_
      << numberIterations_ << numberNodes_<<getCurrentSeconds()
      << CoinMessageEol ;
  }
  if (numberStrongIterations_)
    handler_->message(CBC_STRONG_STATS,messages_)
      << strongInfo_[0] << numberStrongIterations_ << strongInfo_[2]
      << strongInfo_[1] << CoinMessageEol ;
  if (statistics_) {
    // report in some way
    int * lookup = new int[numberObjects_];
    int i;
    for (i=0;i<numberObjects_;i++) 
      lookup[i]=-1;
    bool goodIds=true;
    for (i=0;i<numberObjects_;i++) {
      int id = object_[i]->id();
      int iColumn = object_[i]->columnNumber();
      if (iColumn<0)
        iColumn = id+numberColumns;
      if(id>=0&&id<numberObjects_) {
        if (lookup[id]==-1) {
          lookup[id]=iColumn;
        } else {
          goodIds=false;
          break;
        }
      } else {
        goodIds=false;
        break;
      }
    }
    if (!goodIds) {
      delete [] lookup;
      lookup=NULL;
    }
    if (doStatistics==3) {
      printf("  node parent depth column   value                    obj      inf\n");
      for ( i=0;i<numberNodes2_;i++) {
        statistics_[i]->print(lookup);
      }
    }
    if (doStatistics>1) {
      // Find last solution
      int k;
      for (k=numberNodes2_-1;k>=0;k--) {
        if (statistics_[k]->endingObjective()!=COIN_DBL_MAX&&
            !statistics_[k]->endingInfeasibility())
          break;
      }
      if (k>=0) {
        int depth=statistics_[k]->depth();
        int * which = new int[depth+1];
        for (i=depth;i>=0;i--) {
          which[i]=k;
          k=statistics_[k]->parentNode();
        }
        printf("  node parent depth column   value                    obj      inf\n");
        for (i=0;i<=depth;i++) {
          statistics_[which[i]]->print(lookup);
        }
        delete [] which;
      }
    }
    // now summary
    int maxDepth=0;
    double averageSolutionDepth=0.0;
    int numberSolutions=0;
    double averageCutoffDepth=0.0;
    double averageSolvedDepth=0.0;
    int numberCutoff=0;
    int numberDown=0;
    int numberFirstDown=0;
    double averageInfDown=0.0;
    double averageObjDown=0.0;
    int numberCutoffDown=0;
    int numberUp=0;
    int numberFirstUp=0;
    double averageInfUp=0.0;
    double averageObjUp=0.0;
    int numberCutoffUp=0;
    double averageNumberIterations1=0.0;
    double averageValue=0.0;
    for ( i=0;i<numberNodes2_;i++) {
      int depth =  statistics_[i]->depth(); 
      int way =  statistics_[i]->way(); 
      double value = statistics_[i]->value(); 
      double startingObjective =  statistics_[i]->startingObjective(); 
      int startingInfeasibility = statistics_[i]->startingInfeasibility(); 
      double endingObjective = statistics_[i]->endingObjective(); 
      int endingInfeasibility = statistics_[i]->endingInfeasibility(); 
      maxDepth = CoinMax(depth,maxDepth);
      // Only for completed
      averageNumberIterations1 += statistics_[i]->numberIterations();
      averageValue += value;
      if (endingObjective!=COIN_DBL_MAX&&!endingInfeasibility) {
        numberSolutions++;
        averageSolutionDepth += depth;
      }
      if (endingObjective==COIN_DBL_MAX) {
        numberCutoff++;
        averageCutoffDepth += depth;
        if (way<0) {
          numberDown++;
          numberCutoffDown++;
          if (way==-1)
            numberFirstDown++;
        } else {
          numberUp++;
          numberCutoffUp++;
          if (way==1)
            numberFirstUp++;
        }
      } else {
        averageSolvedDepth += depth;
        if (way<0) {
          numberDown++;
          averageInfDown += startingInfeasibility-endingInfeasibility;
          averageObjDown += endingObjective-startingObjective;
          if (way==-1)
            numberFirstDown++;
        } else {
          numberUp++;
          averageInfUp += startingInfeasibility-endingInfeasibility;
          averageObjUp += endingObjective-startingObjective;
          if (way==1)
            numberFirstUp++;
        }
      }
    }
    // Now print
    if (numberSolutions)
      averageSolutionDepth /= (double) numberSolutions;
    int numberSolved = numberNodes2_-numberCutoff;
    double averageNumberIterations2=numberIterations_-averageNumberIterations1
      -numberIterationsAtContinuous;
    if(numberCutoff) {
      averageCutoffDepth /= (double) numberCutoff;
      averageNumberIterations2 /= (double) numberCutoff;
    }
    if (numberNodes2_) 
      averageValue /= (double) numberNodes2_;
    if (numberSolved) {
      averageNumberIterations1 /= (double) numberSolved;
      averageSolvedDepth /= (double) numberSolved;
    }
    printf("%d solution(s) were found (by branching) at an average depth of %g\n",
           numberSolutions,averageSolutionDepth);
    printf("average value of variable being branched on was %g\n",
           averageValue);
    printf("%d nodes were cutoff at an average depth of %g with iteration count of %g\n",
           numberCutoff,averageCutoffDepth,averageNumberIterations2);
    printf("%d nodes were solved at an average depth of %g with iteration count of %g\n",
           numberSolved,averageSolvedDepth,averageNumberIterations1);
    if (numberDown) {
      averageInfDown /= (double) numberDown;
      averageObjDown /= (double) numberDown;
    }
    printf("Down %d nodes (%d first, %d second) - %d cutoff, rest decrease numinf %g increase obj %g\n",
           numberDown,numberFirstDown,numberDown-numberFirstDown,numberCutoffDown,
           averageInfDown,averageObjDown);
    if (numberUp) {
      averageInfUp /= (double) numberUp;
      averageObjUp /= (double) numberUp;
    }
    printf("Up %d nodes (%d first, %d second) - %d cutoff, rest decrease numinf %g increase obj %g\n",
           numberUp,numberFirstUp,numberUp-numberFirstUp,numberCutoffUp,
           averageInfUp,averageObjUp);
    for ( i=0;i<numberNodes2_;i++) 
      delete statistics_[i];
    delete [] statistics_;
    statistics_=NULL;
    maximumStatistics_=0;
    delete [] lookup;
  }
/*
  If we think we have a solution, restore and confirm it with a call to
  setBestSolution().  We need to reset the cutoff value so as not to fathom
  the solution on bounds.  Note that calling setBestSolution( ..., true)
  leaves the continuousSolver_ bounds vectors fixed at the solution value.

  Running resolve() here is a failsafe --- setBestSolution has already
  reoptimised using the continuousSolver_. If for some reason we fail to
  prove optimality, run the problem again after instructing the solver to
  tell us more.

  If all looks good, replace solver_ with continuousSolver_, so that the
  outside world will be able to obtain information about the solution using
  public methods.
*/
  if (bestSolution_&&solverCharacteristics_->solverType()<2)
  { setCutoff(1.0e50) ; // As best solution should be worse than cutoff
    phase_=5;
    double increment = getDblParam(CbcModel::CbcCutoffIncrement) ;
    bestObjective_ += 100.0*increment+1.0e-3;
    setBestSolution(CBC_END_SOLUTION,bestObjective_,bestSolution_,true) ;
    continuousSolver_->resolve() ;
    if (!continuousSolver_->isProvenOptimal())
    { continuousSolver_->messageHandler()->setLogLevel(2) ;
      continuousSolver_->initialSolve() ; }
    delete solver_ ;
    // above deletes solverCharacteristics_
    solverCharacteristics_ = NULL;
    solver_ = continuousSolver_ ;
    setPointers(solver_);
    continuousSolver_ = NULL ; }
/*
  Clean up dangling objects. continuousSolver_ may already be toast.
*/
  delete lastws ;
  delete [] whichGenerator_ ;
  whichGenerator_=NULL;
  delete [] lowerBefore ;
  delete [] upperBefore ;
  delete [] walkback_ ;
  walkback_ = NULL ;
  delete [] addedCuts_ ;
  addedCuts_ = NULL ;
  // Get rid of characteristics
  solverCharacteristics_=NULL;
  if (continuousSolver_)
  { delete continuousSolver_ ;
    continuousSolver_ = NULL ; }
/*
  Destroy global cuts by replacing with an empty OsiCuts object.
*/
  globalCuts_= OsiCuts() ;
  if (strategy_&&strategy_->preProcessState()>0) {
    // undo preprocessing
    CglPreProcess * process = strategy_->process();
    assert (process);
    int n = originalSolver->getNumCols();
    if (bestSolution_) {
      delete [] bestSolution_;
      bestSolution_ = new double [n];
      process->postProcess(*solver_);
    }
    strategy_->deletePreProcess();
    // Solution now back in originalSolver
    delete solver_;
    solver_=originalSolver;
    if (bestSolution_)
      memcpy(bestSolution_,solver_->getColSolution(),n*sizeof(double));
    // put back original objects if there were any
    if (originalObject) {
      int iColumn;
      for (iColumn=0;iColumn<numberObjects_;iColumn++) 
        delete object_[iColumn];
      delete [] object_;
      numberObjects_ = numberOriginalObjects;
      object_=originalObject;
      delete [] integerVariable_;
      numberIntegers_=0;
      for (iColumn=0;iColumn<n;iColumn++) {
        if (solver_->isInteger(iColumn))
          numberIntegers_++;
      }
      integerVariable_ = new int[numberIntegers_];
      numberIntegers_=0;
      for (iColumn=0;iColumn<n;iColumn++) {
        if (solver_->isInteger(iColumn))
            integerVariable_[numberIntegers_++]=iColumn;
      }
    }
  }
  return ; }


// Solve the initial LP relaxation 
void 
CbcModel::initialSolve() 
{
  assert (solver_);
  assert (!solverCharacteristics_);
  OsiBabSolver * solverCharacteristics = dynamic_cast<OsiBabSolver *> (solver_->getAuxiliaryInfo());
  if (solverCharacteristics) {
    solverCharacteristics_ = solverCharacteristics;
  } else {
    // replace in solver
    OsiBabSolver defaultC;
    solver_->setAuxiliaryInfo(&defaultC);
    solverCharacteristics_ = dynamic_cast<OsiBabSolver *> (solver_->getAuxiliaryInfo());
  }
  solverCharacteristics_->setSolver(solver_);
  solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,NULL) ;
  solver_->initialSolve();
  solver_->setHintParam(OsiDoInBranchAndCut,false,OsiHintDo,NULL) ;
  // But set up so Jon Lee will be happy
  status_=-1;
  secondaryStatus_ = -1;
  originalContinuousObjective_ = solver_->getObjValue()*solver_->getObjSense();
  delete [] continuousSolution_;
  continuousSolution_ = CoinCopyOfArray(solver_->getColSolution(),
                                             solver_->getNumCols());
  setPointers(solver_);
  solverCharacteristics_ = NULL;
}

/*! \brief Get an empty basis object

  Return an empty CoinWarmStartBasis object with the requested capacity,
  appropriate for the current solver. The object is cloned from the object
  cached as emptyWarmStart_. If there is no cached object, the routine
  queries the solver for a warm start object, empties it, and caches the
  result.
*/

CoinWarmStartBasis *CbcModel::getEmptyBasis (int ns, int na) const

{ CoinWarmStartBasis *emptyBasis ;
/*
  Acquire an empty basis object, if we don't yet have one.
*/
  if (emptyWarmStart_ == 0)
  { if (solver_ == 0)
    { throw CoinError("Cannot construct basis without solver!",
                      "getEmptyBasis","CbcModel") ; }
    emptyBasis =
        dynamic_cast<CoinWarmStartBasis *>(solver_->getEmptyWarmStart()) ;
    if (emptyBasis == 0)
    { throw CoinError(
        "Solver does not appear to use a basis-oriented warm start.",
        "getEmptyBasis","CbcModel") ; }
    emptyBasis->setSize(0,0) ;
    emptyWarmStart_ = dynamic_cast<CoinWarmStart *>(emptyBasis) ; }
/*
  Clone the empty basis object, resize it as requested, and return.
*/
  emptyBasis = dynamic_cast<CoinWarmStartBasis *>(emptyWarmStart_->clone()) ;
  assert(emptyBasis) ;
  if (ns != 0 || na != 0) emptyBasis->setSize(ns,na) ;

  return (emptyBasis) ; }
    

/** Default Constructor

  Creates an empty model without an associated solver.
*/
CbcModel::CbcModel() 

:
  solver_(NULL),
  ourSolver_(true),
  continuousSolver_(NULL),
  referenceSolver_(NULL),
  defaultHandler_(true),
  emptyWarmStart_(NULL),
  bestObjective_(COIN_DBL_MAX),
  bestPossibleObjective_(COIN_DBL_MAX),
  sumChangeObjective1_(0.0),
  sumChangeObjective2_(0.0),
  bestSolution_(NULL),
  currentSolution_(NULL),
  testSolution_(NULL),
  minimumDrop_(1.0e-4),
  numberSolutions_(0),
  stateOfSearch_(0),
  hotstartSolution_(NULL),
  hotstartPriorities_(NULL),
  numberHeuristicSolutions_(0),
  numberNodes_(0),
  numberNodes2_(0),
  numberIterations_(0),
  status_(-1),
  secondaryStatus_(-1),
  numberIntegers_(0),
  numberRowsAtContinuous_(0),
  maximumNumberCuts_(0),
  phase_(0),
  currentNumberCuts_(0),
  maximumDepth_(0),
  walkback_(NULL),
  addedCuts_(NULL),
  nextRowCut_(NULL),
  currentNode_(NULL),
  integerVariable_(NULL),
  integerInfo_(NULL),
  continuousSolution_(NULL),
  usedInSolution_(NULL),
  specialOptions_(0),
  subTreeModel_(NULL),
  numberStoppedSubTrees_(0),
  presolve_(0),
  numberStrong_(5),
  numberBeforeTrust_(0),
  numberPenalties_(20),
  penaltyScaleFactor_(3.0),
  numberAnalyzeIterations_(0),
  analyzeResults_(NULL),
  numberInfeasibleNodes_(0),
  problemType_(0),
  printFrequency_(0),
  numberCutGenerators_(0),
  generator_(NULL),
  virginGenerator_(NULL),
  numberHeuristics_(0),
  heuristic_(NULL),
  lastHeuristic_(NULL),
  eventHandler_(0),
  numberObjects_(0),
  object_(NULL),
  originalColumns_(NULL),
  howOftenGlobalScan_(1),
  numberGlobalViolations_(0),
  continuousObjective_(COIN_DBL_MAX),
  originalContinuousObjective_(COIN_DBL_MAX),
  continuousInfeasibilities_(INT_MAX),
  maximumCutPassesAtRoot_(20),
  maximumCutPasses_(10),
  currentPassNumber_(0),
  maximumWhich_(1000),
  whichGenerator_(NULL),
  maximumStatistics_(0),
  statistics_(NULL),
  numberFixedAtRoot_(0),
  numberFixedNow_(0),
  stoppedOnGap_(false),
  eventHappened_(false),
  numberLongStrong_(0),
  numberOldActiveCuts_(0),
  numberNewCuts_(0),
  sizeMiniTree_(0),
  searchStrategy_(-1),
  numberStrongIterations_(0),
  resolveAfterTakeOffCuts_(true)
{
  intParam_[CbcMaxNumNode] = 2147483647;
  intParam_[CbcMaxNumSol] = 9999999;
  intParam_[CbcFathomDiscipline] = 0;

  dblParam_[CbcIntegerTolerance] = 1e-6;
  dblParam_[CbcInfeasibilityWeight] = 0.0;
  dblParam_[CbcCutoffIncrement] = 1e-5;
  dblParam_[CbcAllowableGap] = 1.0e-10;
  dblParam_[CbcAllowableFractionGap] = 0.0;
  dblParam_[CbcMaximumSeconds] = 1.0e100;
  dblParam_[CbcCurrentCutoff] = 1.0e100;
  dblParam_[CbcOptimizationDirection] = 1.0;
  dblParam_[CbcCurrentObjectiveValue] = 1.0e100;
  dblParam_[CbcCurrentMinimizationObjectiveValue] = 1.0e100;
  dblParam_[CbcStartSeconds] = 0.0;
  strongInfo_[0]=0;
  strongInfo_[1]=0;
  strongInfo_[2]=0;
  solverCharacteristics_ = NULL;
  nodeCompare_=new CbcCompareDefault();;
  problemFeasibility_=new CbcFeasibilityBase();
  tree_= new CbcTree();
  branchingMethod_=NULL;
  strategy_=NULL;
  parentModel_=NULL;
  cbcColLower_ = NULL;
  cbcColUpper_ = NULL;
  cbcRowLower_ = NULL;
  cbcRowUpper_ = NULL;
  cbcColSolution_ = NULL;
  cbcRowPrice_ = NULL;
  cbcReducedCost_ = NULL;
  cbcRowActivity_ = NULL;
  appData_=NULL;
  handler_ = new CoinMessageHandler();
  handler_->setLogLevel(2);
  messages_ = CbcMessage();
  eventHandler_ = new CbcEventHandler() ;
}

/** Constructor from solver.

  Creates a model complete with a clone of the solver passed as a parameter.
*/

CbcModel::CbcModel(const OsiSolverInterface &rhs)
:
  continuousSolver_(NULL),
  referenceSolver_(NULL),
  defaultHandler_(true),
  emptyWarmStart_(NULL),
  bestObjective_(COIN_DBL_MAX),
  bestPossibleObjective_(COIN_DBL_MAX),
  sumChangeObjective1_(0.0),
  sumChangeObjective2_(0.0),
  minimumDrop_(1.0e-4),
  numberSolutions_(0),
  stateOfSearch_(0),
  hotstartSolution_(NULL),
  hotstartPriorities_(NULL),
  numberHeuristicSolutions_(0),
  numberNodes_(0),
  numberNodes2_(0),
  numberIterations_(0),
  status_(-1),
  secondaryStatus_(-1),
  numberRowsAtContinuous_(0),
  maximumNumberCuts_(0),
  phase_(0),
  currentNumberCuts_(0),
  maximumDepth_(0),
  walkback_(NULL),
  addedCuts_(NULL),
  nextRowCut_(NULL),
  currentNode_(NULL),
  integerInfo_(NULL),
  specialOptions_(0),
  subTreeModel_(NULL),
  numberStoppedSubTrees_(0),
  presolve_(0),
  numberStrong_(5),
  numberBeforeTrust_(0),
  numberPenalties_(20),
  penaltyScaleFactor_(3.0),
  numberAnalyzeIterations_(0),
  analyzeResults_(NULL),
  numberInfeasibleNodes_(0),
  problemType_(0),
  printFrequency_(0),
  numberCutGenerators_(0),
  generator_(NULL),
  virginGenerator_(NULL),
  numberHeuristics_(0),
  heuristic_(NULL),
  lastHeuristic_(NULL),
  eventHandler_(0),
  numberObjects_(0),
  object_(NULL),
  originalColumns_(NULL),
  howOftenGlobalScan_(1),
  numberGlobalViolations_(0),
  continuousObjective_(COIN_DBL_MAX),
  originalContinuousObjective_(COIN_DBL_MAX),
  continuousInfeasibilities_(INT_MAX),
  maximumCutPassesAtRoot_(20),
  maximumCutPasses_(10),
  currentPassNumber_(0),
  maximumWhich_(1000),
  whichGenerator_(NULL),
  maximumStatistics_(0),
  statistics_(NULL),
  numberFixedAtRoot_(0),
  numberFixedNow_(0),
  stoppedOnGap_(false),
  eventHappened_(false),
  numberLongStrong_(0),
  numberOldActiveCuts_(0),
  numberNewCuts_(0),
  sizeMiniTree_(0),
  searchStrategy_(-1),
  numberStrongIterations_(0),
  resolveAfterTakeOffCuts_(true)
{
  intParam_[CbcMaxNumNode] = 2147483647;
  intParam_[CbcMaxNumSol] = 9999999;
  intParam_[CbcFathomDiscipline] = 0;

  dblParam_[CbcIntegerTolerance] = 1e-6;
  dblParam_[CbcInfeasibilityWeight] = 0.0;
  dblParam_[CbcCutoffIncrement] = 1e-5;
  dblParam_[CbcAllowableGap] = 1.0e-10;
  dblParam_[CbcAllowableFractionGap] = 0.0;
  dblParam_[CbcMaximumSeconds] = 1.0e100;
  dblParam_[CbcCurrentCutoff] = 1.0e100;
  dblParam_[CbcOptimizationDirection] = 1.0;
  dblParam_[CbcCurrentObjectiveValue] = 1.0e100;
  dblParam_[CbcCurrentMinimizationObjectiveValue] = 1.0e100;
  dblParam_[CbcStartSeconds] = 0.0;
  strongInfo_[0]=0;
  strongInfo_[1]=0;
  strongInfo_[2]=0;
  solverCharacteristics_ = NULL;

  nodeCompare_=new CbcCompareDefault();;
  problemFeasibility_=new CbcFeasibilityBase();
  tree_= new CbcTree();
  branchingMethod_=NULL;
  strategy_=NULL;
  parentModel_=NULL;
  appData_=NULL;
  handler_ = new CoinMessageHandler();
  handler_->setLogLevel(2);
  messages_ = CbcMessage();
  eventHandler_ = new CbcEventHandler() ;
  solver_ = rhs.clone();
  referenceSolver_ = solver_->clone();
  ourSolver_ = true ;
  cbcColLower_ = NULL;
  cbcColUpper_ = NULL;
  cbcRowLower_ = NULL;
  cbcRowUpper_ = NULL;
  cbcColSolution_ = NULL;
  cbcRowPrice_ = NULL;
  cbcReducedCost_ = NULL;
  cbcRowActivity_ = NULL;

  // Initialize solution and integer variable vectors
  bestSolution_ = NULL; // to say no solution found
  numberIntegers_=0;
  int numberColumns = solver_->getNumCols();
  int iColumn;
  if (numberColumns) {
    // Space for current solution
    currentSolution_ = new double[numberColumns];
    continuousSolution_ = new double[numberColumns];
    usedInSolution_ = new int[numberColumns];
    for (iColumn=0;iColumn<numberColumns;iColumn++) {
      if( solver_->isInteger(iColumn)) 
        numberIntegers_++;
    }
  } else {
    // empty model
    currentSolution_=NULL;
    continuousSolution_=NULL;
    usedInSolution_=NULL;
  }
  testSolution_=currentSolution_;
  if (numberIntegers_) {
    integerVariable_ = new int [numberIntegers_];
    numberIntegers_=0;
    for (iColumn=0;iColumn<numberColumns;iColumn++) {
      if( solver_->isInteger(iColumn)) 
        integerVariable_[numberIntegers_++]=iColumn;
    }
  } else {
    integerVariable_ = NULL;
  }
}

/*
  Assign a solver to the model (model assumes ownership)

  The integer variable vector is initialized if it's not already present.
  If deleteSolver then current solver deleted (if model owned)

  Assuming ownership matches usage in OsiSolverInterface
  (cf. assignProblem, loadProblem).

  TODO: What to do about solver parameters? A simple copy likely won't do it,
        because the SI must push the settings into the underlying solver. In
        the context of switching solvers in cbc, this means that command line
        settings will get lost. Stash the command line somewhere and reread it
        here, maybe?
  
  TODO: More generally, how much state should be transferred from the old
        solver to the new solver? Best perhaps to see how usage develops.
        What's done here mimics the CbcModel(OsiSolverInterface) constructor.
*/
void
CbcModel::assignSolver(OsiSolverInterface *&solver, bool deleteSolver)

{
  // resize best solution if exists
  if (bestSolution_) {
    int nOld = solver_->getNumCols();
    int nNew = solver->getNumCols();
    if (nNew>nOld) {
      double * temp = new double[nNew];
      memcpy(temp,bestSolution_,nOld*sizeof(double));
      memset(temp+nOld,0,(nNew-nOld)*sizeof(double));
      delete [] bestSolution_;
      bestSolution_=temp;
    }
  }
  // Keep the current message level for solver (if solver exists)
  if (solver_)
    solver->messageHandler()->setLogLevel(solver_->messageHandler()->logLevel()) ;

  if (ourSolver_&&deleteSolver) delete solver_ ;
  solver_ = solver;
  solver = NULL ;
  ourSolver_ = true ;
/*
  Basis information is solver-specific.
*/
  if (emptyWarmStart_)
  { delete emptyWarmStart_  ;
    emptyWarmStart_ = 0 ; }
/*
  Initialize integer variable vector.
*/
  numberIntegers_=0;
  int numberColumns = solver_->getNumCols();
  int iColumn;
  for (iColumn=0;iColumn<numberColumns;iColumn++) {
    if( solver_->isInteger(iColumn)) 
      numberIntegers_++;
  }
  if (numberIntegers_) {
    delete [] integerVariable_;
    integerVariable_ = new int [numberIntegers_];
    numberIntegers_=0;
    for (iColumn=0;iColumn<numberColumns;iColumn++) {
      if( solver_->isInteger(iColumn)) 
        integerVariable_[numberIntegers_++]=iColumn;
    }
  } else {
    integerVariable_ = NULL;
  }

  return ;
}

// Copy constructor.

CbcModel::CbcModel(const CbcModel & rhs, bool noTree)
:
  continuousSolver_(NULL),
  referenceSolver_(NULL),
  defaultHandler_(rhs.defaultHandler_),
  emptyWarmStart_(NULL),
  bestObjective_(rhs.bestObjective_),
  bestPossibleObjective_(rhs.bestPossibleObjective_),
  sumChangeObjective1_(rhs.sumChangeObjective1_),
  sumChangeObjective2_(rhs.sumChangeObjective2_),
  minimumDrop_(rhs.minimumDrop_),
  numberSolutions_(rhs.numberSolutions_),
  stateOfSearch_(rhs.stateOfSearch_),
  numberHeuristicSolutions_(rhs.numberHeuristicSolutions_),
  numberNodes_(rhs.numberNodes_),
  numberNodes2_(rhs.numberNodes2_),
  numberIterations_(rhs.numberIterations_),
  status_(rhs.status_),
  secondaryStatus_(rhs.secondaryStatus_),
  specialOptions_(rhs.specialOptions_),
  subTreeModel_(rhs.subTreeModel_),
  numberStoppedSubTrees_(rhs.numberStoppedSubTrees_),
  presolve_(rhs.presolve_),
  numberStrong_(rhs.numberStrong_),
  numberBeforeTrust_(rhs.numberBeforeTrust_),
  numberPenalties_(rhs.numberPenalties_),
  penaltyScaleFactor_(rhs.penaltyScaleFactor_),
  numberAnalyzeIterations_(rhs.numberAnalyzeIterations_),
  analyzeResults_(NULL),
  numberInfeasibleNodes_(rhs.numberInfeasibleNodes_),
  problemType_(rhs.problemType_),
  printFrequency_(rhs.printFrequency_),
  howOftenGlobalScan_(rhs.howOftenGlobalScan_),
  numberGlobalViolations_(rhs.numberGlobalViolations_),
  continuousObjective_(rhs.continuousObjective_),
  originalContinuousObjective_(rhs.originalContinuousObjective_),
  continuousInfeasibilities_(rhs.continuousInfeasibilities_),
  maximumCutPassesAtRoot_(rhs.maximumCutPassesAtRoot_),
  maximumCutPasses_( rhs.maximumCutPasses_),
  currentPassNumber_(rhs.currentPassNumber_),
  maximumWhich_(rhs.maximumWhich_),
  whichGenerator_(NULL),
  maximumStatistics_(0),
  statistics_(NULL),
  numberFixedAtRoot_(rhs.numberFixedAtRoot_),
  numberFixedNow_(rhs.numberFixedNow_),
  stoppedOnGap_(rhs.stoppedOnGap_),
  eventHappened_(rhs.eventHappened_),
  numberLongStrong_(rhs.numberLongStrong_),
  numberOldActiveCuts_(rhs.numberOldActiveCuts_),
  numberNewCuts_(rhs.numberNewCuts_),
  sizeMiniTree_(rhs.sizeMiniTree_),
  searchStrategy_(rhs.searchStrategy_),
  numberStrongIterations_(rhs.numberStrongIterations_),
  resolveAfterTakeOffCuts_(rhs.resolveAfterTakeOffCuts_)
{
  intParam_[CbcMaxNumNode] = rhs.intParam_[CbcMaxNumNode];
  intParam_[CbcMaxNumSol] = rhs.intParam_[CbcMaxNumSol];
  intParam_[CbcFathomDiscipline] = rhs.intParam_[CbcFathomDiscipline];
  dblParam_[CbcIntegerTolerance] = rhs.dblParam_[CbcIntegerTolerance];
  dblParam_[CbcInfeasibilityWeight] = rhs.dblParam_[CbcInfeasibilityWeight];
  dblParam_[CbcCutoffIncrement] = rhs.dblParam_[CbcCutoffIncrement]; 
  dblParam_[CbcAllowableGap] = rhs.dblParam_[CbcAllowableGap]; 
  dblParam_[CbcAllowableFractionGap] = rhs.dblParam_[CbcAllowableFractionGap]; 
  dblParam_[CbcMaximumSeconds] = rhs.dblParam_[CbcMaximumSeconds];
  dblParam_[CbcCurrentCutoff] = rhs.dblParam_[CbcCurrentCutoff];
  dblParam_[CbcOptimizationDirection] = rhs.dblParam_[CbcOptimizationDirection];
  dblParam_[CbcCurrentObjectiveValue] = rhs.dblParam_[CbcCurrentObjectiveValue];
  dblParam_[CbcCurrentMinimizationObjectiveValue] = rhs.dblParam_[CbcCurrentMinimizationObjectiveValue];
  dblParam_[CbcStartSeconds] = dblParam_[CbcStartSeconds]; // will be overwritten hopefully
  strongInfo_[0]=rhs.strongInfo_[0];
  strongInfo_[1]=rhs.strongInfo_[1];
  strongInfo_[2]=rhs.strongInfo_[2];
  solverCharacteristics_ = NULL;
  if (rhs.emptyWarmStart_) emptyWarmStart_ = rhs.emptyWarmStart_->clone() ;
  if (defaultHandler_) {
    handler_ = new CoinMessageHandler();
    handler_->setLogLevel(2);
  } else {
    handler_ = rhs.handler_;
  }
  messageHandler()->setLogLevel(rhs.messageHandler()->logLevel());
  numberCutGenerators_ = rhs.numberCutGenerators_;
  if (numberCutGenerators_) {
    generator_ = new CbcCutGenerator * [numberCutGenerators_];
    virginGenerator_ = new CbcCutGenerator * [numberCutGenerators_];
    int i;
    for (i=0;i<numberCutGenerators_;i++) {
      generator_[i]=new CbcCutGenerator(*rhs.generator_[i]);
      virginGenerator_[i]=new CbcCutGenerator(*rhs.virginGenerator_[i]);
    }
  } else {
    generator_=NULL;
    virginGenerator_=NULL;
  }
  if (!noTree)
    globalCuts_ = rhs.globalCuts_;
  numberHeuristics_ = rhs.numberHeuristics_;
  if (numberHeuristics_) {
    heuristic_ = new CbcHeuristic * [numberHeuristics_];
    int i;
    for (i=0;i<numberHeuristics_;i++) {
      heuristic_[i]=rhs.heuristic_[i]->clone();
    }
  } else {
    heuristic_=NULL;
  }
  lastHeuristic_ = NULL;
  if (rhs.eventHandler_)
  { eventHandler_ = new CbcEventHandler(*rhs.eventHandler_) ; }
  else
  { eventHandler_ = NULL ; }
  numberObjects_=rhs.numberObjects_;
  if (numberObjects_) {
    object_ = new CbcObject * [numberObjects_];
    int i;
    for (i=0;i<numberObjects_;i++) 
      object_[i]=(rhs.object_[i])->clone();
  } else {
    object_=NULL;
  }
  if (rhs.referenceSolver_)
    referenceSolver_ = rhs.referenceSolver_->clone();
  else
    referenceSolver_=NULL;
  if (!noTree||!rhs.continuousSolver_)
    solver_ = rhs.solver_->clone();
  else
    solver_ = rhs.continuousSolver_->clone();
  if (rhs.originalColumns_) {
    int numberColumns = solver_->getNumCols();
    originalColumns_= new int [numberColumns];
    memcpy(originalColumns_,rhs.originalColumns_,numberColumns*sizeof(int));
  } else {
    originalColumns_=NULL;
  }
  if (maximumWhich_&&rhs.whichGenerator_)
    whichGenerator_ = CoinCopyOfArray(rhs.whichGenerator_,maximumWhich_);
  nodeCompare_=rhs.nodeCompare_->clone();
  problemFeasibility_=rhs.problemFeasibility_->clone();
  tree_= rhs.tree_->clone();
  branchingMethod_=rhs.branchingMethod_;
  cbcColLower_ = NULL;
  cbcColUpper_ = NULL;
  cbcRowLower_ = NULL;
  cbcRowUpper_ = NULL;
  cbcColSolution_ = NULL;
  cbcRowPrice_ = NULL;
  cbcReducedCost_ = NULL;
  cbcRowActivity_ = NULL;
  if (rhs.strategy_)
    strategy_=rhs.strategy_->clone();
  else
    strategy_=NULL;
  parentModel_=rhs.parentModel_;
  appData_=rhs.appData_;
  messages_ = rhs.messages_;
  ourSolver_ = true ;
  messageHandler()->setLogLevel(rhs.messageHandler()->logLevel());
  numberIntegers_=rhs.numberIntegers_;
  if (numberIntegers_) {
    integerVariable_ = new int [numberIntegers_];
    memcpy(integerVariable_,rhs.integerVariable_,numberIntegers_*sizeof(int));
    integerInfo_ = CoinCopyOfArray(rhs.integerInfo_,solver_->getNumCols());
  } else {
    integerVariable_ = NULL;
    integerInfo_=NULL;
  }
  if (rhs.hotstartSolution_) {
    int numberColumns = solver_->getNumCols();
    hotstartSolution_ = CoinCopyOfArray(rhs.hotstartSolution_,numberColumns);
    hotstartPriorities_ = CoinCopyOfArray(rhs.hotstartPriorities_,numberColumns);
  } else {
    hotstartSolution_ = NULL;
    hotstartPriorities_ =NULL;
  }
  if (rhs.bestSolution_&&!noTree) {
    int numberColumns = solver_->getNumCols();
    bestSolution_ = new double[numberColumns];
    memcpy(bestSolution_,rhs.bestSolution_,numberColumns*sizeof(double));
  } else {
    bestSolution_=NULL;
  }
  if (!noTree) {
    int numberColumns = solver_->getNumCols();
    currentSolution_ = CoinCopyOfArray(rhs.currentSolution_,numberColumns);
    continuousSolution_ = CoinCopyOfArray(rhs.continuousSolution_,numberColumns);
    usedInSolution_ = CoinCopyOfArray(rhs.usedInSolution_,numberColumns);
  } else {
    currentSolution_=NULL;
    continuousSolution_=NULL;
    usedInSolution_=NULL;
  }
  testSolution_=currentSolution_;
  numberRowsAtContinuous_ = rhs.numberRowsAtContinuous_;
  maximumNumberCuts_=rhs.maximumNumberCuts_;
  phase_ = rhs.phase_;
  currentNumberCuts_=rhs.currentNumberCuts_;
  maximumDepth_= rhs.maximumDepth_;
  if (noTree) {
    bestObjective_ = COIN_DBL_MAX;
    numberSolutions_ =0;
    stateOfSearch_= 0;
    numberHeuristicSolutions_=0;
    numberNodes_=0;
    numberNodes2_=0;
    numberIterations_=0;
    status_=0;
    subTreeModel_=NULL;
    numberStoppedSubTrees_=0;
    continuousObjective_=COIN_DBL_MAX;
    originalContinuousObjective_=COIN_DBL_MAX;
    continuousInfeasibilities_=INT_MAX;
    maximumNumberCuts_=0;
    tree_->cleanTree(this,-COIN_DBL_MAX,bestPossibleObjective_);
    bestPossibleObjective_ = COIN_DBL_MAX;
  }
  // These are only used as temporary arrays so need not be filled
  if (maximumNumberCuts_) {
    addedCuts_ = new CbcCountRowCut * [maximumNumberCuts_];
  } else {
    addedCuts_ = NULL;
  }
  nextRowCut_ = NULL;
  currentNode_ = NULL;
  if (maximumDepth_)
    walkback_ = new CbcNodeInfo * [maximumDepth_];
  else
    walkback_ = NULL;
  synchronizeModel();
}
  
// Assignment operator 
CbcModel & 
CbcModel::operator=(const CbcModel& rhs)
{
  if (this!=&rhs) {
    gutsOfDestructor();
    if (defaultHandler_)
    { delete handler_;
      handler_ = NULL; }
    defaultHandler_ = rhs.defaultHandler_;
    if (defaultHandler_)
    { handler_ = new CoinMessageHandler();
      handler_->setLogLevel(2); }
    else
    { handler_ = rhs.handler_; }
    messages_ = rhs.messages_;
    messageHandler()->setLogLevel(rhs.messageHandler()->logLevel());

    delete solver_;
    if (rhs.solver_)
    { solver_ = rhs.solver_->clone() ; }
    else
    { solver_ = 0 ; }
    ourSolver_ = true ;
    delete continuousSolver_ ;
    if (rhs.continuousSolver_)
    { solver_ = rhs.continuousSolver_->clone() ; }
    else
    { continuousSolver_ = 0 ; }

    if (rhs.referenceSolver_)
    { solver_ = rhs.referenceSolver_->clone() ; }
    else
    { referenceSolver_ = NULL ; }

    delete emptyWarmStart_ ;
    if (rhs.emptyWarmStart_)
    { emptyWarmStart_ = rhs.emptyWarmStart_->clone() ; }
    else
    { emptyWarmStart_ = 0 ; }

    bestObjective_ = rhs.bestObjective_;
    bestPossibleObjective_=rhs.bestPossibleObjective_;
    sumChangeObjective1_=rhs.sumChangeObjective1_;
    sumChangeObjective2_=rhs.sumChangeObjective2_;
    delete [] bestSolution_;
    if (rhs.bestSolution_) {
      int numberColumns = rhs.getNumCols();
      bestSolution_ = new double[numberColumns];
      memcpy(bestSolution_,rhs.bestSolution_,numberColumns*sizeof(double));
    } else {
      bestSolution_=NULL;
    }
    int numberColumns = solver_->getNumCols();
    currentSolution_ = CoinCopyOfArray(rhs.currentSolution_,numberColumns);
    continuousSolution_ = CoinCopyOfArray(rhs.continuousSolution_,numberColumns);
    usedInSolution_ = CoinCopyOfArray(rhs.usedInSolution_,numberColumns);
    testSolution_=currentSolution_;
    minimumDrop_ = rhs.minimumDrop_;
    numberSolutions_=rhs.numberSolutions_;
    stateOfSearch_= rhs.stateOfSearch_;
    numberHeuristicSolutions_=rhs.numberHeuristicSolutions_;
    numberNodes_ = rhs.numberNodes_;
    numberNodes2_ = rhs.numberNodes2_;
    numberIterations_ = rhs.numberIterations_;
    status_ = rhs.status_;
    secondaryStatus_ = rhs.secondaryStatus_;
    specialOptions_ = rhs.specialOptions_;
    subTreeModel_ = rhs.subTreeModel_;
    numberStoppedSubTrees_ = rhs.numberStoppedSubTrees_;
    presolve_ = rhs.presolve_;
    numberStrong_ = rhs.numberStrong_;
    numberBeforeTrust_ = rhs.numberBeforeTrust_;
    numberPenalties_ = rhs.numberPenalties_;
    penaltyScaleFactor_ = rhs.penaltyScaleFactor_;
    numberAnalyzeIterations_ = rhs.numberAnalyzeIterations_;
    delete [] analyzeResults_;
    analyzeResults_ = NULL;
    numberInfeasibleNodes_ = rhs.numberInfeasibleNodes_;
    problemType_ = rhs.problemType_;
    printFrequency_ = rhs.printFrequency_;
    howOftenGlobalScan_=rhs.howOftenGlobalScan_;
    numberGlobalViolations_=rhs.numberGlobalViolations_;
    continuousObjective_=rhs.continuousObjective_;
    originalContinuousObjective_ = rhs.originalContinuousObjective_;
    continuousInfeasibilities_ = rhs.continuousInfeasibilities_;
    maximumCutPassesAtRoot_ = rhs.maximumCutPassesAtRoot_;
    maximumCutPasses_ = rhs.maximumCutPasses_;
    currentPassNumber_ = rhs.currentPassNumber_;
    intParam_[CbcMaxNumNode] = rhs.intParam_[CbcMaxNumNode];
    intParam_[CbcMaxNumSol] = rhs.intParam_[CbcMaxNumSol];
    intParam_[CbcFathomDiscipline] = rhs.intParam_[CbcFathomDiscipline];
    dblParam_[CbcIntegerTolerance] = rhs.dblParam_[CbcIntegerTolerance];
    dblParam_[CbcInfeasibilityWeight] = rhs.dblParam_[CbcInfeasibilityWeight];
    dblParam_[CbcCutoffIncrement] = rhs.dblParam_[CbcCutoffIncrement]; 
    dblParam_[CbcAllowableGap] = rhs.dblParam_[CbcAllowableGap]; 
    dblParam_[CbcAllowableFractionGap] = rhs.dblParam_[CbcAllowableFractionGap]; 
    dblParam_[CbcMaximumSeconds] = rhs.dblParam_[CbcMaximumSeconds];
    dblParam_[CbcCurrentCutoff] = rhs.dblParam_[CbcCurrentCutoff];
    dblParam_[CbcOptimizationDirection] = rhs.dblParam_[CbcOptimizationDirection];
    dblParam_[CbcCurrentObjectiveValue] = rhs.dblParam_[CbcCurrentObjectiveValue];
    dblParam_[CbcCurrentMinimizationObjectiveValue] = rhs.dblParam_[CbcCurrentMinimizationObjectiveValue];
    dblParam_[CbcStartSeconds] = dblParam_[CbcStartSeconds]; // will be overwritten hopefully
    globalCuts_ = rhs.globalCuts_;
    int i;
    for (i=0;i<numberCutGenerators_;i++) {
      delete generator_[i];
      delete virginGenerator_[i];
    }
    delete [] generator_;
    delete [] virginGenerator_;
    delete [] heuristic_;
    maximumWhich_ = rhs.maximumWhich_;
    delete [] whichGenerator_;
    whichGenerator_ = NULL;
    if (maximumWhich_&&rhs.whichGenerator_)
      whichGenerator_ = CoinCopyOfArray(rhs.whichGenerator_,maximumWhich_);
    for (i=0;i<maximumStatistics_;i++)
      delete statistics_[i];
    delete [] statistics_;
    maximumStatistics_ = 0;
    statistics_ = NULL;
    numberFixedAtRoot_ = rhs.numberFixedAtRoot_;
    numberFixedNow_ = rhs.numberFixedNow_;
    stoppedOnGap_ = rhs.stoppedOnGap_;
    eventHappened_ = rhs.eventHappened_;
    numberLongStrong_ = rhs.numberLongStrong_;
    numberOldActiveCuts_ = rhs.numberOldActiveCuts_;
    numberNewCuts_ = rhs.numberNewCuts_;
    resolveAfterTakeOffCuts_=rhs.resolveAfterTakeOffCuts_;
    sizeMiniTree_ = rhs.sizeMiniTree_;
    searchStrategy_ = rhs.searchStrategy_;
    numberStrongIterations_ = rhs.numberStrongIterations_;
    strongInfo_[0]=rhs.strongInfo_[0];
    strongInfo_[1]=rhs.strongInfo_[1];
    strongInfo_[2]=rhs.strongInfo_[2];
    solverCharacteristics_ = NULL;
    lastHeuristic_ = NULL;
    numberCutGenerators_ = rhs.numberCutGenerators_;
    if (numberCutGenerators_) {
      generator_ = new CbcCutGenerator * [numberCutGenerators_];
      virginGenerator_ = new CbcCutGenerator * [numberCutGenerators_];
      int i;
      for (i=0;i<numberCutGenerators_;i++) {
        generator_[i]=new CbcCutGenerator(*rhs.generator_[i]);
        virginGenerator_[i]=new CbcCutGenerator(*rhs.virginGenerator_[i]);
      }
    } else {
      generator_=NULL;
      virginGenerator_=NULL;
    }
    numberHeuristics_ = rhs.numberHeuristics_;
    if (numberHeuristics_) {
      heuristic_ = new CbcHeuristic * [numberHeuristics_];
      memcpy(heuristic_,rhs.heuristic_,
             numberHeuristics_*sizeof(CbcHeuristic *));
    } else {
      heuristic_=NULL;
    }
    lastHeuristic_ = NULL;
    if (eventHandler_)
      delete eventHandler_ ;
    if (rhs.eventHandler_)
    { eventHandler_ = new CbcEventHandler(*rhs.eventHandler_) ; }
    else
    { eventHandler_ = NULL ; }
    for (i=0;i<numberObjects_;i++)
      delete object_[i];
    delete [] object_;
    numberObjects_=rhs.numberObjects_;
    if (numberObjects_) {
      object_ = new CbcObject * [numberObjects_];
      int i;
      for (i=0;i<numberObjects_;i++) 
        object_[i]=(rhs.object_[i])->clone();
    } else {
      object_=NULL;
    }
    delete [] originalColumns_;
    if (rhs.originalColumns_) {
      int numberColumns = rhs.getNumCols();
      originalColumns_= new int [numberColumns];
      memcpy(originalColumns_,rhs.originalColumns_,numberColumns*sizeof(int));
    } else {
      originalColumns_=NULL;
    }
    nodeCompare_=rhs.nodeCompare_->clone();
    problemFeasibility_=rhs.problemFeasibility_->clone();
    delete tree_;
    tree_= rhs.tree_->clone();
    branchingMethod_=rhs.branchingMethod_;
    delete strategy_;
    if (rhs.strategy_)
      strategy_=rhs.strategy_->clone();
    else
      strategy_=NULL;
    parentModel_=rhs.parentModel_;
    appData_=rhs.appData_;

    delete [] integerVariable_;
    numberIntegers_=rhs.numberIntegers_;
    if (numberIntegers_) {
      integerVariable_ = new int [numberIntegers_];
      memcpy(integerVariable_,rhs.integerVariable_,
             numberIntegers_*sizeof(int));
      integerInfo_ = CoinCopyOfArray(rhs.integerInfo_,solver_->getNumCols());
    } else {
      integerVariable_ = NULL;
      integerInfo_=NULL;
    }
    if (rhs.hotstartSolution_) {
      int numberColumns = solver_->getNumCols();
      hotstartSolution_ = CoinCopyOfArray(rhs.hotstartSolution_,numberColumns);
      hotstartPriorities_ = CoinCopyOfArray(rhs.hotstartPriorities_,numberColumns);
    } else {
      hotstartSolution_ = NULL;
      hotstartPriorities_ =NULL;
    }
    numberRowsAtContinuous_ = rhs.numberRowsAtContinuous_;
    maximumNumberCuts_=rhs.maximumNumberCuts_;
    phase_ = rhs.phase_;
    currentNumberCuts_=rhs.currentNumberCuts_;
    maximumDepth_= rhs.maximumDepth_;
    delete [] addedCuts_;
    delete [] walkback_;
    // These are only used as temporary arrays so need not be filled
    if (maximumNumberCuts_) {
      addedCuts_ = new CbcCountRowCut * [maximumNumberCuts_];
    } else {
      addedCuts_ = NULL;
    }
    nextRowCut_ = NULL;
    currentNode_ = NULL;
    if (maximumDepth_)
      walkback_ = new CbcNodeInfo * [maximumDepth_];
    else
      walkback_ = NULL;
    synchronizeModel();
    cbcColLower_ = NULL;
    cbcColUpper_ = NULL;
    cbcRowLower_ = NULL;
    cbcRowUpper_ = NULL;
    cbcColSolution_ = NULL;
    cbcRowPrice_ = NULL;
    cbcReducedCost_ = NULL;
    cbcRowActivity_ = NULL;
  }
  return *this;
}
  
// Destructor 
CbcModel::~CbcModel ()
{
  if (defaultHandler_) {
    delete handler_;
    handler_ = NULL;
  }
  delete tree_;
  tree_=NULL;
  if (ourSolver_) delete solver_;
  gutsOfDestructor();
  delete eventHandler_ ;
  eventHandler_ = NULL ;
}
// Clears out as much as possible (except solver)
void 
CbcModel::gutsOfDestructor()
{
  delete referenceSolver_;
  referenceSolver_=NULL;
  int i;
  for (i=0;i<numberCutGenerators_;i++) {
    delete generator_[i];
    delete virginGenerator_[i];
  }
  delete [] generator_;
  delete [] virginGenerator_;
  generator_=NULL;
  virginGenerator_=NULL;
  for (i=0;i<numberHeuristics_;i++)
    delete heuristic_[i];
  delete [] heuristic_;
  heuristic_=NULL;
  delete nodeCompare_;
  nodeCompare_=NULL;
  delete problemFeasibility_;
  problemFeasibility_=NULL;
  delete [] originalColumns_;
  originalColumns_=NULL;
  delete strategy_;
  gutsOfDestructor2();
}
// Clears out enough to reset CbcModel
void 
CbcModel::gutsOfDestructor2()
{
  delete [] integerInfo_;
  integerInfo_=NULL;
  delete [] integerVariable_;
  integerVariable_=NULL;
  int i;
  for (i=0;i<numberObjects_;i++)
    delete object_[i];
  delete [] object_;
  object_=NULL;
  numberIntegers_=0;
  numberObjects_=0;
  delete emptyWarmStart_ ;
  emptyWarmStart_ =NULL;
  delete continuousSolver_;
  continuousSolver_=NULL;
  delete [] bestSolution_;
  bestSolution_=NULL;
  delete [] currentSolution_;
  currentSolution_=NULL;
  delete [] continuousSolution_;
  continuousSolution_=NULL;
  solverCharacteristics_=NULL;
  delete [] usedInSolution_;
  usedInSolution_ = NULL;
  testSolution_=NULL;
  lastHeuristic_ = NULL;
  delete [] addedCuts_;
  addedCuts_=NULL;
  nextRowCut_ = NULL;
  currentNode_ = NULL;
  delete [] walkback_;
  walkback_=NULL;
  delete [] whichGenerator_;
  whichGenerator_ = NULL;
  for (i=0;i<maximumStatistics_;i++)
    delete statistics_[i];
  delete [] statistics_;
  statistics_=NULL;
  maximumStatistics_=0;
  delete [] analyzeResults_;
  analyzeResults_=NULL;
  // Below here is whatever consensus is
  ourSolver_=true;
  bestObjective_=COIN_DBL_MAX;
  bestPossibleObjective_=COIN_DBL_MAX;
  sumChangeObjective1_=0.0;
  sumChangeObjective2_=0.0;
  numberSolutions_=0;
  stateOfSearch_=0;
  delete [] hotstartSolution_;
  hotstartSolution_=NULL;
  delete [] hotstartPriorities_;
  hotstartPriorities_=NULL;
  numberHeuristicSolutions_=0;
  numberNodes_=0;
  numberNodes2_=0;
  numberIterations_=0;
  status_=-1;
  secondaryStatus_=-1;
  maximumNumberCuts_=0;
  phase_=0;
  currentNumberCuts_=0;
  maximumDepth_=0;
  nextRowCut_=NULL;
  currentNode_=NULL;
  // clear out tree
  if (tree_&&tree_->size())
    tree_->cleanTree(this, -1.0e100,bestPossibleObjective_) ;
  subTreeModel_=NULL;
  numberStoppedSubTrees_=0;
  numberInfeasibleNodes_=0;
  numberGlobalViolations_=0;
  continuousObjective_=0.0;
  originalContinuousObjective_=0.0;
  continuousInfeasibilities_=0;
  numberFixedAtRoot_=0;
  numberFixedNow_=0;
  stoppedOnGap_=false;
  eventHappened_=false;
  numberLongStrong_=0;
  numberOldActiveCuts_=0;
  numberNewCuts_=0;
  searchStrategy_=-1;
  numberStrongIterations_=0;
  // Parameters which need to be reset
  dblParam_[CbcCutoffIncrement] = 1e-5;
  dblParam_[CbcCurrentCutoff] = 1.0e100;
  dblParam_[CbcCurrentObjectiveValue] = 1.0e100;
  dblParam_[CbcCurrentMinimizationObjectiveValue] = 1.0e100;
}
// Save a copy of the current solver so can be reset to
void 
CbcModel::saveReferenceSolver()
{
  delete referenceSolver_;
  referenceSolver_= solver_->clone();
}

// Uses a copy of reference solver to be current solver
void 
CbcModel::resetToReferenceSolver()
{
  delete solver_;
  solver_ = referenceSolver_->clone();
  // clear many things
  gutsOfDestructor2();
  // Reset cutoff
  // Solvers know about direction
  double direction = solver_->getObjSense();
  double value;
  solver_->getDblParam(OsiDualObjectiveLimit,value); 
  setCutoff(value*direction);
}

// Are there a numerical difficulties?
bool 
CbcModel::isAbandoned() const
{
  return status_ == 2;
}
// Is optimality proven?
bool 
CbcModel::isProvenOptimal() const
{
  if (!status_ && bestObjective_<1.0e30)
    return true;
  else
    return false;
}
// Is  infeasiblity proven (or none better than cutoff)?
bool 
CbcModel::isProvenInfeasible() const
{
  if (!status_ && bestObjective_>=1.0e30)
    return true;
  else
    return false;
}
// Node limit reached?
bool 
CbcModel::isNodeLimitReached() const
{
  return numberNodes_ >= intParam_[CbcMaxNumNode];
}
// Time limit reached?
bool 
CbcModel::isSecondsLimitReached() const
{
  if (status_==1&&secondaryStatus_==4)
    return true;
  else
    return false;
}
// Solution limit reached?
bool 
CbcModel::isSolutionLimitReached() const
{
  return numberSolutions_ >= intParam_[CbcMaxNumSol];
}
// Set language
void 
CbcModel::newLanguage(CoinMessages::Language language)
{
  messages_ = CbcMessage(language);
}
void 
CbcModel::setNumberStrong(int number)
{
  if (number<0)
    numberStrong_=0;
   else
    numberStrong_=number;
}
void 
CbcModel::setNumberBeforeTrust(int number)
{
  if (number<-1) {
    numberBeforeTrust_=0;
  } else {
    numberBeforeTrust_=number;
    //numberStrong_ = CoinMax(numberStrong_,1);
  }
}
void 
CbcModel::setNumberPenalties(int number)
{
  if (number<=0) {
    numberPenalties_=0;
  } else {
    numberPenalties_=number;
  }
}
void 
CbcModel::setPenaltyScaleFactor(double value)
{
  if (value<=0) {
    penaltyScaleFactor_=3.0;
  } else {
    penaltyScaleFactor_=value;
  }
}
void 
CbcModel::setHowOftenGlobalScan(int number)
{
  if (number<-1)
    howOftenGlobalScan_=0;
   else
    howOftenGlobalScan_=number;
}

// Add one generator
void 
CbcModel::addCutGenerator(CglCutGenerator * generator,
                          int howOften, const char * name,
                          bool normal, bool atSolution,
                          bool whenInfeasible,int howOftenInSub,
                          int whatDepth, int whatDepthInSub)
{
  CbcCutGenerator ** temp = generator_;
  generator_ = new CbcCutGenerator * [numberCutGenerators_+1];
  memcpy(generator_,temp,numberCutGenerators_*sizeof(CbcCutGenerator *));
  delete[] temp ;
  generator_[numberCutGenerators_]= 
    new CbcCutGenerator(this,generator, howOften, name,
                        normal,atSolution,whenInfeasible,howOftenInSub,
                        whatDepth, whatDepthInSub);
  // and before any cahnges
  temp = virginGenerator_;
  virginGenerator_ = new CbcCutGenerator * [numberCutGenerators_+1];
  memcpy(virginGenerator_,temp,numberCutGenerators_*sizeof(CbcCutGenerator *));
  delete[] temp ;
  virginGenerator_[numberCutGenerators_++]= 
    new CbcCutGenerator(this,generator, howOften, name,
                        normal,atSolution,whenInfeasible,howOftenInSub,
                        whatDepth, whatDepthInSub);
                                                          
}
// Add one heuristic
void 
CbcModel::addHeuristic(CbcHeuristic * generator)
{
  CbcHeuristic ** temp = heuristic_;
  heuristic_ = new CbcHeuristic * [numberHeuristics_+1];
  memcpy(heuristic_,temp,numberHeuristics_*sizeof(CbcHeuristic *));
  delete [] temp;
  heuristic_[numberHeuristics_++]=generator->clone();
}

/*
  The last subproblem handled by the solver is not necessarily related to the
  one being recreated, so the first action is to remove all cuts from the
  constraint system.  Next, traverse the tree from node to the root to
  determine the basis size required for this subproblem and create an empty
  basis with the right capacity.  Finally, traverse the tree from root to
  node, adjusting bounds in the constraint system, adjusting the basis, and
  collecting the cuts that must be added to the constraint system.
  applyToModel does the heavy lifting.

  addCuts1 is used in contexts where all that's desired is the list of cuts:
  the node is already fathomed, and we're collecting cuts so that we can
  adjust reference counts as we prune nodes. Arguably the two functions
  should be separated. The culprit is applyToModel, which performs cut
  collection and model adjustment.

  Certainly in the contexts where all we need is a list of cuts, there's no
  point in passing in a valid basis --- an empty basis will do just fine.
*/
void CbcModel::addCuts1 (CbcNode * node, CoinWarmStartBasis *&lastws)
{ int i;
  int nNode=0;
  int numberColumns = getNumCols();
  CbcNodeInfo * nodeInfo = node->nodeInfo();

/*
  Remove all cuts from the constraint system.
  (original comment includes ``see note below for later efficiency'', but
  the reference isn't clear to me).
*/
  int currentNumberCuts = solver_->getNumRows()-numberRowsAtContinuous_;
  int *which = new int[currentNumberCuts];
  for (i = 0 ; i < currentNumberCuts ; i++)
    which[i] = i+numberRowsAtContinuous_;
  solver_->deleteRows(currentNumberCuts,which);
  delete [] which;
/*
  Accumulate the path from node to the root in walkback_, and accumulate a
  cut count in currentNumberCuts.

  original comment: when working then just unwind until where new node joins
  old node (for cuts?)
*/
  currentNumberCuts = 0;
  while (nodeInfo) {
    //printf("nNode = %d, nodeInfo = %x\n",nNode,nodeInfo);
    walkback_[nNode++]=nodeInfo;
    currentNumberCuts += nodeInfo->numberCuts() ;
    nodeInfo = nodeInfo->parent() ;
    if (nNode==maximumDepth_) {
      maximumDepth_ *= 2;
      CbcNodeInfo ** temp = new CbcNodeInfo * [maximumDepth_];
      for (i=0;i<nNode;i++) 
        temp[i] = walkback_[i];
      delete [] walkback_;
      walkback_ = temp;
    }
  }
/*
  Create an empty basis with sufficient capacity for the constraint system
  we'll construct: original system plus cuts. Make sure we have capacity to
  record those cuts in addedCuts_.

  The method of adjusting the basis at a FullNodeInfo object (the root, for
  example) is to use a copy constructor to duplicate the basis held in the
  nodeInfo, then resize it and return the new basis object. Guaranteed,
  lastws will point to a different basis when it returns. We pass in a basis
  because we need the parameter to return the allocated basis, and it's an
  easy way to pass in the size. But we take a hit for memory allocation.
*/
  currentNumberCuts_=currentNumberCuts;
  if (currentNumberCuts >= maximumNumberCuts_) {
    maximumNumberCuts_ = currentNumberCuts;
    delete [] addedCuts_;
    addedCuts_ = new CbcCountRowCut * [maximumNumberCuts_];
  }
  lastws->setSize(numberColumns,numberRowsAtContinuous_+currentNumberCuts);
/*
  This last bit of code traverses the path collected in walkback_ from the
  root back to node. At the end of the loop,
   * lastws will be an appropriate basis for node;
   * variable bounds in the constraint system will be set to be correct for
     node; and
   * addedCuts_ will be set to a list of cuts that need to be added to the
     constraint system at node.
  applyToModel does all the heavy lifting.
*/
  currentNumberCuts=0;
  while (nNode) {
    --nNode;
    walkback_[nNode]->applyToModel(this,lastws,addedCuts_,currentNumberCuts);
  }
}

/*
  adjustCuts might be a better name: If the node is feasible, we sift through
  the cuts collected by addCuts1, add the ones that are tight and omit the
  ones that are loose. If the node is infeasible, we just adjust the
  reference counts to reflect that we're about to prune this node and its
  descendants.
*/
int CbcModel::addCuts (CbcNode *node, CoinWarmStartBasis *&lastws,bool canFix)
{
/*
  addCuts1 performs step 1 of restoring the subproblem at this node; see the
  comments there.
*/
  addCuts1(node,lastws);
  int i;
  int numberColumns = getNumCols();
  CbcNodeInfo * nodeInfo = node->nodeInfo();
  double cutoff = getCutoff() ;
  int currentNumberCuts=currentNumberCuts_;
  if (canFix) {
    bool feasible=true;
    const double *lower = solver_->getColLower() ;
    const double *upper = solver_->getColUpper() ;
    double * newLower = analyzeResults_;
    double * objLower = newLower+numberIntegers_;
    double * newUpper = objLower+numberIntegers_;
    double * objUpper = newUpper+numberIntegers_;
    int n=0;
    for (i=0;i<numberIntegers_;i++) {
      int iColumn = integerVariable_[i];
      bool changed=false;
      double lo = 0.0;
      double up = 0.0;
      if (objLower[i]>cutoff) {
        lo = lower[iColumn];
        up = upper[iColumn];
        if (lo<newLower[i]) {
          lo = newLower[i];
          solver_->setColLower(iColumn,lo);
          changed=true;
          n++;
        }
        if (objUpper[i]>cutoff) {
          if (up>newUpper[i]) {
            up = newUpper[i];
            solver_->setColUpper(iColumn,up);
            changed=true;
            n++;
          }
        }
      } else if (objUpper[i]>cutoff) {
        lo = lower[iColumn];
        up = upper[iColumn];
        if (up>newUpper[i]) {
          up = newUpper[i];
          solver_->setColUpper(iColumn,up);
          changed=true;
          n++;
        }
      }
      if (changed&&lo>up) {
        feasible=false;
        break;
      }
    }
    if (!feasible) {
      printf("analysis says node infeas\n");
      cutoff=-COIN_DBL_MAX;
    }
  }
/*
  If the node can't be fathomed by bound, reinstall tight cuts in the
  constraint system. Even if there are no cuts, we'll want to set the
  reconstructed basis in the solver.
*/
  if (node->objectiveValue() < cutoff)
  { 
#   ifdef CBC_CHECK_BASIS
    printf("addCuts: expanded basis; rows %d+%d\n",
           numberRowsAtContinuous_,currentNumberCuts);
    lastws->print();
#   endif
/*
  Adjust the basis and constraint system so that we retain only active cuts.
  There are three steps:
    1) Scan the basis. Sort the cuts into effective cuts to be kept and
       loose cuts to be dropped.
    2) Drop the loose cuts and resize the basis to fit.
    3) Install the tight cuts in the constraint system (applyRowCuts) and
       and install the basis (setWarmStart).
  Use of compressRows conveys we're compressing the basis and not just
  tweaking the artificialStatus_ array.
*/
    if (currentNumberCuts > 0) {
      int numberToAdd = 0;
      const OsiRowCut **addCuts;
      int numberToDrop = 0 ;
      int *cutsToDrop ;
      addCuts = new const OsiRowCut* [currentNumberCuts];
      cutsToDrop = new int[currentNumberCuts] ;
      assert (currentNumberCuts+numberRowsAtContinuous_<=lastws->getNumArtificial());
      for (i=0;i<currentNumberCuts;i++) {
        CoinWarmStartBasis::Status status = 
          lastws->getArtifStatus(i+numberRowsAtContinuous_);
        if (addedCuts_[i] &&
            (status != CoinWarmStartBasis::basic ||
             addedCuts_[i]->effectiveness()==COIN_DBL_MAX)) {
#         ifdef CHECK_CUT_COUNTS
          printf("Using cut %d %x as row %d\n",i,addedCuts_[i],
                 numberRowsAtContinuous_+numberToAdd);
#         endif
          addCuts[numberToAdd++] = new OsiRowCut(*addedCuts_[i]);
        } else {
#         ifdef CHECK_CUT_COUNTS
          printf("Dropping cut %d %x\n",i,addedCuts_[i]);
#         endif
          addedCuts_[i]=NULL;
          cutsToDrop[numberToDrop++] = numberRowsAtContinuous_+i ;
        }
      }
      int numberRowsNow=numberRowsAtContinuous_+numberToAdd;
      lastws->compressRows(numberToDrop,cutsToDrop) ;
      lastws->resize(numberRowsNow,numberColumns);
      solver_->applyRowCuts(numberToAdd,addCuts);
#     ifdef CBC_CHECK_BASIS
      printf("addCuts: stripped basis; rows %d + %d\n",
             numberRowsAtContinuous_,numberToAdd);
      lastws->print();
#     endif
      for (i=0;i<numberToAdd;i++)
        delete addCuts[i];
      delete [] addCuts;
      delete [] cutsToDrop ;
    }
/*
  Set the basis in the solver.
*/
    solver_->setWarmStart(lastws);
#if 0
    if ((numberNodes_%printFrequency_)==0) {
      printf("Objective %g, depth %d, unsatisfied %d\n",
             node->objectiveValue(),
             node->depth(),node->numberUnsatisfied());
    }
#endif
/*
  Clean up and we're out of here.
*/
    numberNodes_++;
    return 0;
  } 
/*
  This node has been fathomed by bound as we try to revive it out of the live
  set. Adjust the cut reference counts to reflect that we no longer need to
  explore the remaining branch arms, hence they will no longer reference any
  cuts. Cuts whose reference count falls to zero are deleted.
*/
  else
  { int i;
    int numberLeft = nodeInfo->numberBranchesLeft();
    for (i = 0 ; i < currentNumberCuts ; i++)
    { if (addedCuts_[i])
      { if (!addedCuts_[i]->decrement(numberLeft))
        { delete addedCuts_[i];
          addedCuts_[i] = NULL; } } }
    return 1 ; }
}


/*
  Perform reduced cost fixing on integer variables.

  The variables in question are already nonbasic at bound. We're just nailing
  down the current situation.
*/

int CbcModel::reducedCostFix ()

{
  if(!solverCharacteristics_->reducedCostsAccurate())
    return 0; //NLP
  double cutoff = getCutoff() ;
  double direction = solver_->getObjSense() ;
  double gap = cutoff - solver_->getObjValue()*direction ;
  double tolerance;
  solver_->getDblParam(OsiDualTolerance,tolerance) ;
  if (gap<=0.0)
    return 0;
  gap += 100.0*tolerance;
  double integerTolerance = getDblParam(CbcIntegerTolerance) ;

  const double *lower = solver_->getColLower() ;
  const double *upper = solver_->getColUpper() ;
  const double *solution = solver_->getColSolution() ;
  const double *reducedCost = solver_->getReducedCost() ;

  int numberFixed = 0 ;

# ifdef COIN_HAS_CLP
  OsiClpSolverInterface * clpSolver 
    = dynamic_cast<OsiClpSolverInterface *> (solver_);
  ClpSimplex * clpSimplex=NULL;
  if (clpSolver) 
    clpSimplex = clpSolver->getModelPtr();
# endif
  for (int i = 0 ; i < numberIntegers_ ; i++)
  { int iColumn = integerVariable_[i] ;
    double djValue = direction*reducedCost[iColumn] ;
    if (upper[iColumn]-lower[iColumn] > integerTolerance)
    { if (solution[iColumn] < lower[iColumn]+integerTolerance && djValue > gap)
      { solver_->setColUpper(iColumn,lower[iColumn]) ;
#ifdef COIN_HAS_CLP
      // may just have been fixed before
      if (clpSimplex)
        assert(clpSimplex->getColumnStatus(iColumn)==ClpSimplex::atLowerBound||
               clpSimplex->getColumnStatus(iColumn)==ClpSimplex::isFixed);
#endif
      numberFixed++ ; }
      else
      if (solution[iColumn] > upper[iColumn]-integerTolerance && -djValue > gap)
      { solver_->setColLower(iColumn,upper[iColumn]) ;
#ifdef COIN_HAS_CLP
      // may just have been fixed before
      if (clpSimplex)
        assert(clpSimplex->getColumnStatus(iColumn)==ClpSimplex::atUpperBound||
               clpSimplex->getColumnStatus(iColumn)==ClpSimplex::isFixed);
#endif
      numberFixed++ ; } } }
  
  return numberFixed; }

// Collect coding to replace whichGenerator
void
CbcModel::resizeWhichGenerator(int numberNow, int numberAfter)
{
  if (numberAfter > maximumWhich_) {
    maximumWhich_ = CoinMax(maximumWhich_*2+100,numberAfter) ;
    int * temp = new int[2*maximumWhich_] ;
    memcpy(temp,whichGenerator_,numberNow*sizeof(int)) ;
    delete [] whichGenerator_ ;
    whichGenerator_ = temp ;
    memset(whichGenerator_+numberNow,0,(maximumWhich_-numberNow)*sizeof(int));
  }
}

/** Solve the model using cuts

  This version takes off redundant cuts from node.
  Returns true if feasible.

  \todo
  Why do I need to resolve the problem? What has been done between the last
  relaxation and calling solveWithCuts?

  If numberTries == 0 then user did not want any cuts.
*/

bool 
CbcModel::solveWithCuts (OsiCuts &cuts, int numberTries, CbcNode *node)
/*
  Parameters:
    numberTries: (i) the maximum number of iterations for this round of cut
                     generation; if negative then we don't mind if drop is tiny.
    
    cuts:       (o) all cuts generated in this round of cut generation

    node: (i)     So we can update dynamic pseudo costs
*/
                        

{
  bool feasible = true ;
  int lastNumberCuts = 0 ;
  double lastObjective = -1.0e100 ;
  int violated = 0 ;
  int numberRowsAtStart = solver_->getNumRows() ;
  //printf("solver had %d rows\n",numberRowsAtStart);
  int numberColumns = solver_->getNumCols() ;
  CoinBigIndex numberElementsAtStart = solver_->getNumElements();

  numberOldActiveCuts_ = numberRowsAtStart-numberRowsAtContinuous_ ;
  numberNewCuts_ = 0 ;

  bool onOptimalPath = false ;
  const OsiRowCutDebugger *debugger = NULL;
  if ((specialOptions_&1)!=0) {
    /*
      See OsiRowCutDebugger for details. In a nutshell, make sure that current
      variable values do not conflict with a known optimal solution. (Obviously
      this can be fooled when there are multiple solutions.)
    */
    debugger = solver_->getRowCutDebugger() ;
    if (debugger) 
      onOptimalPath = (debugger->onOptimalPath(*solver_)) ;
  }
  OsiCuts slackCuts;
/*
  Resolve the problem. If we've lost feasibility, might as well bail out right
  after the debug stuff. The resolve will also refresh cached copies of the
  solver solution (cbcColLower_, ...) held by CbcModel.
*/
  double objectiveValue = solver_->getObjValue()*solver_->getObjSense();
  if (node)
    objectiveValue= node->objectiveValue();
  int returnCode = resolve(node ? node->nodeInfo() : NULL,1);
  if (node&&!node->nodeInfo()->numberBranchesLeft())
    node->nodeInfo()->allBranchesGone(); // can clean up
  feasible = returnCode  != 0 ;
  if (returnCode<0)
    numberTries=0;
  if (problemFeasibility_->feasible(this,0)<0) {
    feasible=false; // pretend infeasible
  }
  
  // Update branching information if wanted
  if(node &&branchingMethod_)
    branchingMethod_->updateInformation(solver_,node);

#ifdef CBC_DEBUG
  if (feasible)
  { printf("Obj value %g (%s) %d rows\n",solver_->getObjValue(),
           (solver_->isProvenOptimal())?"proven":"unproven",
           solver_->getNumRows()) ; }
  
  else
  { printf("Infeasible %d rows\n",solver_->getNumRows()) ; }
#endif
  if ((specialOptions_&1)!=0) {
/*
  If the RowCutDebugger said we were compatible with the optimal solution,
  and now we're suddenly infeasible, we might be confused. Then again, we
  may have fathomed by bound, heading for a rediscovery of an optimal solution.
*/
    if (onOptimalPath && !solver_->isDualObjectiveLimitReached()) {
      if (!feasible) {
        solver_->writeMps("infeas");
        CoinWarmStartBasis *slack =
          dynamic_cast<CoinWarmStartBasis *>(solver_->getEmptyWarmStart()) ;
        solver_->setWarmStart(slack);
        delete slack ;
        solver_->setHintParam(OsiDoReducePrint,false,OsiHintDo,0) ;
        solver_->initialSolve();
      }
      assert(feasible) ;
    }
  }

  if (!feasible) {
    numberInfeasibleNodes_++;
    return (false) ;
  }
  sumChangeObjective1_ += solver_->getObjValue()*solver_->getObjSense()
    - objectiveValue ;
  if ( CoinCpuTime()-dblParam_[CbcStartSeconds] > dblParam_[CbcMaximumSeconds] )
    numberTries=0; // exit
  //if ((numberNodes_%100)==0)
  //printf("XXa sum obj changed by %g\n",sumChangeObjective1_);
  objectiveValue = solver_->getObjValue()*solver_->getObjSense();
  // Return at once if numberTries zero
  if (!numberTries) {
    cuts=OsiCuts();
    numberNewCuts_=0;
    return true;
  }
/*
  Do reduced cost fixing.
*/
  reducedCostFix() ;
/*
  Set up for at most numberTries rounds of cut generation. If numberTries is
  negative, we'll ignore the minimumDrop_ cutoff and keep generating cuts for
  the specified number of rounds.
*/
  double minimumDrop = minimumDrop_ ;
  if (numberTries<0)
  { numberTries = -numberTries ;
    minimumDrop = -1.0 ; }
/*
  Is it time to scan the cuts in order to remove redundant cuts? If so, set
  up to do it.
*/
# define SCANCUTS 100  
  int *countColumnCuts = NULL ;
  // Always accumulate row cut counts
  int * countRowCuts =new int[numberCutGenerators_+numberHeuristics_] ;
  memset(countRowCuts,0,
         (numberCutGenerators_+numberHeuristics_)*sizeof(int)) ;
  bool fullScan = false ;
  if ((numberNodes_%SCANCUTS) == 0)
  { fullScan = true ;
    countColumnCuts = new int[numberCutGenerators_+numberHeuristics_] ;
    memset(countColumnCuts,0,
           (numberCutGenerators_+numberHeuristics_)*sizeof(int)) ; }

  double direction = solver_->getObjSense() ;
  double startObjective = solver_->getObjValue()*direction ;

  currentPassNumber_ = 0 ;
  double primalTolerance = 1.0e-7 ;
/*
  Begin cut generation loop. Cuts generated during each iteration are
  collected in theseCuts. The loop can be divided into four phases:
   1) Prep: Fix variables using reduced cost. In the first iteration only,
      consider scanning globalCuts_ and activating any applicable cuts.
   2) Cut Generation: Call each generator and heuristic registered in the
      generator_ and heuristic_ arrays. Newly generated global cuts are
      copied to globalCuts_ at this time.
   3) Cut Installation and Reoptimisation: Install column and row cuts in
      the solver. Copy row cuts to cuts (parameter). Reoptimise.
   4) Cut Purging: takeOffCuts() removes inactive cuts from the solver, and
      does the necessary bookkeeping in the model.
*/
  do
  { currentPassNumber_++ ;
    numberTries-- ;
    OsiCuts theseCuts ;
/*
  Scan previously generated global column and row cuts to see if any are
  useful.
*/
    int numberViolated=0;
    if (currentPassNumber_ == 1 && howOftenGlobalScan_ > 0 &&
        (numberNodes_%howOftenGlobalScan_) == 0)
    { int numberCuts = globalCuts_.sizeColCuts() ;
      int i;
      // possibly extend whichGenerator
      resizeWhichGenerator(numberViolated, numberViolated+numberCuts);
      for ( i = 0 ; i < numberCuts ; i++)
      { const OsiColCut *thisCut = globalCuts_.colCutPtr(i) ;
        if (thisCut->violated(cbcColSolution_)>primalTolerance) {
          printf("Global cut added - violation %g\n",
                 thisCut->violated(cbcColSolution_)) ;
          whichGenerator_[numberViolated++]=-1;
          theseCuts.insert(*thisCut) ;
        }
      }
      numberCuts = globalCuts_.sizeRowCuts() ;
      // possibly extend whichGenerator
      resizeWhichGenerator(numberViolated, numberViolated+numberCuts);
      for ( i = 0;i<numberCuts;i++) {
        const OsiRowCut * thisCut = globalCuts_.rowCutPtr(i) ;
        if (thisCut->violated(cbcColSolution_)>primalTolerance) {
          //printf("Global cut added - violation %g\n",
          // thisCut->violated(cbcColSolution_)) ;
          whichGenerator_[numberViolated++]=-1;
          theseCuts.insert(*thisCut) ;
        }
      }
      numberGlobalViolations_+=numberViolated;
    }
/*
  Generate new cuts (global and/or local) and/or apply heuristics.  If
  CglProbing is used, then it should be first as it can fix continuous
  variables.

  At present, CglProbing is the only case where generateCuts will return
  true. generateCuts actually modifies variable bounds in the solver when
  CglProbing indicates that it can fix a variable. Reoptimisation is required
  to take full advantage.

  The need to resolve here should only happen after a heuristic solution.
  (Note default OSI implementation of optimalBasisIsAvailable always returns
  false.)
*/
    if (solverCharacteristics_->warmStart()&&
        !solver_->optimalBasisIsAvailable()) {
      //printf("XXXXYY no opt basis\n");
      resolve(node ? node->nodeInfo() : NULL,3);
    }
    if (nextRowCut_) {
      // branch was a cut - add it
      theseCuts.insert(*nextRowCut_);
      if (handler_->logLevel()>1)
        nextRowCut_->print();
      const OsiRowCut * cut=nextRowCut_;
      double lb = cut->lb();
      double ub = cut->ub();
      int n=cut->row().getNumElements();
      const int * column = cut->row().getIndices();
      const double * element = cut->row().getElements();
      double sum=0.0;
      for (int i=0;i<n;i++) {
        int iColumn = column[i];
        double value = element[i];
        //if (cbcColSolution_[iColumn]>1.0e-7)
        //printf("value of %d is %g\n",iColumn,cbcColSolution_[iColumn]);
        sum += value * cbcColSolution_[iColumn];
      }
      delete nextRowCut_;
      nextRowCut_=NULL;
      if (handler_->logLevel()>1)
        printf("applying branch cut, sum is %g, bounds %g %g\n",sum,lb,ub);
      // possibly extend whichGenerator
      resizeWhichGenerator(numberViolated, numberViolated+1);
      // set whichgenerator (also serves as marker to say don't delete0
      whichGenerator_[numberViolated++]=-2;
    }
    double * newSolution = new double [numberColumns] ;
    double heuristicValue = getCutoff() ;
    int found = -1; // no solution found

    for (int i = 0;i<numberCutGenerators_+numberHeuristics_;i++) {
      int numberRowCutsBefore = theseCuts.sizeRowCuts() ;
      int numberColumnCutsBefore = theseCuts.sizeColCuts() ;
      if (i<numberCutGenerators_) {
        bool generate = generator_[i]->normal();
        // skip if not optimal and should be (maybe a cut generator has fixed variables)
        if (generator_[i]->needsOptimalBasis()&&!solver_->basisIsAvailable())
          generate=false;
        if (generate) {
          bool mustResolve = 
            generator_[i]->generateCuts(theseCuts,fullScan,node) ;
#ifdef CBC_DEBUG
          {
            int numberRowCutsAfter = theseCuts.sizeRowCuts() ;
            int k ;
            for (k = numberRowCutsBefore;k<numberRowCutsAfter;k++) {
              OsiRowCut thisCut = theseCuts.rowCut(k) ;
              /* check size of elements.
                 We can allow smaller but this helps debug generators as it
                 is unsafe to have small elements */
              int n=thisCut.row().getNumElements();
              const int * column = thisCut.row().getIndices();
              const double * element = thisCut.row().getElements();
              //assert (n);
              for (int i=0;i<n;i++) {
                double value = element[i];
                assert(fabs(value)>1.0e-12&&fabs(value)<1.0e20);
              }
            }
          }
#endif
          if (mustResolve) {
            int returncode = resolve(node ? node->nodeInfo() : NULL,2);
            feasible = returnCode  != 0 ;
            if (returncode<0)
              numberTries=0;
            if ((specialOptions_&1)!=0) {
              debugger = solver_->getRowCutDebugger() ;
              if (debugger) 
                onOptimalPath = (debugger->onOptimalPath(*solver_)) ;
              else
                onOptimalPath=false;
              if (onOptimalPath && !solver_->isDualObjectiveLimitReached())
                assert(feasible) ;
            }
            if (!feasible)
              break ;
          }
        }
      } else {
        // see if heuristic will do anything
        double saveValue = heuristicValue ;
        int ifSol = 
          heuristic_[i-numberCutGenerators_]->solution(heuristicValue,
                                                       newSolution,
                                                       theseCuts) ;
        if (ifSol>0) {
          // better solution found
          found = i-numberCutGenerators_ ;
          incrementUsed(newSolution);
        } else if (ifSol<0) {
          heuristicValue = saveValue ;
        }
      }
      int numberRowCutsAfter = theseCuts.sizeRowCuts() ;
      int numberColumnCutsAfter = theseCuts.sizeColCuts() ;

      if ((specialOptions_&1)!=0) {
        if (onOptimalPath) {
          int k ;
          for (k = numberRowCutsBefore;k<numberRowCutsAfter;k++) {
            OsiRowCut thisCut = theseCuts.rowCut(k) ;
            if(debugger->invalidCut(thisCut)) {
              solver_->writeMps("badCut");
            }
            assert(!debugger->invalidCut(thisCut)) ;
          }
        }
      }
/*
  The cut generator/heuristic has done its thing, and maybe it generated some
  cuts and/or a new solution.  Do a bit of bookkeeping: load
  whichGenerator[i] with the index of the generator responsible for a cut,
  and place cuts flagged as global in the global cut pool for the model.

  lastNumberCuts is the sum of cuts added in previous iterations; it's the
  offset to the proper starting position in whichGenerator.
*/
      int numberBefore =
            numberRowCutsBefore+numberColumnCutsBefore+lastNumberCuts ;
      int numberAfter =
            numberRowCutsAfter+numberColumnCutsAfter+lastNumberCuts ;
      // possibly extend whichGenerator
      resizeWhichGenerator(numberBefore, numberAfter);
      int j ;
      if (fullScan) {
        // counts
        countColumnCuts[i] += numberColumnCutsAfter-numberColumnCutsBefore ;
      }
      countRowCuts[i] += numberRowCutsAfter-numberRowCutsBefore ;
      
      for (j = numberRowCutsBefore;j<numberRowCutsAfter;j++) {
        whichGenerator_[numberBefore++] = i ;
        const OsiRowCut * thisCut = theseCuts.rowCutPtr(j) ;
        if (thisCut->lb()>thisCut->ub())
          violated=-2; // sub-problem is infeasible
        if (thisCut->globallyValid()) {
          // add to global list
          globalCuts_.insert(*thisCut) ;
        }
      }
      for (j = numberColumnCutsBefore;j<numberColumnCutsAfter;j++) {
        whichGenerator_[numberBefore++] = i ;
        const OsiColCut * thisCut = theseCuts.colCutPtr(j) ;
        if (thisCut->globallyValid()) {
          // add to global list
          globalCuts_.insert(*thisCut) ;
        }
      }
    }
    // If at root node and first pass do heuristics without cuts
    if (!numberNodes_&&currentPassNumber_==1) {
      // Save number solutions
      int saveNumberSolutions = numberSolutions_;
      int saveNumberHeuristicSolutions = numberHeuristicSolutions_;
      for (int i = 0;i<numberHeuristics_;i++) {
        // see if heuristic will do anything
        double saveValue = heuristicValue ;
        int ifSol = heuristic_[i]->solution(heuristicValue,
                                            newSolution);
        if (ifSol>0) {
          // better solution found
          found = i ;
          incrementUsed(newSolution);
          // increment number of solutions so other heuristics can test
          numberSolutions_++;
          numberHeuristicSolutions_++;
        } else {
          heuristicValue = saveValue ;
        }
      }
      // Restore number solutions
      numberSolutions_ = saveNumberSolutions;
      numberHeuristicSolutions_ = saveNumberHeuristicSolutions;
    }
    // Add in any violated saved cuts
    if (!theseCuts.sizeRowCuts()&&!theseCuts.sizeColCuts()) {
      int numberOld = theseCuts.sizeRowCuts();
      int numberCuts = slackCuts.sizeRowCuts() ;
      int i;
      // possibly extend whichGenerator
      resizeWhichGenerator(numberOld, numberOld+numberCuts);
      for ( i = 0;i<numberCuts;i++) {
        const OsiRowCut * thisCut = slackCuts.rowCutPtr(i) ;
        if (thisCut->violated(cbcColSolution_)>100.0*primalTolerance) {
          if (messageHandler()->logLevel()>2)
            printf("Old cut added - violation %g\n",
                   thisCut->violated(cbcColSolution_)) ;
          whichGenerator_[numberOld++]=-1;
          theseCuts.insert(*thisCut) ;
        }
      }
    }
/*
  End of the loop to exercise each generator/heuristic.

  Did any of the heuristics turn up a new solution? Record it before we free
  the vector.
*/
    if (found >= 0) { 
      phase_=4;
      incrementUsed(newSolution);
      setBestSolution(CBC_ROUNDING,heuristicValue,newSolution) ;
      lastHeuristic_ = heuristic_[found];
      CbcTreeLocal * tree 
          = dynamic_cast<CbcTreeLocal *> (tree_);
      if (tree)
        tree->passInSolution(bestSolution_,heuristicValue);
    }
    delete [] newSolution ;

#if 0
    // switch on to get all cuts printed
    theseCuts.printCuts() ;
#endif
    int numberColumnCuts = theseCuts.sizeColCuts() ;
    int numberRowCuts = theseCuts.sizeRowCuts() ;
    if (violated>=0)
      violated = numberRowCuts + numberColumnCuts ;
/*
  Apply column cuts (aka bound tightening). This may be partially redundant
  for column cuts returned by CglProbing, as generateCuts installs bounds
  from CglProbing when it determines it can fix a variable.

  TODO: Looks like the use of violated has evolved. The value set above is
        completely ignored. All that's left is violated == -1 indicates some
        cut is violated, violated == -2 indicates infeasibility. Only
        infeasibility warrants exceptional action.

  TODO: Strikes me that this code will fail to detect infeasibility, because
        the breaks escape the inner loops but the outer loop keeps going.
        Infeasibility in an early cut will be overwritten if a later cut is
        merely violated.
*/
    if (numberColumnCuts) {

#ifdef CBC_DEBUG
      double * oldLower = new double [numberColumns] ;
      double * oldUpper = new double [numberColumns] ;
      memcpy(oldLower,cbcColLower_,numberColumns*sizeof(double)) ;
      memcpy(oldUpper,cbcColUpper_,numberColumns*sizeof(double)) ;
#endif

      double integerTolerance = getDblParam(CbcIntegerTolerance) ;
      for (int i = 0;i<numberColumnCuts;i++) {
        const OsiColCut * thisCut = theseCuts.colCutPtr(i) ;
        const CoinPackedVector & lbs = thisCut->lbs() ;
        const CoinPackedVector & ubs = thisCut->ubs() ;
        int j ;
        int n ;
        const int * which ;
        const double * values ;
        n = lbs.getNumElements() ;
        which = lbs.getIndices() ;
        values = lbs.getElements() ;
        for (j = 0;j<n;j++) {
          int iColumn = which[j] ;
          double value = cbcColSolution_[iColumn] ;
#if CBC_DEBUG>1
          printf("%d %g %g %g %g\n",iColumn,oldLower[iColumn],
                 cbcColSolution_[iColumn],oldUpper[iColumn],values[j]) ;
#endif
          solver_->setColLower(iColumn,values[j]) ;
          if (value<values[j]-integerTolerance)
            violated = -1 ;
          if (values[j]>cbcColUpper_[iColumn]+integerTolerance) {
            // infeasible
            violated = -2 ;
            break ;
          }
        }
        n = ubs.getNumElements() ;
        which = ubs.getIndices() ;
        values = ubs.getElements() ;
        for (j = 0;j<n;j++) {
          int iColumn = which[j] ;
          double value = cbcColSolution_[iColumn] ;
#if CBC_DEBUG>1
          printf("%d %g %g %g %g\n",iColumn,oldLower[iColumn],
                 cbcColSolution_[iColumn],oldUpper[iColumn],values[j]) ;
#endif
          solver_->setColUpper(iColumn,values[j]) ;
          if (value>values[j]+integerTolerance)
            violated = -1 ;
          if (values[j]<cbcColLower_[iColumn]-integerTolerance) {
            // infeasible
            violated = -2 ;
            break ;
          }
        }
      }
#ifdef CBC_DEBUG
      delete [] oldLower ;
      delete [] oldUpper ;
#endif
    }
/*
  End installation of column cuts. The break here escapes the numberTries
  loop.
*/
    if (violated == -2||!feasible) {
      // infeasible
      feasible = false ;
      violated = -2;
      if (!numberNodes_) 
        messageHandler()->message(CBC_INFEAS,
                                  messages())
                                    << CoinMessageEol ;
      break ;
    }
/*
  Now apply the row (constraint) cuts. This is a bit more work because we need
  to obtain and augment the current basis.

  TODO: Why do this work, if there are no row cuts? The current basis will do
        just fine.
*/
    int numberRowsNow = solver_->getNumRows() ;
    assert(numberRowsNow == numberRowsAtStart+lastNumberCuts) ;
    int numberToAdd = theseCuts.sizeRowCuts() ;
    numberNewCuts_ = lastNumberCuts+numberToAdd ;
/*
  Now actually add the row cuts and reoptimise.

  Install the cuts in the solver using applyRowCuts and
  augment the basis with the corresponding slack. We also add each row cut to
  the set of row cuts (cuts.insert()) supplied as a parameter. The new basis
  must be set with setWarmStart().

  TODO: Seems to me the original code could allocate addCuts with size 0, if
        numberRowCuts was 0 and numberColumnCuts was nonzero. That might
        explain the memory fault noted in the comment by AJK.  Unfortunately,
        just commenting out the delete[] results in massive memory leaks. Try
        a revision to separate the row cut case. Why do we need addCuts at
        all? A typing issue, apparently: OsiCut vs. OsiRowCut.

  TODO: It looks to me as if numberToAdd and numberRowCuts are identical at
        this point. Confirm & get rid of one of them.

  TODO: Any reason why the three loops can't be consolidated?
*/
    if (numberRowCuts > 0 || numberColumnCuts > 0)
    { if (numberToAdd > 0)
      { int i ;
        // Faster to add all at once
        const OsiRowCut ** addCuts = new const OsiRowCut * [numberToAdd] ;
        for (i = 0 ; i < numberToAdd ; i++)
        { addCuts[i] = &theseCuts.rowCut(i) ; }
        solver_->applyRowCuts(numberToAdd,addCuts) ;
        // AJK this caused a memory fault on Win32
        // may be okay now with ** form
        delete [] addCuts ;
        for (i = 0 ; i < numberToAdd ; i++)
        { cuts.insert(theseCuts.rowCut(i)) ; }
        CoinWarmStartBasis * basis = dynamic_cast<CoinWarmStartBasis*>(solver_->getWarmStart()) ;
        assert(basis != NULL); // make sure not volume
/* dylp bug

  Consistent size used by OsiDylp as sanity check. Implicit resize seen
  as an error. Hence this call to resize is necessary.
*/
        basis->resize(numberRowsAtStart+numberNewCuts_,numberColumns) ;
        for (i = 0 ; i < numberToAdd ; i++) 
        { basis->setArtifStatus(numberRowsNow+i,
                                 CoinWarmStartBasis::basic) ; }
        if (solver_->setWarmStart(basis) == false)
        { throw CoinError("Fail setWarmStart() after cut installation.",
                          "solveWithCuts","CbcModel") ; }
        delete basis;
      }
      feasible = resolve(node ? node->nodeInfo() : NULL,2) ;
      if ( CoinCpuTime()-dblParam_[CbcStartSeconds] > dblParam_[CbcMaximumSeconds] )
        numberTries=0; // exit
#     ifdef CBC_DEBUG
      printf("Obj value after cuts %g %d rows\n",solver_->getObjValue(),
              solver_->getNumRows()) ;
      if (onOptimalPath && !solver_->isDualObjectiveLimitReached())
        assert(feasible) ;
#     endif
    }
/*
  No cuts. Cut short the cut generation (numberTries) loop.
*/
    else
    { numberTries = 0 ; }
/*
  If the problem is still feasible, first, call takeOffCuts() to remove cuts
  that are now slack. takeOffCuts() will call the solver to reoptimise if
  that's needed to restore a valid solution.

  Next, see if we should quit due to diminishing returns:
    * we've tried three rounds of cut generation and we're getting
      insufficient improvement in the objective; or
    * we generated no cuts; or
    * the solver declared optimality with 0 iterations after we added the
      cuts generated in this round.
  If we decide to keep going, prep for the next iteration.

  It just seems more safe to tell takeOffCuts() to call resolve(), even if
  we're not continuing cut generation. Otherwise code executed between here
  and final disposition of the node will need to be careful not to access the
  lp solution. It can happen that we lose feasibility in takeOffCuts ---
  numerical jitters when the cutoff bound is epsilon less than the current
  best, and we're evaluating an alternative optimum.

  TODO: After successive rounds of code motion, there seems no need to
        distinguish between the two checks for aborting the cut generation
        loop. Confirm and clean up.
*/
    if (feasible)
    { int cutIterations = solver_->getIterationCount() ;
      if (numberOldActiveCuts_+numberNewCuts_) {
        OsiCuts * saveCuts = node ? NULL : &slackCuts;
        takeOffCuts(cuts,resolveAfterTakeOffCuts_,saveCuts) ;
        if (solver_->isDualObjectiveLimitReached()&&resolveAfterTakeOffCuts_)
          { feasible = false ;
#       ifdef CBC_DEBUG
          double z = solver_->getObjValue() ;
          double cut = getCutoff() ;
          printf("Lost feasibility by %g in takeOffCuts; z = %g, cutoff = %g\n",
                 z-cut,z,cut) ;
#       endif
          }
      }
      if (feasible) {
        numberRowsAtStart = numberOldActiveCuts_+numberRowsAtContinuous_ ;
        lastNumberCuts = numberNewCuts_ ;
        if (direction*solver_->getObjValue() < lastObjective+minimumDrop &&
            currentPassNumber_ >= 3)
          { numberTries = 0 ; }
        if (numberRowCuts+numberColumnCuts == 0 || cutIterations == 0)
          { break ; }
        if (numberTries > 0) {
          reducedCostFix() ;
          lastObjective = direction*solver_->getObjValue() ;
        }
      }
    }
/*
  We've lost feasibility --- this node won't be referencing the cuts we've
  been collecting, so decrement the reference counts.
*/
    if (!feasible)
    { int i ;
      for (i = 0;i<currentNumberCuts_;i++) {
        // take off node
        if (addedCuts_[i]) {
          if (!addedCuts_[i]->decrement())
            delete addedCuts_[i] ;
          addedCuts_[i] = NULL ;
        }
      }
      numberTries = 0 ;
    }
  } while (numberTries>0) ;
  //check feasibility.
  //If solution seems to be integer feasible calling setBestSolution
  //will eventually add extra global cuts which we need to install at
  //the nodes


  if(feasible&&solverCharacteristics_->solutionAddsCuts())  //check integer feasibility
  {
    bool integerFeasible = true;
    const double * save = testSolution_;
    testSolution_ = solver_->getColSolution();
    for (int i=0;i<numberObjects_ && integerFeasible;i++)
    {
      int preferredWay;
      double infeasibility = object_[i]->infeasibility(preferredWay);
      if(infeasibility)
        integerFeasible = false;
    }
    testSolution_ = save;
    if(integerFeasible)//update
    {
      double objValue = solver_->getObjValue();
      int numberGlobalBefore = globalCuts_.sizeRowCuts();
      // SOLUTION2 so won't up cutoff or print message
      setBestSolution(CBC_SOLUTION2, objValue, 
                      solver_->getColSolution(),0);
      int numberGlobalAfter = globalCuts_.sizeRowCuts();
      int numberToAdd = numberGlobalAfter - numberGlobalBefore;
      if(numberToAdd > 0)
        //We have added some cuts say they are tight at that node
        //Basis and lp should already have been updated
      {
        feasible = (solver_->isProvenOptimal() &&
                    !solver_->isDualObjectiveLimitReached()) ;
        if(feasible)
        {
          int numberCuts = numberNewCuts_ = cuts.sizeRowCuts();
      // possibly extend whichGenerator
          resizeWhichGenerator(numberCuts, numberToAdd+numberCuts);
          
          for (int i = numberGlobalBefore ; i < numberGlobalAfter ; i++)
            {        
              whichGenerator_[numberNewCuts_++]=-1;
              cuts.insert(globalCuts_.rowCut(i)) ; 
            }
          numberNewCuts_ = lastNumberCuts+numberToAdd;
          //now take off the cuts which are not tight anymore
          takeOffCuts(cuts,resolveAfterTakeOffCuts_, NULL) ;
          if (solver_->isDualObjectiveLimitReached()&&resolveAfterTakeOffCuts_)
            { feasible = false ;}
        }
        if(!feasible) //node will be fathomed
          { 
          for (int i = 0;i<currentNumberCuts_;i++) 
            {
            // take off node
            if (addedCuts_[i])
              {
                if (!addedCuts_[i]->decrement())
                  delete addedCuts_[i] ;
                addedCuts_[i] = NULL ;
              }
            }
        }
      }
    }
  }
  // Reduced cost fix at end
  if (solver_->isProvenOptimal())
    reducedCostFix();
  // If at root node do heuristics
  if (!numberNodes_) {
    // First see if any cuts are slack
    int numberRows = solver_->getNumRows();
    int numberAdded = numberRows-numberRowsAtContinuous_;
    if (numberAdded) {
      CoinWarmStartBasis * basis = dynamic_cast<CoinWarmStartBasis*>(solver_->getWarmStart()) ;
      assert(basis != NULL);
      int * added = new int[numberAdded];
      int nDelete = 0;
      for (int j=numberRowsAtContinuous_;j<numberRows;j++) {
        if (basis->getArtifStatus(j)==CoinWarmStartBasis::basic) {
          //printf("%d slack!\n",j);
          added[nDelete++]=j;
        }
      }
      if (nDelete) {
        solver_->deleteRows(nDelete,added);
      }
      delete [] added;
      delete basis ;
    }
    // mark so heuristics can tell
    int savePass=currentPassNumber_;
    currentPassNumber_=999999;
    double * newSolution = new double [numberColumns] ;
    double heuristicValue = getCutoff() ;
    int found = -1; // no solution found
    for (int i = 0;i<numberHeuristics_;i++) {
      // see if heuristic will do anything
      double saveValue = heuristicValue ;
      int ifSol = heuristic_[i]->solution(heuristicValue,
                                          newSolution);
      if (ifSol>0) {
        // better solution found
        found = i ;
        incrementUsed(newSolution);
      } else {
        heuristicValue = saveValue ;
      }
    }
    currentPassNumber_=savePass;
    if (found >= 0) { 
      phase_=4;
      incrementUsed(newSolution);
      setBestSolution(CBC_ROUNDING,heuristicValue,newSolution) ;
      lastHeuristic_ = heuristic_[found];
    }
    delete [] newSolution ;
  }
  // Up change due to cuts
  if (feasible)
    sumChangeObjective2_ += solver_->getObjValue()*solver_->getObjSense()
      - objectiveValue ;
  //if ((numberNodes_%100)==0)
  //printf("XXb sum obj changed by %g\n",sumChangeObjective2_);
/*
  End of cut generation loop.

  Now, consider if we want to disable or adjust the frequency of use for any
  of the cut generators. If the client specified a positive number for
  howOften, it will never change. If the original value was negative, it'll
  be converted to 1000000+|howOften|, and this value will be adjusted each
  time fullScan is true. Actual cut generation is performed every
  howOften%1000000 nodes; the 1000000 offset is just a convenient way to
  specify that the frequency is adjustable.

  During cut generation, we recorded the number of cuts produced by each
  generator for this node. For all cuts, whichGenerator records the generator
  that produced a cut.

  TODO: All this should probably be hidden in a method of the CbcCutGenerator
  class.

  TODO: Can the loop that scans over whichGenerator to accumulate per generator
        counts be replaced by values in countRowCuts and countColumnCuts?
*/
#ifdef NODE_LOG
  int fatherNum = (node == NULL) ? -1 : node->nodeNumber();
  double value =  (node == NULL) ? -1 : node->branchingObject()->value();
  string bigOne = (solver_->getIterationCount() > 30) ? "*******" : "";
  string way = (node == NULL) ? "" : (node->branchingObject()->way())==1 ? "Down" : "Up";
  std::cout<<"Node "<<numberNodes_<<", father "<<fatherNum<<", #iterations "<<solver_->getIterationCount()<<", sol value : "<<solver_->getObjValue()<<std::endl;
#endif
  if (fullScan&&numberCutGenerators_) {
    /* If cuts just at root node then it will probably be faster to
       update matrix and leave all in */
    int willBeCutsInTree=0;
    double thisObjective = solver_->getObjValue()*direction ;
    // get sizes
    int numberRowsAdded = solver_->getNumRows()-numberRowsAtStart;
    CoinBigIndex numberElementsAdded =  solver_->getNumElements()-numberElementsAtStart ;
    double densityOld = ((double) numberElementsAtStart)/((double) numberRowsAtStart);
    double densityNew = numberRowsAdded ? ((double) (numberElementsAdded))/((double) numberRowsAdded)
      : 0.0;
    if (!numberNodes_) {
      if (numberRowsAdded)
        handler_->message(CBC_CUTS_STATS,messages_)
          <<numberRowsAdded
          <<densityNew
          <<CoinMessageEol ;
      if (thisObjective-startObjective<1.0e-5&&numberElementsAdded>0.2*numberElementsAtStart)
        willBeCutsInTree=-1;
    }
    if ((numberRowsAdded>100+0.5*numberRowsAtStart
         ||numberElementsAdded>0.5*numberElementsAtStart)
        &&(densityNew>200.0&&numberRowsAdded>100&&densityNew>2.0*densityOld)) {
      // much bigger
      //if (thisObjective-startObjective<0.1*fabs(startObjective)+1.0e-5)
      //willBeCutsInTree=-1;
      //printf("Cuts will be taken off , %d rows added with density %g\n",
      //     numberRowsAdded,densityNew);
    }
    if (densityNew>100.0&&numberRowsAdded>2&&densityNew>2.0*densityOld) {
      //if (thisObjective-startObjective<0.1*fabs(startObjective)+1.0e-5)
      //willBeCutsInTree=-2;
      //printf("Density says no cuts ? , %d rows added with density %g\n",
      //     numberRowsAdded,densityNew);
    }
    // Root node or every so often - see what to turn off
    int i ;
    for (i = 0;i<numberCutGenerators_;i++) {
      int howOften = generator_[i]->howOften() ;
      if (howOften>-90) 
        willBeCutsInTree=0;
    }
    if (!numberNodes_)
      handler_->message(CBC_ROOT,messages_)
        <<numberNewCuts_
        <<startObjective<<thisObjective
        <<currentPassNumber_
        <<CoinMessageEol ;
    int * count = new int[numberCutGenerators_] ;
    memset(count,0,numberCutGenerators_*sizeof(int)) ;
    int numberActiveGenerators=0;
    for (i = 0;i<numberNewCuts_;i++) {
      int iGenerator = whichGenerator_[i];
      if (iGenerator>=0&&iGenerator<numberCutGenerators_)
        count[iGenerator]++ ;
    }
    double totalCuts = 0.0 ;
    //#define JUST_ACTIVE
    for (i = 0;i<numberCutGenerators_;i++) {
      if (countRowCuts[i]||countColumnCuts[i])
        numberActiveGenerators++;
#ifdef JUST_ACTIVE
      totalCuts += count[i] + 5.0*countColumnCuts[i] ;
#else
      totalCuts += countRowCuts[i] + 5.0*countColumnCuts[i] ;
#endif
    }
    double small = (0.5* totalCuts) /
      ((double) numberActiveGenerators) ;
    for (i = 0;i<numberCutGenerators_;i++) {
      int howOften = generator_[i]->howOften() ;
      if (willBeCutsInTree<0&&howOften==-98)
        howOften =-99;
      if (howOften==-98&&generator_[i]->switchOffIfLessThan()<0) {
        if (thisObjective-startObjective<0.005*fabs(startObjective)+1.0e-5)
          howOften=-99; // switch off
        if (thisObjective-startObjective<0.1*fabs(startObjective)+1.0e-5
            &&5*solver_->getNumRows()<solver_->getNumCols())
          howOften=-99; // switch off
      }
      if (howOften<-99)
        continue ;
      if (howOften<0||howOften >= 1000000) {
        if( !numberNodes_) {
          // If small number switch mostly off
#ifdef JUST_ACTIVE
          double thisCuts = count[i] + 5.0*countColumnCuts[i] ;
#else
          double thisCuts = countRowCuts[i] + 5.0*countColumnCuts[i] ;
#endif
          if (!thisCuts||howOften == -99) {
            if (howOften == -99||howOften == -98) 
              howOften = -100 ;
            else
              howOften = 1000000+SCANCUTS; // wait until next time
          } else if (thisCuts<small) {
            int k = (int) sqrt(small/thisCuts) ;
            if (howOften!=-98)
              howOften = k+1000000 ;
            else
              howOften=-100;
          } else {
            howOften = 1+1000000 ;
            if (thisObjective-startObjective<0.1*fabs(startObjective)+1.0e-5)
              generator_[i]->setWhatDepth(5);
          }
        }
        // If cuts useless switch off
        if (numberNodes_>=100000&&sumChangeObjective1_>2.0e2*(sumChangeObjective2_+1.0e-12)) {
          howOften = 1000000+SCANCUTS; // wait until next time
          //printf("switch off cut %d due to lack of use\n",i);
        }
      }
      if (howOften>=0&&generator_[i]->generator()->mayGenerateRowCutsInTree())
        willBeCutsInTree=1;
        
      generator_[i]->setHowOften(howOften) ;
      if (howOften>=1000000&&howOften<2000000&&0) {
        // Go to depth
        int bias=1;
        if (howOften==1+1000000)
          generator_[i]->setWhatDepth(bias+1);
        else if (howOften<=10+1000000)
          generator_[i]->setWhatDepth(bias+2);
        else
          generator_[i]->setWhatDepth(bias+1000);
      }
      int newFrequency = generator_[i]->howOften()%1000000 ;
      // increment cut counts
      generator_[i]->incrementNumberCutsInTotal(countRowCuts[i]);
      generator_[i]->incrementNumberCutsActive(count[i]);
      if (handler_->logLevel()>1||!numberNodes_) {
        handler_->message(CBC_GENERATOR,messages_)
          <<i
          <<generator_[i]->cutGeneratorName()
          <<countRowCuts[i]<<count[i]
          <<countColumnCuts[i];
        handler_->printing(!numberNodes_&&generator_[i]->timing())
          <<generator_[i]->timeInCutGenerator();
        handler_->message()
          <<newFrequency
          <<CoinMessageEol ;
      }
    } 
    delete [] count ;
    if( !numberNodes_) {
      if (willBeCutsInTree==-2)
        willBeCutsInTree=0;
      if( willBeCutsInTree<=0) {
        // Take off cuts
        cuts = OsiCuts();
        numberNewCuts_=0;
        if (!willBeCutsInTree) {
          // update size of problem
          numberRowsAtContinuous_ = solver_->getNumRows() ;
        } else {
          // take off cuts
          int numberRows = solver_->getNumRows();
          int numberAdded = numberRows-numberRowsAtContinuous_;
          if (numberAdded) {
            int * added = new int[numberAdded];
            for (int i=0;i<numberAdded;i++)
              added[i]=i+numberRowsAtContinuous_;
            solver_->deleteRows(numberAdded,added);
            delete [] added;
            // resolve so optimal
            solver_->resolve();
          }
        }
#ifdef COIN_HAS_CLP
        OsiClpSolverInterface * clpSolver 
          = dynamic_cast<OsiClpSolverInterface *> (solver_);
        if (clpSolver) {
          // Maybe solver might like to know only column bounds will change
          //int options = clpSolver->specialOptions();
          //clpSolver->setSpecialOptions(options|128);
          clpSolver->synchronizeModel();
        }
#endif
      } else {
#ifdef COIN_HAS_CLP
        OsiClpSolverInterface * clpSolver 
          = dynamic_cast<OsiClpSolverInterface *> (solver_);
        if (clpSolver) {
        // make sure factorization can't carry over
          int options = clpSolver->specialOptions();
          clpSolver->setSpecialOptions(options&(~8));
        }
#endif
      }
    }
  } else if (numberCutGenerators_) {
    int i;
    // add to counts anyway
    for (i = 0;i<numberCutGenerators_;i++) 
      generator_[i]->incrementNumberCutsInTotal(countRowCuts[i]);
    // What if not feasible as cuts may have helped
    if (feasible) {
      for (i = 0;i<numberNewCuts_;i++) {
        int iGenerator = whichGenerator_[i];
        if (iGenerator>=0)
          generator_[iGenerator]->incrementNumberCutsActive();
      }
    }
  }

  delete [] countRowCuts ;
  delete [] countColumnCuts ;


#ifdef CHECK_CUT_COUNTS
  if (feasible)
  { 
    CoinWarmStartBasis * basis = dynamic_cast<CoinWarmStartBasis*>(solver_->getWarmStart()) ;
    printf("solveWithCuts: Number of rows at end (only active cuts) %d\n",
           numberRowsAtContinuous_+numberNewCuts_+numberOldActiveCuts_) ;
    basis->print() ; delete basis;}
#endif
#ifdef CBC_DEBUG
  if (onOptimalPath && !solver_->isDualObjectiveLimitReached())
    assert(feasible) ;
#endif

  return feasible ; }


/*
  Remove slack cuts. We obtain a basis and scan it. Cuts with basic slacks
  are purged. If any cuts are purged, resolve() is called to restore the
  solution held in the solver.  If resolve() pivots, there's the possibility
  that a slack may be pivoted in (trust me :-), so the process iterates.
  Setting allowResolve to false will suppress reoptimisation (but see note
  below).

  At the level of the solver's constraint system, loose cuts are really
  deleted.  There's an implicit assumption that deleteRows will also update
  the active basis in the solver.

  At the level of nodes and models, it's more complicated.

  New cuts exist only in the collection of cuts passed as a parameter. They
  are deleted from the collection and that's the end of them.

  Older cuts have made it into addedCuts_. Two separate actions are needed.
  The reference count for the CbcCountRowCut object is decremented. If this
  count falls to 0, the node which owns the cut is located, the reference to
  the cut is removed, and then the cut object is destroyed (courtesy of the
  CbcCountRowCut destructor). We also need to set the addedCuts_ entry to
  NULL. This is important so that when it comes time to generate basis edits
  we can tell this cut was dropped from the basis during processing of the
  node.

  NOTE: In general, it's necessary to call resolve() after purging slack
        cuts.  Deleting constraints constitutes a change in the problem, and
        an OSI is not required to maintain a valid solution when the problem
        is changed. But ... it's really useful to maintain the active basis,
        and the OSI is supposed to do that. (Yes, it's splitting hairs.) In
        some places, it's possible to know that the solution will never be
        consulted after this call, only the basis.  (E.g., this routine is
        called as a last act before generating info to place the node in the
        live set.) For such use, set allowResolve to false.
  
  TODO: No real harm would be done if we just ignored the rare occasion when
        the call to resolve() pivoted a slack back into the basis. It's a
        minor inefficiency, at worst. But it does break assertions which
        check that there are no loose cuts in the basis. It might be better
        to remove the assertions.
*/

void
CbcModel::takeOffCuts (OsiCuts &newCuts,
                       bool allowResolve, OsiCuts * saveCuts)

{ // int resolveIterations = 0 ;
  int firstOldCut = numberRowsAtContinuous_ ;
  int totalNumberCuts = numberNewCuts_+numberOldActiveCuts_ ;
  int *solverCutIndices = new int[totalNumberCuts] ;
  int *newCutIndices = new int[numberNewCuts_] ;
  const CoinWarmStartBasis* ws ;
  CoinWarmStartBasis::Status status ;
  bool needPurge = true ;
/*
  The outer loop allows repetition of purge in the event that reoptimisation
  changes the basis. To start an iteration, clear the deletion counts and grab
  the current basis.
*/
  while (needPurge)
  { int numberNewToDelete = 0 ;
    int numberOldToDelete = 0 ;
    int i ;
    ws = dynamic_cast<const CoinWarmStartBasis*>(solver_->getWarmStart()) ;
/*
  Scan the basis entries of the old cuts generated prior to this round of cut
  generation.  Loose cuts are `removed' by decrementing their reference count
  and setting the addedCuts_ entry to NULL. (If the reference count falls to
  0, they're really deleted.  See CbcModel and CbcCountRowCut doc'n for
  principles of cut handling.)
*/
    int oldCutIndex = 0 ;
    for (i = 0 ; i < numberOldActiveCuts_ ; i++)
    { status = ws->getArtifStatus(i+firstOldCut) ;
      while (!addedCuts_[oldCutIndex]) oldCutIndex++ ;
      assert(oldCutIndex < currentNumberCuts_) ;
      // always leave if from nextRowCut_
      if (status == CoinWarmStartBasis::basic&&
          addedCuts_[oldCutIndex]->effectiveness()!=COIN_DBL_MAX)
      { solverCutIndices[numberOldToDelete++] = i+firstOldCut ;
        if (saveCuts) {
          // send to cut pool
          OsiRowCut * slackCut = addedCuts_[oldCutIndex];
          if (slackCut->effectiveness()!=-1.234) {
            slackCut->setEffectiveness(-1.234);
            saveCuts->insert(*slackCut);
          }
        }
        if (addedCuts_[oldCutIndex]->decrement() == 0)
          delete addedCuts_[oldCutIndex] ;
        addedCuts_[oldCutIndex] = NULL ;
        oldCutIndex++ ; }
      else
      { oldCutIndex++ ; } }
/*
  Scan the basis entries of the new cuts generated with this round of cut
  generation.  At this point, newCuts is the only record of the new cuts, so
  when we delete loose cuts from newCuts, they're really gone. newCuts is a
  vector, so it's most efficient to compress it (eraseRowCut) from back to
  front.
*/
    int firstNewCut = firstOldCut+numberOldActiveCuts_ ;
    int k = 0 ;
    for (i = 0 ; i < numberNewCuts_ ; i++)
    { status = ws->getArtifStatus(i+firstNewCut) ;
      if (status == CoinWarmStartBasis::basic&&whichGenerator_[i]!=-2)
      { solverCutIndices[numberNewToDelete+numberOldToDelete] = i+firstNewCut ;
        newCutIndices[numberNewToDelete++] = i ; }
      else
      { // save which generator did it
        whichGenerator_[k++] = whichGenerator_[i] ; } }
    delete ws ;
    for (i = numberNewToDelete-1 ; i >= 0 ; i--)
    { int iCut = newCutIndices[i] ;
      if (saveCuts) {
        // send to cut pool
        OsiRowCut * slackCut = newCuts.rowCutPtr(iCut);
        if (slackCut->effectiveness()!=-1.234) {
          slackCut->setEffectiveness(-1.234);
          saveCuts->insert(*slackCut);
        }
      }
      newCuts.eraseRowCut(iCut) ; }
/*
  Did we delete anything? If so, delete the cuts from the constraint system
  held in the solver and reoptimise unless we're forbidden to do so. If the
  call to resolve() results in pivots, there's the possibility we again have
  basic slacks. Repeat the purging loop.
*/
    if (numberNewToDelete+numberOldToDelete > 0)
    { solver_->deleteRows(numberNewToDelete+numberOldToDelete,
                          solverCutIndices) ;
      numberNewCuts_ -= numberNewToDelete ;
      numberOldActiveCuts_ -= numberOldToDelete ;
#     ifdef CBC_DEBUG
      printf("takeOffCuts: purged %d+%d cuts\n", numberOldToDelete,
             numberNewToDelete );
#     endif
      if (allowResolve)
      { 
        phase_=3;
        // can do quick optimality check
        int easy=2;
        solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,&easy) ;
        solver_->resolve() ;
        setPointers(solver_);
        solver_->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo,NULL) ;
        if (solver_->getIterationCount() == 0)
        { needPurge = false ; }
#       ifdef CBC_DEBUG
        else
          { printf( "Repeating purging loop. %d iters.\n",
                    solver_->getIterationCount()); }
#       endif
      }
      else
      { needPurge = false ; } }
    else
    { needPurge = false ; } }
/*
  Clean up and return.
*/
  delete [] solverCutIndices ;
  delete [] newCutIndices ;
}



int
CbcModel::resolve(CbcNodeInfo * parent, int whereFrom)
{
  // We may have deliberately added in violated cuts - check to avoid message
  int iRow;
  int numberRows = solver_->getNumRows();
  const double * rowLower = solver_->getRowLower();
  const double * rowUpper = solver_->getRowUpper();
  bool feasible=true;
  for (iRow= numberRowsAtContinuous_;iRow<numberRows;iRow++) {
    if (rowLower[iRow]>rowUpper[iRow]+1.0e-8)
      feasible=false;
  }
  // Can't happen if strong branching as would have been found before
  if (!numberStrong_&&numberObjects_>numberIntegers_) {
    int iColumn;
    int numberColumns = solver_->getNumCols();
    const double * columnLower = solver_->getColLower();
    const double * columnUpper = solver_->getColUpper();
    for (iColumn= 0;iColumn<numberColumns;iColumn++) {
      if (columnLower[iColumn]>columnUpper[iColumn]+1.0e-5)
        feasible=false;
    }
  }
#if 0
  {
    int iColumn;
    int numberColumns = solver_->getNumCols();
    const double * columnLower = solver_->getColLower();
    const double * columnUpper = solver_->getColUpper();
    const double * sol = solver_->getColSolution();
    bool feasible=true;
    double xx[37];
    memset(xx,0,sizeof(xx));
    xx[6]=xx[7]=xx[9]=xx[18]=xx[26]=xx[36]=1.0;
    for (iColumn= 0;iColumn<numberColumns;iColumn++) {
      if (solver_->isInteger(iColumn)) {
        //printf("yy %d at %g (bounds %g, %g)\n",iColumn,
        //     sol[iColumn],columnLower[iColumn],columnUpper[iColumn]);
        if (columnLower[iColumn]>xx[iColumn]||
            columnUpper[iColumn]<xx[iColumn])
          feasible=false;
        //solver_->setColLower(iColumn,CoinMin(xx[iColumn],columnUpper[iColumn]));
        //solver_->setColUpper(iColumn,CoinMax(xx[iColumn],columnLower[iColumn]));
      }
    }
    if (feasible)
      printf("On Feasible path\n");
  }
#endif
/*
  Reoptimize. Consider the possibility that we should fathom on bounds. But be
  careful --- where the objective takes on integral values, we may want to keep
  a solution where the objective is right on the cutoff.
*/
  if (feasible)
    {
      solver_->resolve() ;
      numberIterations_ += solver_->getIterationCount() ;
      feasible = (solver_->isProvenOptimal() &&
                  !solver_->isDualObjectiveLimitReached()) ;
    }
  if (0&&feasible) {
    const double * lb = solver_->getColLower();
    const double * ub = solver_->getColUpper();
    const double * x = solver_->getColSolution();
    const double * dj = solver_->getReducedCost();
    int numberColumns = solver_->getNumCols();
    for (int i=0;i<numberColumns;i++) {
      if (dj[i]>1.0e-4&&ub[i]-lb[i]>1.0e-4&&x[i]>lb[i]+1.0e-4)
        printf("error %d %g %g %g %g\n",i,dj[i],lb[i],x[i],ub[i]);
      if (dj[i]<-1.0e-4&&ub[i]-lb[i]>1.0e-4&&x[i]<ub[i]-1.0e-4)
        printf("error %d %g %g %g %g\n",i,dj[i],lb[i],x[i],ub[i]);
    }
  } 
  if (!feasible&& continuousObjective_ <-1.0e30) {
    // at root node - double double check
    bool saveTakeHint;
    OsiHintStrength saveStrength;
    solver_->getHintParam(OsiDoDualInResolve,saveTakeHint,saveStrength);
    if (saveTakeHint||saveStrength==OsiHintIgnore) {
      solver_->setHintParam(OsiDoDualInResolve,false,OsiHintDo) ;
      solver_->resolve();
      solver_->setHintParam(OsiDoDualInResolve,saveTakeHint,saveStrength);
      numberIterations_ += solver_->getIterationCount() ;
      feasible = solver_->isProvenOptimal();
      //      solver_->writeMps("infeas");
    }
  }
  setPointers(solver_);
  int returnStatus = feasible ? 1 : 0;
  if (strategy_) {
    // user can play clever tricks here
    int status = strategy_->status(this,parent,whereFrom);
    if (status>=0) {
      if (status==0)
        returnStatus = 1;
      else if(status==1)
        returnStatus=-1;
      else
        returnStatus=0;
    }
  }
  return returnStatus ;
}


/* Set up objects.  Only do ones whose length is in range.
   If makeEquality true then a new model may be returned if
   modifications had to be made, otherwise "this" is returned.

   Could use Probing at continuous to extend objects
*/
CbcModel * 
CbcModel::findCliques(bool makeEquality,
                      int atLeastThisMany, int lessThanThis, int defaultValue)
{
  // No objects are allowed to exist
  assert(numberObjects_==numberIntegers_||!numberObjects_);
  CoinPackedMatrix matrixByRow(*solver_->getMatrixByRow());
  int numberRows = solver_->getNumRows();
  int numberColumns = solver_->getNumCols();

  // We may want to add columns
  int numberSlacks=0;
  int * rows = new int[numberRows];
  double * element =new double[numberRows];

  int iRow;

  findIntegers(true);
  numberObjects_=numberIntegers_;

  int numberCliques=0;
  CbcObject ** object = new CbcObject * [numberRows];
  int * which = new int[numberIntegers_];
  char * type = new char[numberIntegers_];
  int * lookup = new int[numberColumns];
  int i;
  for (i=0;i<numberColumns;i++) 
    lookup[i]=-1;
  for (i=0;i<numberIntegers_;i++) 
    lookup[integerVariable_[i]]=i;

  // Statistics
  int totalP1=0,totalM1=0;
  int numberBig=0,totalBig=0;
  int numberFixed=0;

  // Row copy
  const double * elementByRow = matrixByRow.getElements();
  const int * column = matrixByRow.getIndices();
  const CoinBigIndex * rowStart = matrixByRow.getVectorStarts();
  const int * rowLength = matrixByRow.getVectorLengths();

  // Column lengths for slacks
  const int * columnLength = solver_->getMatrixByCol()->getVectorLengths();

  const double * lower = getColLower();
  const double * upper = getColUpper();
  const double * rowLower = getRowLower();
  const double * rowUpper = getRowUpper();

  for (iRow=0;iRow<numberRows;iRow++) {
    int numberP1=0, numberM1=0;
    int j;
    double upperValue=rowUpper[iRow];
    double lowerValue=rowLower[iRow];
    bool good=true;
    int slack = -1;
    for (j=rowStart[iRow];j<rowStart[iRow]+rowLength[iRow];j++) {
      int iColumn = column[j];
      int iInteger=lookup[iColumn];
      if (upper[iColumn]-lower[iColumn]<1.0e-8) {
        // fixed
        upperValue -= lower[iColumn]*elementByRow[j];
        lowerValue -= lower[iColumn]*elementByRow[j];
        continue;
      } else if (upper[iColumn]!=1.0||lower[iColumn]!=0.0) {
        good = false;
        break;
      } else if (iInteger<0) {
        good = false;
        break;
      } else {
        if (columnLength[iColumn]==1)
          slack = iInteger;
      }
      if (fabs(elementByRow[j])!=1.0) {
        good=false;
        break;
      } else if (elementByRow[j]>0.0) {
        which[numberP1++]=iInteger;
      } else {
        numberM1++;
        which[numberIntegers_-numberM1]=iInteger;
      }
    }
    int iUpper = (int) floor(upperValue+1.0e-5);
    int iLower = (int) ceil(lowerValue-1.0e-5);
    int state=0;
    if (upperValue<1.0e6) {
      if (iUpper==1-numberM1)
        state=1;
      else if (iUpper==-numberM1)
        state=2;
      else if (iUpper<-numberM1)
        state=3;
    }
    if (!state&&lowerValue>-1.0e6) {
      if (-iLower==1-numberP1)
        state=-1;
      else if (-iLower==-numberP1)
        state=-2;
      else if (-iLower<-numberP1)
        state=-3;
    }
    if (good&&state) {
      if (abs(state)==3) {
        // infeasible
        numberObjects_=-1;
        break;
      } else if (abs(state)==2) {
        // we can fix all
        numberFixed += numberP1+numberM1;
        if (state>0) {
          // fix all +1 at 0, -1 at 1
          for (i=0;i<numberP1;i++)
            solver_->setColUpper(integerVariable_[which[i]],0.0);
          for (i=0;i<numberM1;i++)
            solver_->setColLower(integerVariable_[which[numberIntegers_-i-1]],
                                 1.0);
        } else {
          // fix all +1 at 1, -1 at 0
          for (i=0;i<numberP1;i++)
            solver_->setColLower(integerVariable_[which[i]],1.0);
          for (i=0;i<numberM1;i++)
            solver_->setColUpper(integerVariable_[which[numberIntegers_-i-1]],
                                 0.0);
        }
      } else {
        int length = numberP1+numberM1;
        if (length >= atLeastThisMany&&length<lessThanThis) {
          // create object
          bool addOne=false;
          int objectType;
          if (iLower==iUpper) {
            objectType=1;
          } else {
            if (makeEquality) {
              objectType=1;
              element[numberSlacks]=state;
              rows[numberSlacks++]=iRow;
              addOne=true;
            } else {
              objectType=0;
            }
          }
          if (state>0) {
            totalP1 += numberP1;
            totalM1 += numberM1;
            for (i=0;i<numberP1;i++)
              type[i]=1;
            for (i=0;i<numberM1;i++) {
              which[numberP1]=which[numberIntegers_-i-1];
              type[numberP1++]=0;
            }
          } else {
            totalP1 += numberM1;
            totalM1 += numberP1;
            for (i=0;i<numberP1;i++)
              type[i]=0;
            for (i=0;i<numberM1;i++) {
              which[numberP1]=which[numberIntegers_-i-1];
              type[numberP1++]=1;
            }
          }
          if (addOne) {
            // add in slack
            which[numberP1]=numberIntegers_+numberSlacks-1;
            slack = numberP1;
            type[numberP1++]=1;
          } else if (slack >= 0) {
            for (i=0;i<numberP1;i++) {
              if (which[i]==slack) {
                slack=i;
              }
            }
          }
          object[numberCliques] = new CbcClique(this,objectType,numberP1,
                                              which,type,
                                               1000000+numberCliques,slack);
          numberCliques++;
        } else if (numberP1+numberM1 >= lessThanThis) {
          // too big
          numberBig++;
          totalBig += numberP1+numberM1;
        }
      }
    }
  }
  delete [] which;
  delete [] type;
  delete [] lookup;
  if (numberCliques<0) {
    printf("*** Problem infeasible\n");
  } else {
    if (numberCliques)
      printf("%d cliques of average size %g found, %d P1, %d M1\n",
             numberCliques,
             ((double)(totalP1+totalM1))/((double) numberCliques),
             totalP1,totalM1);
    else
      printf("No cliques found\n");
    if (numberBig)
      printf("%d large cliques ( >= %d) found, total %d\n",
             numberBig,lessThanThis,totalBig);
    if (numberFixed)
      printf("%d variables fixed\n",numberFixed);
  }
  if (numberCliques>0&&numberSlacks&&makeEquality) {
    printf("adding %d integer slacks\n",numberSlacks);
    // add variables to make equality rows
    int * temp = new int[numberIntegers_+numberSlacks];
    memcpy(temp,integerVariable_,numberIntegers_*sizeof(int));
    // Get new model
    CbcModel * newModel = new CbcModel(*this);
    OsiSolverInterface * newSolver = newModel->solver();
    for (i=0;i<numberSlacks;i++) {
      temp[i+numberIntegers_]=i+numberColumns;
      int iRow = rows[i];
      double value = element[i];
      double lowerValue = 0.0;
      double upperValue = 1.0;
      double objValue  = 0.0;
      CoinPackedVector column(1,&iRow,&value);
      newSolver->addCol(column,lowerValue,upperValue,objValue);
      // set integer
      newSolver->setInteger(numberColumns+i);
      if (value >0)
        newSolver->setRowLower(iRow,rowUpper[iRow]);
      else
        newSolver->setRowUpper(iRow,rowLower[iRow]);
    }
    // replace list of integers
    for (i=0;i<newModel->numberObjects_;i++)
      delete newModel->object_[i];
    newModel->numberObjects_ = 0;
    delete [] newModel->object_;
    newModel->object_=NULL;
    newModel->findIntegers(true); //Set up all integer objects
    for (i=0;i<numberIntegers_;i++) {
      newModel->modifiableObject(i)->setPriority(object_[i]->priority());
    }
    if (originalColumns_) {
      // old model had originalColumns
      delete [] newModel->originalColumns_;
      newModel->originalColumns_ = new int[numberColumns+numberSlacks];
      memcpy(newModel->originalColumns_,originalColumns_,numberColumns*sizeof(int));
      // mark as not in previous model
      for (i=numberColumns;i<numberColumns+numberSlacks;i++)
        newModel->originalColumns_[i]=-1;
    }
    delete [] rows;
    delete [] element;
    newModel->addObjects(numberCliques,object);
    for (;i<numberCliques;i++) 
      delete object[i];
    delete [] object;
    newModel->synchronizeModel();
    return newModel;
  } else {
    if (numberCliques>0) {
      addObjects(numberCliques,object);
      for (;i<numberCliques;i++) 
        delete object[i];
      synchronizeModel();
    }
    delete [] object;
    delete [] rows;
    delete [] element;
    return this;
  }
}
// Fill in useful estimates
void 
CbcModel::pseudoShadow(double * down, double * up)
{
  // Column copy of matrix
  const double * element = solver_->getMatrixByCol()->getElements();
  const int * row = solver_->getMatrixByCol()->getIndices();
  const CoinBigIndex * columnStart = solver_->getMatrixByCol()->getVectorStarts();
  const int * columnLength = solver_->getMatrixByCol()->getVectorLengths();
  const double *objective = solver_->getObjCoefficients() ;
  int numberColumns = solver_->getNumCols() ;
  double direction = solver_->getObjSense();
  int iColumn;
  const double * dual = cbcRowPrice_;
  down = new double[numberColumns];
  up = new double[numberColumns];
  double upSum=1.0e-20;
  double downSum = 1.0e-20;
  int numberIntegers=0;
  for (iColumn=0;iColumn<numberColumns;iColumn++) {
    CoinBigIndex start = columnStart[iColumn];
    CoinBigIndex end = start + columnLength[iColumn];
    double upValue = 0.0;
    double downValue = 0.0;
    double value = direction*objective[iColumn];
    if (value) {
      if (value>0.0)
        upValue += value;
      else
        downValue -= value;
    }
    for (CoinBigIndex j=start;j<end;j++) {
      int iRow = row[j];
      value = -dual[iRow];
      if (value) {
        value *= element[j];
        if (value>0.0)
          upValue += value;
        else
          downValue -= value;
      }
    }
    // use dj if bigger
    double dj = cbcReducedCost_[iColumn];
    upValue = CoinMax(upValue,dj);
    downValue = CoinMax(downValue,-dj);
    up[iColumn]=upValue;
    down[iColumn]=downValue;
    if (solver_->isInteger(iColumn)) {
      if (!numberNodes_)
        printf("%d - dj %g up %g down %g cost %g\n",
               iColumn,dj,upValue,downValue,objective[iColumn]);
      upSum += upValue;
      downSum += downValue;
      numberIntegers++;
    }
  }
  if (numberIntegers) {
    double smallDown = 0.01*(downSum/((double) numberIntegers));
    double smallUp = 0.01*(upSum/((double) numberIntegers));
    for (int i=0;i<numberObjects_;i++) {
      CbcSimpleIntegerDynamicPseudoCost * obj1 =
        dynamic_cast <CbcSimpleIntegerDynamicPseudoCost *>(object_[i]) ;
      if (obj1) {
        iColumn = obj1->columnNumber();
        double upPseudoCost = obj1->upDynamicPseudoCost();
        double saveUp = upPseudoCost;
        upPseudoCost = CoinMax(upPseudoCost,smallUp);
        upPseudoCost = CoinMax(upPseudoCost,up[iColumn]);
        upPseudoCost = CoinMax(upPseudoCost,0.1*down[iColumn]);
        obj1->setUpDynamicPseudoCost(upPseudoCost);
        if (upPseudoCost>saveUp&&!numberNodes_)
          printf("For %d up went from %g to %g\n",
                 iColumn,saveUp,upPseudoCost);
        double downPseudoCost = obj1->downDynamicPseudoCost();
        double saveDown = downPseudoCost;
        downPseudoCost = CoinMax(downPseudoCost,smallDown);
        downPseudoCost = CoinMax(downPseudoCost,down[iColumn]);
        downPseudoCost = CoinMax(downPseudoCost,0.1*down[iColumn]);
        obj1->setDownDynamicPseudoCost(downPseudoCost);
        if (downPseudoCost>saveDown&&!numberNodes_)
          printf("For %d down went from %g to %g\n",
                 iColumn,saveDown,downPseudoCost);
      }
    }
  }
  delete [] down;
  delete [] up;
}

/*
  Set branching priorities.

  Setting integer priorities looks pretty robust; the call to findIntegers
  makes sure that SimpleInteger objects are in place. Setting priorities for
  other objects is entirely dependent on their existence, and the routine may
  quietly fail in several directions.
*/

void 
CbcModel::passInPriorities (const int * priorities,
                            bool ifObject)
{
  findIntegers(false);
  int i;
  if (priorities) {
    int i0=0;
    int i1=numberObjects_-1;
    if (ifObject) {
      for (i=numberIntegers_;i<numberObjects_;i++) {
        object_[i]->setPriority(priorities[i-numberIntegers_]);
      }
      i0=numberIntegers_;
    } else {
      for (i=0;i<numberIntegers_;i++) {
        object_[i]->setPriority(priorities[i]);
      }
      i1=numberIntegers_-1;
    }
    messageHandler()->message(CBC_PRIORITY,
                              messages())
                                << i0<<i1<<numberObjects_ << CoinMessageEol ;
  }
}

// Delete all object information
void 
CbcModel::deleteObjects()
{
  int i;
  for (i=0;i<numberObjects_;i++)
    delete object_[i];
  delete [] object_;
  object_ = NULL;
  numberObjects_=0;
  findIntegers(true);
}

/*!
  Ensure all attached objects (CbcObjects, heuristics, and cut
  generators) point to this model.
*/
void CbcModel::synchronizeModel()
{
  int i;
  for (i=0;i<numberHeuristics_;i++) 
    heuristic_[i]->setModel(this);
  for (i=0;i<numberObjects_;i++)
    object_[i]->setModel(this);
  for (i=0;i<numberCutGenerators_;i++)
    generator_[i]->refreshModel(this);
}

// Fill in integers and create objects

/**
  The routine first does a scan to count the number of integer variables.
  It then creates an array, integerVariable_, to store the indices of the
  integer variables, and an array of `objects', one for each variable.

  The scan is repeated, this time recording the index of each integer
  variable in integerVariable_, and creating an CbcSimpleInteger object that
  contains information about the integer variable. Initially, this is just
  the index and upper & lower bounds.

  \todo
  Note the assumption in cbc that the first numberIntegers_ objects are
  CbcSimpleInteger. In particular, the code which handles the startAgain
  case assumes that if the object_ array exists it can simply replace the first
  numberInteger_ objects. This is arguably unsafe.

  I am going to re-order if necessary
*/

void 
CbcModel::findIntegers(bool startAgain,int type)
{
  assert(solver_);
/*
  No need to do this if we have previous information, unless forced to start
  over.
*/
  if (numberIntegers_&&!startAgain&&object_)
    return;
/*
  Clear out the old integer variable list, then count the number of integer
  variables.
*/
  delete [] integerVariable_;
  numberIntegers_=0;
  int numberColumns = getNumCols();
  int iColumn;
  for (iColumn=0;iColumn<numberColumns;iColumn++) {
    if (isInteger(iColumn)) 
      numberIntegers_++;
  }
  // Find out how many old non-integer objects there are
  int nObjects=0;
  CbcObject ** oldObject = object_;
  int iObject;
  for (iObject = 0;iObject<numberObjects_;iObject++) {
    CbcSimpleInteger * obj =
      dynamic_cast <CbcSimpleInteger *>(oldObject[iObject]) ;
    if (obj) 
      delete oldObject[iObject];
    else
      oldObject[nObjects++]=oldObject[iObject];
  }
    
/*
  Found any? Allocate an array to hold the indices of the integer variables.
  Make a large enough array for all objects
*/
  object_ = new CbcObject * [numberIntegers_+nObjects];
  numberObjects_=numberIntegers_+nObjects;;
  integerVariable_ = new int [numberIntegers_];
/*
  Walk the variables again, filling in the indices and creating objects for
  the integer variables. Initially, the objects hold the index and upper &
  lower bounds.
*/
  numberIntegers_=0;
  for (iColumn=0;iColumn<numberColumns;iColumn++) {
    if(isInteger(iColumn)) {
      if (!type)
        object_[numberIntegers_] =
          new CbcSimpleInteger(this,numberIntegers_,iColumn);
      else if (type==1)
        object_[numberIntegers_] =
          new CbcSimpleIntegerPseudoCost(this,numberIntegers_,iColumn,0.3);
      integerVariable_[numberIntegers_++]=iColumn;
    }
  }
  // Now append other objects
  memcpy(object_+numberIntegers_,oldObject,nObjects*sizeof(CbcObject *));
  // Delete old array (just array)
  delete [] oldObject;
  
  if (!numberObjects_)
      handler_->message(CBC_NOINT,messages_) << CoinMessageEol ;
}
/* If numberBeforeTrust >0 then we are going to use CbcBranchDynamic.
   Scan and convert CbcSimpleInteger objects
*/
void 
CbcModel::convertToDynamic()
{
  int iObject;
  const double * cost = solver_->getObjCoefficients();
  for (iObject = 0;iObject<numberObjects_;iObject++) {
    CbcSimpleInteger * obj1 =
      dynamic_cast <CbcSimpleInteger *>(object_[iObject]) ;
    CbcSimpleIntegerPseudoCost * obj1a =
      dynamic_cast <CbcSimpleIntegerPseudoCost *>(object_[iObject]) ;
    CbcSimpleIntegerDynamicPseudoCost * obj2 =
      dynamic_cast <CbcSimpleIntegerDynamicPseudoCost *>(object_[iObject]) ;
    if (obj1&&!obj2) {
      // replace
      int iColumn = obj1->columnNumber();
      int priority = obj1->priority();
      int preferredWay = obj1->preferredWay();
      delete object_[iObject];
      double costValue = CoinMax(1.0e-5,fabs(cost[iColumn]));
      // treat as if will cost what it says up
      double upCost=costValue;
      // and balance at breakeven of 0.3
      double downCost=(0.7*upCost)/0.3;
      if (obj1a) {
        upCost=obj1a->upPseudoCost();
        downCost=obj1a->downPseudoCost();
      }
      CbcSimpleIntegerDynamicPseudoCost * newObject =
        new CbcSimpleIntegerDynamicPseudoCost(this,iObject,iColumn,downCost,upCost);
      newObject->setNumberBeforeTrust(numberBeforeTrust_);
      newObject->setPriority(priority);
      newObject->setPreferredWay(preferredWay);
      object_[iObject] = newObject;
    }
  }
  if (branchingMethod_&&(branchingMethod_->whichMethod()&1)==0) {
    // Need a method which can do better
    branchingMethod_=NULL;
  }
}

/* Add in any object information (objects are cloned - owner can delete
   originals */
void 
CbcModel::addObjects(int numberObjects, CbcObject ** objects)
{
  // If integers but not enough objects fudge
  if (numberIntegers_>numberObjects_)
    findIntegers(true);
  /* But if incoming objects inherit from simple integer we just want
     to replace */
  int numberColumns = solver_->getNumCols();
  /** mark is -1 if not integer, >=0 if using existing simple integer and
      >=numberColumns if using new integer */
  int * mark = new int[numberColumns];
  int i;
  for (i=0;i<numberColumns;i++)
    mark[i]=-1;
  int newNumberObjects = numberObjects;
  int newIntegers=0;
  for (i=0;i<numberObjects;i++) { 
    CbcSimpleInteger * obj =
      dynamic_cast <CbcSimpleInteger *>(objects[i]) ;
    if (obj) {
      int iColumn = obj->columnNumber();
      mark[iColumn]=i+numberColumns;
      newIntegers++;
    }
  }
  // and existing
  for (i=0;i<numberObjects_;i++) { 
    CbcSimpleInteger * obj =
      dynamic_cast <CbcSimpleInteger *>(object_[i]) ;
    if (obj) {
      int iColumn = obj->columnNumber();
      if (mark[iColumn]<0) {
        newIntegers++;
        newNumberObjects++;
        mark[iColumn]=i;
      }
    }
  } 
  delete [] integerVariable_;
  integerVariable_=NULL;
  if (newIntegers!=numberIntegers_) 
    printf("changing number of integers from %d to %d\n",
           numberIntegers_,newIntegers);
  numberIntegers_ = newIntegers;
  integerVariable_ = new int [numberIntegers_];
  CbcObject ** temp  = new CbcObject * [newNumberObjects];
  // Put integers first
  newIntegers=0;
  numberIntegers_=0;
  for (i=0;i<numberColumns;i++) {
    int which = mark[i];
    if (which>=0) {
      if (!isInteger(i)) {
        newIntegers++;
        solver_->setInteger(i);
      }
      if (which<numberColumns) {
        temp[numberIntegers_]=object_[which];
        object_[which]=NULL;
      } else {
        temp[numberIntegers_]=objects[which-numberColumns]->clone();
        temp[numberIntegers_]->setModel(this);
      }
      integerVariable_[numberIntegers_++]=i;
    }
  }
  if (newIntegers)
    printf("%d variables were declared integer\n",newIntegers);
  int n=numberIntegers_;
  // Now rest of old
  for (i=0;i<numberObjects_;i++) { 
    if (object_[i]) {
      CbcSimpleInteger * obj =
        dynamic_cast <CbcSimpleInteger *>(object_[i]) ;
      if (obj) {
        delete object_[i];
      } else {
        temp[n++]=object_[i];
      }
    }
  }
  // and rest of new
  for (i=0;i<numberObjects;i++) { 
    CbcSimpleInteger * obj =
      dynamic_cast <CbcSimpleInteger *>(objects[i]) ;
    if (!obj) {
      temp[n]=objects[i]->clone();
      temp[n++]->setModel(this);
    }
  }
  delete [] mark;
  delete [] object_;
  object_ = temp;
  assert (n==newNumberObjects);
  numberObjects_ = newNumberObjects;
}

/**
  This routine sets the objective cutoff value used for fathoming and
  determining monotonic variables.

  If the fathoming discipline is strict, a small tolerance is added to the
  new cutoff. This avoids problems due to roundoff when the target value
  is exact. The common example would be an IP with only integer variables in
  the objective. If the target is set to the exact value z of the optimum,
  it's possible to end up fathoming an ancestor of the solution because the
  solver returns z+epsilon.

  Determining if strict fathoming is needed is best done by analysis.
  In cbc, that's analyseObjective. The default is false.

  In cbc we always minimize so add epsilon
*/

void CbcModel::setCutoff (double value)

{
#if 0
  double tol = 0 ;
  int fathomStrict = getIntParam(CbcFathomDiscipline) ;
  if (fathomStrict == 1)
  { solver_->getDblParam(OsiDualTolerance,tol) ;
  tol = tol*(1+fabs(value)) ;
  
  value += tol ; }
#endif
  dblParam_[CbcCurrentCutoff]=value;
  // Solvers know about direction
  double direction = solver_->getObjSense();
  solver_->setDblParam(OsiDualObjectiveLimit,value*direction); }



/*
  Call this to really test if a valid solution can be feasible. The cutoff is
  passed in as a parameter so that we don't need to worry here after swapping
  solvers.  The solution is assumed to be numberColumns in size.  If
  fixVariables is true then the bounds of the continuous solver are updated.
  The routine returns the objective value determined by reoptimizing from
  scratch. If the solution is rejected, this will be worse than the cutoff.

  TODO: There's an issue with getting the correct cutoff value: We update the
        cutoff in the regular solver, but not in continuousSolver_. But our only
        use for continuousSolver_ is verifying candidate solutions. Would it
        make sense to update the cutoff? Then we wouldn't need to step around
        isDualObjectiveLimitReached().
*/
double 
CbcModel::checkSolution (double cutoff, const double *solution,
                         bool fixVariables, double objectiveValue)

{
  if (!solverCharacteristics_->solutionAddsCuts()) {
    // Can trust solution
    int numberColumns = solver_->getNumCols();
    
    /*
      Grab the continuous solver (the pristine copy of the problem, made before
      starting to work on the root node). Save the bounds on the variables.
      Install the solution passed as a parameter, and copy it to the model's
      currentSolution_.
      
      TODO: This is a belt-and-suspenders approach. Once the code has settled
      a bit, we can cast a critical eye here.
    */
    OsiSolverInterface * saveSolver = solver_;
    if (continuousSolver_)
      solver_ = continuousSolver_;
    // move solution to continuous copy
    solver_->setColSolution(solution);
    // Put current solution in safe place
    // Point to current solution
    const double * save = testSolution_;
    // Safe as will be const inside infeasibility()
    testSolution_ = solver_->getColSolution();
    //memcpy(currentSolution_,solver_->getColSolution(),
    // numberColumns*sizeof(double));
    //solver_->messageHandler()->setLogLevel(4);
    
    // save original bounds
    double * saveUpper = new double[numberColumns];
    double * saveLower = new double[numberColumns];
    memcpy(saveUpper,getColUpper(),numberColumns*sizeof(double));
    memcpy(saveLower,getColLower(),numberColumns*sizeof(double));
    
    /*
      Run through the objects and use feasibleRegion() to set variable bounds
      so as to fix the variables specified in the objects at their value in this
      solution. Since the object list contains (at least) one object for every
      integer variable, this has the effect of fixing all integer variables.
    */
    int i;
    for (i=0;i<numberObjects_;i++)
      object_[i]->feasibleRegion();
    // We can switch off check
    if ((specialOptions_&4)==0) {
      if ((specialOptions_&2)==0&&solverCharacteristics_->warmStart()) {
        /*
          Remove any existing warm start information to be sure there is no
          residual influence on initialSolve().
        */
        CoinWarmStartBasis *slack =
          dynamic_cast<CoinWarmStartBasis *>(solver_->getEmptyWarmStart()) ;
        solver_->setWarmStart(slack);
        delete slack ;
      }
      // Give a hint to do dual
      bool saveTakeHint;
      OsiHintStrength saveStrength;
#ifndef NDEBUG
      bool gotHint = (solver_->getHintParam(OsiDoDualInInitial,saveTakeHint,saveStrength));
      assert (gotHint);
#else
      (solver_->getHintParam(OsiDoDualInInitial,saveTakeHint,saveStrength));
#endif
      solver_->setHintParam(OsiDoDualInInitial,true,OsiHintTry);
      solver_->initialSolve();
      if (!solver_->isProvenOptimal())
        { //printf("checkSolution infeas! Retrying wihout scaling.\n");
          bool saveTakeHint;
          OsiHintStrength saveStrength;
          bool savePrintHint;
          //solver_->writeMps("infeas");
          bool gotHint = (solver_->getHintParam(OsiDoReducePrint,savePrintHint,saveStrength));
          gotHint = (solver_->getHintParam(OsiDoScale,saveTakeHint,saveStrength));
          solver_->setHintParam(OsiDoScale,false,OsiHintTry);
          //solver_->setHintParam(OsiDoReducePrint,false,OsiHintTry) ;
          solver_->setHintParam(OsiDoDualInInitial,false,OsiHintTry);
          solver_->initialSolve();
          solver_->setHintParam(OsiDoScale,saveTakeHint,saveStrength);
          //solver_->setHintParam(OsiDoReducePrint,savePrintHint,OsiHintTry) ;
        }
      //assert(solver_->isProvenOptimal());
      solver_->setHintParam(OsiDoDualInInitial,saveTakeHint,saveStrength);
      objectiveValue = solver_->getObjValue()*solver_->getObjSense();
    }
    
    /*
      Check that the solution still beats the objective cutoff.
      
      If it passes, make a copy of the primal variable values and do some
      cleanup and checks:
      + Values of all variables are are within original bounds and values of
      all integer variables are within tolerance of integral.
      + There are no constraint violations.
      There really should be no need for the check against original bounds.
      Perhaps an opportunity for a sanity check?
    */
    if ((solver_->isProvenOptimal()||(specialOptions_&4)!=0) && objectiveValue <= cutoff) { 
      double * solution = new double[numberColumns];
      memcpy(solution ,solver_->getColSolution(),numberColumns*sizeof(double)) ;
      
      int iColumn;
#ifndef NDEBUG
      double integerTolerance = getIntegerTolerance() ;
#endif
      for (iColumn = 0 ; iColumn < numberColumns ; iColumn++) {
        double value = solution[iColumn] ;
        value = CoinMax(value, saveLower[iColumn]) ;
        value = CoinMin(value, saveUpper[iColumn]) ;
        if (solver_->isInteger(iColumn)) 
          assert(fabs(value-solution[iColumn]) <= integerTolerance) ;
        solution[iColumn] = value ; 
      }
      if ((specialOptions_&16)==0) {
        const double * rowLower = solver_->getRowLower() ;
        const double * rowUpper = solver_->getRowUpper() ;
        int numberRows = solver_->getNumRows() ;
        double *rowActivity = new double[numberRows] ;
        memset(rowActivity,0,numberRows*sizeof(double)) ;
        solver_->getMatrixByCol()->times(solution,rowActivity) ;
        double primalTolerance ;
        solver_->getDblParam(OsiPrimalTolerance,primalTolerance) ;
        double largestInfeasibility =0.0;
        for (i=0 ; i < numberRows ; i++) {
          largestInfeasibility = CoinMax(largestInfeasibility,
                                         rowLower[i]-rowActivity[i]);
          largestInfeasibility = CoinMax(largestInfeasibility,
                                         rowActivity[i]-rowUpper[i]);
        }
        if (largestInfeasibility>100.0*primalTolerance) {
          handler_->message(CBC_NOTFEAS3, messages_)
            << largestInfeasibility << CoinMessageEol ;
          objectiveValue=1.0e50 ; 
        }
        delete [] rowActivity ;
      }
      delete [] solution;
    } else {
      objectiveValue=1.0e50 ; 
    }
    /*
      Regardless of what we think of the solution, we may need to restore the
      original bounds of the continuous solver. Unfortunately, const'ness
      prevents us from simply reversing the memcpy used to make these snapshots.
    */
    if (!fixVariables)
      { for (int iColumn = 0 ; iColumn < numberColumns ; iColumn++)
        { solver_->setColLower(iColumn,saveLower[iColumn]) ;
        solver_->setColUpper(iColumn,saveUpper[iColumn]) ; } }
    delete [] saveLower;
    delete [] saveUpper;
    
    /*
      Restore the usual solver.
    */
    solver_=saveSolver;
    testSolution_ = save;
    return objectiveValue;
  } else {
    // Outer approximation or similar
    //If this is true then the solution comes from the nlp we don't need to resolve the same nlp with ipopt
    //solverCharacteristics_->setSolver(solver_);
    bool solutionComesFromNlp = solverCharacteristics_->bestObjectiveValue()<cutoff;
    double objectiveValue;
    int numberColumns = solver_->getNumCols();
    double *saveLower = NULL;
    double * saveUpper = NULL;
    
    if(! solutionComesFromNlp)//Otherwise solution already comes from ipopt and cuts are known
      {
        if(fixVariables)//Will temporarily fix all integer valued var
          {
            // save original bounds
            saveUpper = new double[numberColumns];
            saveLower = new double[numberColumns];
            memcpy(saveUpper,solver_->getColUpper(),numberColumns*sizeof(double));
            memcpy(saveLower,solver_->getColLower(),numberColumns*sizeof(double));
            //in any case solution should be already loaded into solver_
            /*
              Run through the objects and use feasibleRegion() to set variable bounds
              so as to fix the variables specified in the objects at their value in this
              solution. Since the object list contains (at least) one object for every
              integer variable, this has the effect of fixing all integer variables.
            */
            const double * save = testSolution_;
            testSolution_ = solution;
            for (int i=0;i<numberObjects_;i++)
              object_[i]->feasibleRegion();
            testSolution_ = save;
            solver_->resolve();
          }
        
        /*
          Now step through the cut generators and see if any of them are flagged to
          run when a new solution is discovered. Only global cuts are useful. 
          (The solution being evaluated may not correspond to the current location in the
          search tree --- discovered by heuristic, for example.)
        */
        OsiCuts theseCuts;
        int i;
        int lastNumberCuts=0;
        for (i=0;i<numberCutGenerators_;i++) 
          {
            if (generator_[i]->atSolution()) 
              {
                generator_[i]->generateCuts(theseCuts,true,NULL);
                int numberCuts = theseCuts.sizeRowCuts();
                for (int j=lastNumberCuts;j<numberCuts;j++) 
                  {
                    const OsiRowCut * thisCut = theseCuts.rowCutPtr(j);
                    if (thisCut->globallyValid()) 
                      {
                        //           if ((specialOptions_&1)!=0) 
                        //           {
                        //             /* As these are global cuts -
                        //             a) Always get debugger object
                        // b) Not fatal error to cutoff optimal (if we have just got optimal)
                        // */
                        //             const OsiRowCutDebugger *debugger = solver_->getRowCutDebuggerAlways() ;
                        //             if (debugger) 
                        //             {
                        //               if(debugger->invalidCut(*thisCut))
                        //                 printf("ZZZZ Global cut - cuts off optimal solution!\n");
                        //             }
                        //           }
                        // add to global list
                        globalCuts_.insert(*thisCut);
                        generator_[i]->incrementNumberCutsInTotal();
                      }
                  }
              }
          }
        //   int numberCuts = theseCuts.sizeColCuts();
        //   for (i=0;i<numberCuts;i++) {
        //     const OsiColCut * thisCut = theseCuts.colCutPtr(i);
        //     if (thisCut->globallyValid()) {
        //       // add to global list
        //       globalCuts_.insert(*thisCut);
        //     }
        //   }
        //have to retrieve the solution and its value from the nlp
      }
    double newObjectiveValue=cutoff;
    if(solverCharacteristics_->solution(newObjectiveValue,
                                        const_cast<double *> (solution),
                                        numberColumns))
      {
        objectiveValue = newObjectiveValue;
      }
    else
      {
        objectiveValue = 2e50;
      }
    if (!solutionComesFromNlp && fixVariables)
      { 
        for (int iColumn = 0 ; iColumn < numberColumns ; iColumn++)
          { 
            solver_->setColLower(iColumn,saveLower[iColumn]) ;
            solver_->setColUpper(iColumn,saveUpper[iColumn]) ;  
          }
        delete [] saveLower;
        delete [] saveUpper;
      }
    //If the variables were fixed the cutting plane procedure may have believed that the node could be fathomed
    //re-establish truth.- should do no harm for non nlp
    if(!solutionComesFromNlp && fixVariables)
      solverCharacteristics_->setMipBound(-COIN_DBL_MAX);
    return objectiveValue;
  }
}

/*
  Call this routine from anywhere when a solution is found. The solution
  vector is assumed to contain one value for each structural variable.

  The first action is to run checkSolution() to confirm the objective and
  feasibility. If this check causes the solution to be rejected, we're done.
  If fixVariables = true, the variable bounds held by the continuous solver
  will be left fixed to the values in the solution; otherwise they are
  restored to the original values.

  If the solution is accepted, install it as the best solution.