source: branches/pre/ClpNonLinearCost.cpp @ 223

// Copyright (C) 2002, International Business Machines
// Corporation and others.  All Rights Reserved.

#include "CoinPragma.hpp"
#include <iostream>

#include "CoinIndexedVector.hpp"

#include "ClpNonLinearCost.hpp"
#include "ClpSimplex.hpp"

//#############################################################################
// Constructors / Destructor / Assignment
//#############################################################################

//-------------------------------------------------------------------
// Default Constructor 
//-------------------------------------------------------------------
ClpNonLinearCost::ClpNonLinearCost () :
  changeCost_(0.0),
  largestInfeasibility_(0.0),
  sumInfeasibilities_(0.0),
  numberRows_(0),
  numberColumns_(0),
  start_(NULL),
  whichRange_(NULL),
  offset_(NULL),
  lower_(NULL),
  cost_(NULL),
  model_(NULL),
  infeasible_(NULL),
  numberInfeasibilities_(-1),
  convex_(true),
  bothWays_(false)
{

}
/* Constructor from simplex.
   This will just set up wasteful arrays for linear, but
   later may do dual analysis and even finding duplicate columns 
*/
ClpNonLinearCost::ClpNonLinearCost ( ClpSimplex * model)
{
  model_ = model;
  numberRows_ = model_->numberRows();
  numberColumns_ = model_->numberColumns();
  int numberTotal = numberRows_+numberColumns_;
  convex_ = true;
  bothWays_ = false;
  start_ = new int [numberTotal+1];
  whichRange_ = new int [numberTotal];
  offset_ = new int [numberTotal];
  memset(offset_,0,numberTotal*sizeof(int));

  numberInfeasibilities_=0;
  changeCost_=0.0;
  double infeasibilityCost = model_->infeasibilityCost();
  sumInfeasibilities_=0.0;
  largestInfeasibility_=0.0;

  // First see how much space we need
  int put=0;

  int iSequence;
  double * upper = model_->upperRegion();
  double * lower = model_->lowerRegion();
  double * cost = model_->costRegion();

  // For quadratic we need -inf,0,0,+inf
  for (iSequence=0;iSequence<numberTotal;iSequence++) {
    if (lower[iSequence]>-1.0e20)
      put++;
    if (upper[iSequence]<1.0e20)
      put++;
    put += 2;
  }

  lower_ = new double [put];
  cost_ = new double [put];
  infeasible_ = new unsigned int[(put+31)>>5];
  memset(infeasible_,0,((put+31)>>5)*sizeof(unsigned int));

  put=0;

  start_[0]=0;

  for (iSequence=0;iSequence<numberTotal;iSequence++) {

    if (lower[iSequence]>-COIN_DBL_MAX) {
      lower_[put] = -COIN_DBL_MAX;
      setInfeasible(put,true);
      cost_[put++] = cost[iSequence]-infeasibilityCost;
    }
    whichRange_[iSequence]=put;
    lower_[put] = lower[iSequence];
    cost_[put++] = cost[iSequence];
    lower_[put] = upper[iSequence];
    cost_[put++] = cost[iSequence]+infeasibilityCost;
    if (upper[iSequence]<1.0e20) {
      lower_[put] = COIN_DBL_MAX;
      setInfeasible(put-1,true);
      cost_[put++] = 1.0e50;
    }
    start_[iSequence+1]=put;
  }

}
ClpNonLinearCost::ClpNonLinearCost(ClpSimplex * model,const int * starts,
                   const double * lowerNon, const double * costNon)
{

  // what about scaling? - only try without it initially
  assert(!model->scalingFlag());
  model_ = model;
  numberRows_ = model_->numberRows();
  numberColumns_ = model_->numberColumns();
  int numberTotal = numberRows_+numberColumns_;
  convex_ = true;
  bothWays_ = true;
  start_ = new int [numberTotal+1];
  whichRange_ = new int [numberTotal];
  offset_ = new int [numberTotal];
  memset(offset_,0,numberTotal*sizeof(int));
  
  double whichWay = model_->optimizationDirection();
  printf("Direction %g\n",whichWay);

  numberInfeasibilities_=0;
  changeCost_=0.0;
  double infeasibilityCost = model_->infeasibilityCost();
  largestInfeasibility_=0.0;
  sumInfeasibilities_=0.0;

  int iSequence;
  assert (!model_->rowObjective());
  double * cost = model_->objective();

  // First see how much space we need 
  // Also set up feasible bounds
  int put=starts[numberColumns_];

  double * columnUpper = model_->columnUpper();
  double * columnLower = model_->columnLower();
  for (iSequence=0;iSequence<numberColumns_;iSequence++) {
    if (columnLower[iSequence]>-1.0e20)
      put++;
    if (columnUpper[iSequence]<1.0e20)
      put++;
  }

  double * rowUpper = model_->rowUpper();
  double * rowLower = model_->rowLower();
  for (iSequence=0;iSequence<numberRows_;iSequence++) {
    if (rowLower[iSequence]>-1.0e20)
      put++;
    if (rowUpper[iSequence]<1.0e20)
      put++;
    put +=2;
  }
  lower_ = new double [put];
  cost_ = new double [put];
  infeasible_ = new unsigned int[(put+31)>>5];
  memset(infeasible_,0,((put+31)>>5)*sizeof(unsigned int));

  // now fill in 
  put=0;

  start_[0]=0;
  for (iSequence=0;iSequence<numberTotal;iSequence++) {
    lower_[put] = -COIN_DBL_MAX;
    whichRange_[iSequence]=put+1;
    double thisCost;
    double lowerValue;
    double upperValue;
    if (iSequence>=numberColumns_) {
      // rows
      lowerValue = rowLower[iSequence-numberColumns_];
      upperValue = rowUpper[iSequence-numberColumns_];
      if (lowerValue>-1.0e30) {
        setInfeasible(put,true);
        cost_[put++] = -infeasibilityCost;
        lower_[put] = lowerValue;
      }
      cost_[put++] = 0.0;
      thisCost = 0.0;
    } else {
      // columns - move costs and see if convex
      lowerValue = columnLower[iSequence];
      upperValue = columnUpper[iSequence];
      if (lowerValue>-1.0e30) {
        setInfeasible(put,true);
        cost_[put++] = whichWay*cost[iSequence]-infeasibilityCost;
        lower_[put] = lowerValue;
      }
      int iIndex = starts[iSequence];
      int end = starts[iSequence+1];
      assert (fabs(columnLower[iSequence]-lowerNon[iIndex])<1.0e-8);
      thisCost = -COIN_DBL_MAX;
      for (;iIndex<end;iIndex++) {
        if (lowerNon[iIndex]<columnUpper[iSequence]-1.0e-8) {
          lower_[put] = lowerNon[iIndex];
          cost_[put++] = whichWay*costNon[iIndex];
          // check convexity
          if (whichWay*costNon[iIndex]<thisCost-1.0e-12)
            convex_ = false;
          thisCost = whichWay*costNon[iIndex];
        } else {
          break;
        }
      }
    }
    lower_[put] = upperValue;
    setInfeasible(put,true);
    cost_[put++] = thisCost+infeasibilityCost;
    if (upperValue<1.0e20) {
      lower_[put] = COIN_DBL_MAX;
      cost_[put++] = 1.0e50;
    }
    int iFirst = start_[iSequence];
    if (lower_[iFirst] != -COIN_DBL_MAX) {
      setInfeasible(iFirst,true);
      whichRange_[iSequence]=iFirst+1;
    } else {
      whichRange_[iSequence]=iFirst;
    }
    start_[iSequence+1]=put;
  }
  // can't handle non-convex at present
  assert(convex_);
}

//-------------------------------------------------------------------
// Copy constructor 
//-------------------------------------------------------------------
ClpNonLinearCost::ClpNonLinearCost (const ClpNonLinearCost & rhs) :
  changeCost_(0.0),
  largestInfeasibility_(0.0),
  sumInfeasibilities_(0.0),
  numberRows_(rhs.numberRows_),
  numberColumns_(rhs.numberColumns_),
  start_(NULL),
  whichRange_(NULL),
  offset_(NULL),
  lower_(NULL),
  cost_(NULL),
  model_(NULL),
  infeasible_(NULL),
  numberInfeasibilities_(-1),
  convex_(true),
  bothWays_(rhs.bothWays_)
{  
  if (numberRows_) {
    int numberTotal = numberRows_+numberColumns_;
    start_ = new int [numberTotal+1];
    memcpy(start_,rhs.start_,(numberTotal+1)*sizeof(int));
    whichRange_ = new int [numberTotal];
    memcpy(whichRange_,rhs.whichRange_,numberTotal*sizeof(int));
    offset_ = new int [numberTotal];
    memcpy(offset_,rhs.offset_,numberTotal*sizeof(int));
    int numberEntries = start_[numberTotal];
    lower_ = new double [numberEntries];
    memcpy(lower_,rhs.lower_,numberEntries*sizeof(double));
    cost_ = new double [numberEntries];
    memcpy(cost_,rhs.cost_,numberEntries*sizeof(double));
    model_ = rhs.model_;
    numberInfeasibilities_=rhs.numberInfeasibilities_;
    changeCost_ = rhs.changeCost_;
    largestInfeasibility_ = rhs.largestInfeasibility_;
    sumInfeasibilities_ = rhs.sumInfeasibilities_;
    convex_ = rhs.convex_;
    infeasible_ = new unsigned int[(numberEntries+31)>>5];
    memcpy(infeasible_,rhs.infeasible_,
           ((numberEntries+31)>>5)*sizeof(unsigned int));
  }
}

//-------------------------------------------------------------------
// Destructor 
//-------------------------------------------------------------------
ClpNonLinearCost::~ClpNonLinearCost ()
{
  delete [] start_;
  delete [] whichRange_;
  delete [] offset_;
  delete [] lower_;
  delete [] cost_;
  delete [] infeasible_;
}

//----------------------------------------------------------------
// Assignment operator 
//-------------------------------------------------------------------
ClpNonLinearCost &
ClpNonLinearCost::operator=(const ClpNonLinearCost& rhs)
{
  if (this != &rhs) {
    numberRows_ = rhs.numberRows_;
    numberColumns_ = rhs.numberColumns_;
    delete [] start_;
    delete [] whichRange_;
    delete [] offset_;
    delete [] lower_;
    delete []cost_;
    delete [] infeasible_;
    start_ = NULL;
    whichRange_ = NULL;
    lower_ = NULL;
    cost_= NULL;
    infeasible_=NULL;
    if (numberRows_) {
      int numberTotal = numberRows_+numberColumns_;
      start_ = new int [numberTotal+1];
      memcpy(start_,rhs.start_,(numberTotal+1)*sizeof(int));
      whichRange_ = new int [numberTotal];
      memcpy(whichRange_,rhs.whichRange_,numberTotal*sizeof(int));
      offset_ = new int [numberTotal];
      memcpy(offset_,rhs.offset_,numberTotal*sizeof(int));
      int numberEntries = start_[numberTotal];
      lower_ = new double [numberEntries];
      memcpy(lower_,rhs.lower_,numberEntries*sizeof(double));
      cost_ = new double [numberEntries];
      memcpy(cost_,rhs.cost_,numberEntries*sizeof(double));
      infeasible_ = new unsigned int[(numberEntries+31)>>5];
      memcpy(infeasible_,rhs.infeasible_,
             ((numberEntries+31)>>5)*sizeof(unsigned int));
    }
    model_ = rhs.model_;
    numberInfeasibilities_=rhs.numberInfeasibilities_;
    changeCost_ = rhs.changeCost_;
    largestInfeasibility_ = rhs.largestInfeasibility_;
    sumInfeasibilities_ = rhs.sumInfeasibilities_;
    convex_ = rhs.convex_;
    bothWays_ = rhs.bothWays_;
  }
  return *this;
}

// Changes infeasible costs and computes number and cost of infeas
// We will need to re-think objective offsets later
// We will also need a 2 bit per variable array for some
// purpose which will come to me later
void 
ClpNonLinearCost::checkInfeasibilities(double oldTolerance)
{
  numberInfeasibilities_=0;
  double infeasibilityCost = model_->infeasibilityCost();
  changeCost_=0.0;
  largestInfeasibility_ = 0.0;
  sumInfeasibilities_ = 0.0;
  double primalTolerance = model_->currentPrimalTolerance();
  int iSequence;
  double * solution = model_->solutionRegion();
  double * upper = model_->upperRegion();
  double * lower = model_->lowerRegion();
  double * cost = model_->costRegion();
  bool toNearest = oldTolerance<=0.0;
    
  // nonbasic should be at a valid bound
  for (iSequence=0;iSequence<numberColumns_+numberRows_;iSequence++) {
    double lowerValue;
    double upperValue;
    double value=solution[iSequence];
    int iRange;
    // get correct place
    int start = start_[iSequence];
    int end = start_[iSequence+1]-1;
    // correct costs for this infeasibility weight
    if (infeasible(start))
      cost_[start] = cost_[start+1]-infeasibilityCost;
    if (infeasible(end-1))
      cost_[end-1] = cost_[end-2]+infeasibilityCost;
    for (iRange=start; iRange<end;iRange++) {
      if (value<lower_[iRange+1]+primalTolerance) {
        // put in better range if infeasible
        if (value>=lower_[iRange+1]-primalTolerance&&infeasible(iRange)&&iRange==start) 
          iRange++;
        whichRange_[iSequence]=iRange;
        break;
      }
    }
    assert(iRange<end);
    lowerValue = lower_[iRange];
    upperValue = lower_[iRange+1];
    ClpSimplex::Status status = model_->getStatus(iSequence);
    if (upperValue==lowerValue) {
      if (status != ClpSimplex::basic) 
        model_->setStatus(iSequence,ClpSimplex::isFixed);
    }
    switch(status) {
      
    case ClpSimplex::basic:
    case ClpSimplex::superBasic:
      // iRange is in correct place
      // slot in here
      if (infeasible(iRange)) {
        if (lower_[iRange]<-1.0e50) {
          //cost_[iRange] = cost_[iRange+1]-infeasibilityCost;
          // possibly below
          lowerValue = lower_[iRange+1];
          if (value<lowerValue-primalTolerance) {
            value = lowerValue-value;
            sumInfeasibilities_ += value;
            largestInfeasibility_ = max(largestInfeasibility_,value);
            changeCost_ -= lowerValue*
              (cost_[iRange]-cost[iSequence]);
            numberInfeasibilities_++;
          }
        } else {
          //cost_[iRange] = cost_[iRange-1]+infeasibilityCost;
          // possibly above
          upperValue = lower_[iRange];
          if (value>upperValue+primalTolerance) {
            value = value-upperValue;
            sumInfeasibilities_ += value;
            largestInfeasibility_ = max(largestInfeasibility_,value);
            changeCost_ -= upperValue*
              (cost_[iRange]-cost[iSequence]);
            numberInfeasibilities_++;
          }
        }
      }
      //lower[iSequence] = lower_[iRange];
      //upper[iSequence] = lower_[iRange+1];
      //cost[iSequence] = cost_[iRange];
      break;
    case ClpSimplex::isFree:
      if (toNearest)
        solution[iSequence] = 0.0;
      break;
    case ClpSimplex::atUpperBound:
      if (!toNearest) {
        // With increasing tolerances - we may be at wrong place
        if (fabs(value-upperValue)>oldTolerance*1.0001) {
          if (fabs(value-lowerValue)<=oldTolerance*1.0001) {
            if  (fabs(value-lowerValue)>primalTolerance)
              solution[iSequence]=lowerValue;
            model_->setStatus(iSequence,ClpSimplex::atLowerBound);
          } else {
            model_->setStatus(iSequence,ClpSimplex::superBasic);
          }
        } else if  (fabs(value-upperValue)>primalTolerance) {
          solution[iSequence]=upperValue;
        }
      } else {
        // Set to nearest and make at upper bound
        int kRange;
        iRange=-1;
        double nearest = COIN_DBL_MAX;
        for (kRange=start; kRange<end;kRange++) {
          if (fabs(lower_[kRange]-value)<nearest) {
            nearest = fabs(lower_[kRange]-value);
            iRange=kRange;
          }
        }
        assert (iRange>=0);
        iRange--;
        solution[iSequence]=lower_[iRange+1];
      }
      break;
    case ClpSimplex::atLowerBound:
      if (!toNearest) {
        // With increasing tolerances - we may be at wrong place
        if (fabs(value-lowerValue)>oldTolerance*1.0001) {
          if (fabs(value-upperValue)<=oldTolerance*1.0001) {
            if  (fabs(value-upperValue)>primalTolerance)
              solution[iSequence]=upperValue;
            model_->setStatus(iSequence,ClpSimplex::atLowerBound);
          } else {
            model_->setStatus(iSequence,ClpSimplex::superBasic);
          }
        } else if  (fabs(value-lowerValue)>primalTolerance) {
          solution[iSequence]=lowerValue;
        }
      } else {
        // Set to nearest and make at lower bound
        int kRange;
        iRange=-1;
        double nearest = COIN_DBL_MAX;
        for (kRange=start; kRange<end;kRange++) {
          if (fabs(lower_[kRange]-value)<nearest) {
            nearest = fabs(lower_[kRange]-value);
            iRange=kRange;
          }
        }
        assert (iRange>=0);
        solution[iSequence]=lower_[iRange];
      }
      break;
    case ClpSimplex::isFixed:
      if (toNearest) {
        // Set to true fixed
        for (iRange=start; iRange<end;iRange++) {
          if (lower_[iRange]==lower_[iRange+1])
            break;
        }
        solution[iSequence]=lower_[iRange];
      }
      break;
    }
    lower[iSequence] = lower_[iRange];
    upper[iSequence] = lower_[iRange+1];
    cost[iSequence] = cost_[iRange];
  }
}
/* Goes through one bound for each variable.
   If array[i]*multiplier>0 goes down, otherwise up.
   The indices are row indices and need converting to sequences
*/
void 
ClpNonLinearCost::goThru(int numberInArray, double multiplier,
              const int * index, const double * array,
                         double * rhs)
{
  assert (model_!=NULL);
  const int * pivotVariable = model_->pivotVariable();
  int i;
  for (i=0;i<numberInArray;i++) {
    int iRow = index[i];
    int iPivot = pivotVariable[iRow];
    double alpha = multiplier*array[iRow];
    // get where in bound sequence
    int iRange = whichRange_[iPivot];
    iRange += offset_[iPivot]; //add temporary bias
    double value = model_->solution(iPivot);
    if (alpha>0.0) {
      // down one
      iRange--;
      assert(iRange>=start_[iPivot]);
      rhs[iRow] = value - lower_[iRange];
    } else {
      // up one
      iRange++;
      assert(iRange<start_[iPivot+1]-1);
      rhs[iRow] = lower_[iRange+1] - value;
    }
    offset_[iPivot] = iRange - whichRange_[iPivot];
  }
}
/* Takes off last iteration (i.e. offsets closer to 0)
 */
void 
ClpNonLinearCost::goBack(int numberInArray, const int * index, 
              double * rhs)
{
  assert (model_!=NULL);
  const int * pivotVariable = model_->pivotVariable();
  int i;
  for (i=0;i<numberInArray;i++) {
    int iRow = index[i];
    int iPivot = pivotVariable[iRow];
    // get where in bound sequence
    int iRange = whichRange_[iPivot];
    bool down;
    // get closer to original
    if (offset_[iPivot]>0) {
      offset_[iPivot]--;
      assert (offset_[iPivot]>=0);
      down = false;
    } else {
      offset_[iPivot]++;
      assert (offset_[iPivot]<=0);
      down = true;
    }
    iRange += offset_[iPivot]; //add temporary bias
    double value = model_->solution(iPivot);
    if (down) {
      // down one
      assert(iRange>=start_[iPivot]);
      rhs[iRow] = value - lower_[iRange];
    } else {
      // up one
      assert(iRange<start_[iPivot+1]-1);
      rhs[iRow] = lower_[iRange+1] - value;
    }
  }
}
void 
ClpNonLinearCost::goBackAll(const CoinIndexedVector * update)
{
  assert (model_!=NULL);
  const int * pivotVariable = model_->pivotVariable();
  int i;
  int number = update->getNumElements();
  const int * index = update->getIndices();
  for (i=0;i<number;i++) {
    int iRow = index[i];
    int iPivot = pivotVariable[iRow];
    offset_[iPivot]=0;
  }
#ifdef CLP_DEBUG
  for (i=0;i<numberRows_+numberColumns_;i++) 
    assert(!offset_[i]);
#endif
}
void 
ClpNonLinearCost::checkInfeasibilities(int numberInArray, const int * index)
{
  assert (model_!=NULL);
  double primalTolerance = model_->currentPrimalTolerance();
  const int * pivotVariable = model_->pivotVariable();
  int i;
  for (i=0;i<numberInArray;i++) {
    int iRow = index[i];
    int iPivot = pivotVariable[iRow];
    // get where in bound sequence
    int iRange;
    int currentRange = whichRange_[iPivot];
    double value = model_->solution(iPivot);
    int start = start_[iPivot];
    int end = start_[iPivot+1]-1;
    for (iRange=start; iRange<end;iRange++) {
      if (value<lower_[iRange+1]+primalTolerance) {
        // put in better range
        if (value>=lower_[iRange+1]-primalTolerance&&infeasible(iRange)&&iRange==start) 
          iRange++;
        break;
      }
    }
    assert(iRange<end);
    assert(model_->getStatus(iPivot)==ClpSimplex::basic);
    double & lower = model_->lowerAddress(iPivot);
    double & upper = model_->upperAddress(iPivot);
    double & cost = model_->costAddress(iPivot);
    whichRange_[iPivot]=iRange;
    if (iRange!=currentRange) {
      if (infeasible(iRange))
        numberInfeasibilities_++;
      if (infeasible(currentRange))
        numberInfeasibilities_--;
    }
    lower = lower_[iRange];
    upper = lower_[iRange+1];
    cost = cost_[iRange];
  }
}
/* Puts back correct infeasible costs for each variable
   The input indices are row indices and need converting to sequences
   for costs.
   On input array is empty (but indices exist).  On exit just
   changed costs will be stored as normal CoinIndexedVector
*/
void 
ClpNonLinearCost::checkChanged(int numberInArray, CoinIndexedVector * update)
{
  assert (model_!=NULL);
  double primalTolerance = model_->currentPrimalTolerance();
  const int * pivotVariable = model_->pivotVariable();
  int number=0;
  int * index = update->getIndices();
  double * work = update->denseVector();
  int i;
  for (i=0;i<numberInArray;i++) {
    int iRow = index[i];
    int iPivot = pivotVariable[iRow];
    // get where in bound sequence
    int iRange;
    double value = model_->solution(iPivot);
    int start = start_[iPivot];
    int end = start_[iPivot+1]-1;
    for (iRange=start; iRange<end;iRange++) {
      if (value<lower_[iRange+1]+primalTolerance) {
        // put in better range
        if (value>=lower_[iRange+1]-primalTolerance&&infeasible(iRange)&&iRange==start) 
          iRange++;
        break;
      }
    }
    assert(iRange<end);
    assert(model_->getStatus(iPivot)==ClpSimplex::basic);
    int jRange = whichRange_[iPivot];
    if (iRange!=jRange) {
      // changed
      work[iRow] = cost_[jRange]-cost_[iRange];
      index[number++]=iRow;
      double & lower = model_->lowerAddress(iPivot);
      double & upper = model_->upperAddress(iPivot);
      double & cost = model_->costAddress(iPivot);
      whichRange_[iPivot]=iRange;
      if (infeasible(iRange))
        numberInfeasibilities_++;
      if (infeasible(jRange))
        numberInfeasibilities_--;
      lower = lower_[iRange];
      upper = lower_[iRange+1];
      cost = cost_[iRange];
    }
  }
  update->setNumElements(number);
}
/* Sets bounds and cost for one variable - returns change in cost*/
double 
ClpNonLinearCost::setOne(int iPivot, double value)
{
  assert (model_!=NULL);
  double primalTolerance = model_->currentPrimalTolerance();
  // get where in bound sequence
  int iRange;
  int currentRange = whichRange_[iPivot];
  int start = start_[iPivot];
  int end = start_[iPivot+1]-1;
  if (!bothWays_) {
    // If fixed try and get feasible
    if (lower_[start+1]==lower_[start+2]&&fabs(value-lower_[start+1])<1.001*primalTolerance) {
      iRange =start+1;
    } else {
      for (iRange=start; iRange<end;iRange++) {
        if (value<=lower_[iRange+1]+primalTolerance) {
          // put in better range
          if (value>=lower_[iRange+1]-primalTolerance&&infeasible(iRange)&&iRange==start) 
            iRange++;
          break;
        }
      }
    }
  } else {
    // leave in current if possible
    iRange = whichRange_[iPivot];
    if (value<lower_[iRange]-primalTolerance||value>lower_[iRange+1]+primalTolerance) {
      for (iRange=start; iRange<end;iRange++) {
        if (value<lower_[iRange+1]+primalTolerance) {
          // put in better range
          if (value>=lower_[iRange+1]-primalTolerance&&infeasible(iRange)&&iRange==start) 
            iRange++;
          break;
        }
      }
    }
  }
  assert(iRange<end);
  whichRange_[iPivot]=iRange;
  if (iRange!=currentRange) {
    if (infeasible(iRange))
      numberInfeasibilities_++;
    if (infeasible(currentRange))
      numberInfeasibilities_--;
  }
  double & lower = model_->lowerAddress(iPivot);
  double & upper = model_->upperAddress(iPivot);
  double & cost = model_->costAddress(iPivot);
  lower = lower_[iRange];
  upper = lower_[iRange+1];
  ClpSimplex::Status status = model_->getStatus(iPivot);
  if (upper==lower) {
    if (status != ClpSimplex::basic) {
      model_->setStatus(iPivot,ClpSimplex::isFixed);
      status = ClpSimplex::basic; // so will skip
    }
  }
  switch(status) {
      
  case ClpSimplex::basic:
  case ClpSimplex::superBasic:
  case ClpSimplex::isFree:
    break;
  case ClpSimplex::atUpperBound:
  case ClpSimplex::atLowerBound:
  case ClpSimplex::isFixed:
    // set correctly
    if (fabs(value-lower)<=primalTolerance*1.001){
      model_->setStatus(iPivot,ClpSimplex::atLowerBound);
    } else if (fabs(value-upper)<=primalTolerance*1.001){
      model_->setStatus(iPivot,ClpSimplex::atUpperBound);
    } else {
      // set superBasic
      model_->setStatus(iPivot,ClpSimplex::superBasic);
    }
    break;
  }
  double difference = cost-cost_[iRange]; 
  cost = cost_[iRange];
  changeCost_ += value*difference;
  return difference;
}
/* Sets bounds and cost for outgoing variable 
   may change value
   Returns direction */
int 
ClpNonLinearCost::setOneOutgoing(int iPivot, double & value)
{
  assert (model_!=NULL);
  double primalTolerance = model_->currentPrimalTolerance();
  // get where in bound sequence
  int iRange;
  int currentRange = whichRange_[iPivot];
  int start = start_[iPivot];
  int end = start_[iPivot+1]-1;
  // Set perceived direction out
  int direction;
  if (value<=lower_[currentRange]+1.001*primalTolerance) {
    direction=1;
  } else if (value>=lower_[currentRange+1]-1.001*primalTolerance) {
    direction=-1;
  } else {
    // odd
    direction=0;
  }
  // If fixed try and get feasible
  if (lower_[start+1]==lower_[start+2]&&fabs(value-lower_[start+1])<1.001*primalTolerance) {
    iRange =start+1;
  } else {
    // See if exact
    for (iRange=start; iRange<end;iRange++) {
      if (value==lower_[iRange+1]) {
        // put in better range
        if (infeasible(iRange)&&iRange==start) 
          iRange++;
        break;
      }
    }
    if (iRange==end) {
      // not exact
      for (iRange=start; iRange<end;iRange++) {
        if (value<=lower_[iRange+1]+primalTolerance) {
          // put in better range
          if (value>=lower_[iRange+1]-primalTolerance&&infeasible(iRange)&&iRange==start) 
            iRange++;
          break;
        }
      }
    }
  }
  assert(iRange<end);
  whichRange_[iPivot]=iRange;
  if (iRange!=currentRange) {
    if (infeasible(iRange))
      numberInfeasibilities_++;
    if (infeasible(currentRange))
      numberInfeasibilities_--;
  }
  double & lower = model_->lowerAddress(iPivot);
  double & upper = model_->upperAddress(iPivot);
  double & cost = model_->costAddress(iPivot);
  lower = lower_[iRange];
  upper = lower_[iRange+1];
  if (upper==lower) {
    value=upper;
  } else {
    // set correctly
    if (fabs(value-lower)<=primalTolerance*1.001){
      value = min(value,lower+primalTolerance);
    } else if (fabs(value-upper)<=primalTolerance*1.001){
      value = max(value,upper-primalTolerance);
    } else {
      printf("*** variable wandered off bound %g %g %g!\n",
             lower,value,upper);
      if (value-lower<=upper-value) 
        value = lower+primalTolerance;
      else 
        value = upper-primalTolerance;
    }
  }
  double difference = cost-cost_[iRange]; 
  cost = cost_[iRange];
  changeCost_ += value*difference;
  return direction;
}
// Returns nearest bound
double 
ClpNonLinearCost::nearest(int sequence, double solutionValue)
{
  assert (model_!=NULL);
  // get where in bound sequence
  int iRange;
  int start = start_[sequence];
  int end = start_[sequence+1];
  int jRange=-1;
  double nearest=COIN_DBL_MAX;
  for (iRange=start; iRange<end;iRange++) {
    if (fabs(solutionValue-lower_[iRange])<nearest) {
      jRange=iRange;
      nearest=fabs(solutionValue-lower_[iRange]);
    }
  }
  assert(jRange<end);
  return lower_[jRange];
}