/* $Id: CoinAbcDenseFactorization.cpp 2385 2019-01-06 19:43:06Z unxusr $ */
// Copyright (C) 2008, International Business Machines
// Corporation and others, Copyright (C) 2012, FasterCoin.  All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).

#include "CoinUtilsConfig.h"
#include "CoinPragma.hpp"

#include <cassert>
#include <cstdio>

#include "CoinAbcDenseFactorization.hpp"
#include "CoinAbcCommonFactorization.hpp"
#include "CoinIndexedVector.hpp"
#include "CoinHelperFunctions.hpp"
#include "CoinPackedMatrix.hpp"
#include "CoinFinite.hpp"
//:class CoinAbcDenseFactorization.  Deals with Factorization and Updates
//  CoinAbcDenseFactorization.  Constructor
CoinAbcDenseFactorization::CoinAbcDenseFactorization()
  : CoinAbcAnyFactorization()
{
  gutsOfInitialize();
}

/// Copy constructor
CoinAbcDenseFactorization::CoinAbcDenseFactorization(const CoinAbcDenseFactorization &other)
  : CoinAbcAnyFactorization(other)
{
  gutsOfInitialize();
  gutsOfCopy(other);
}
// Clone
CoinAbcAnyFactorization *
CoinAbcDenseFactorization::clone() const
{
  return new CoinAbcDenseFactorization(*this);
}
/// The real work of constructors etc
void CoinAbcDenseFactorization::gutsOfDestructor()
{
  delete[] elements_;
  delete[] pivotRow_;
  delete[] workArea_;
  elements_ = NULL;
  pivotRow_ = NULL;
  workArea_ = NULL;
  numberRows_ = 0;
  numberGoodU_ = 0;
  status_ = -1;
  maximumRows_ = 0;
#if ABC_PARALLEL == 2
  parallelMode_ = 0;
#endif
  maximumSpace_ = 0;
  maximumRowsAdjusted_ = 0;
  solveMode_ = 0;
}
void CoinAbcDenseFactorization::gutsOfInitialize()
{
  pivotTolerance_ = 1.0e-1;
  minimumPivotTolerance_ = 1.0e-1;
  zeroTolerance_ = 1.0e-13;
  areaFactor_ = 1.0;
  numberDense_ = 0;
  maximumPivots_ = 200;
  relaxCheck_ = 1.0;
  numberRows_ = 0;
  numberGoodU_ = 0;
  status_ = -1;
  numberSlacks_ = 0;
  numberPivots_ = 0;
  maximumRows_ = 0;
#if ABC_PARALLEL == 2
  parallelMode_ = 0;
#endif
  maximumSpace_ = 0;
  maximumRowsAdjusted_ = 0;
  elements_ = NULL;
  pivotRow_ = NULL;
  workArea_ = NULL;
  solveMode_ = 0;
}
//  ~CoinAbcDenseFactorization.  Destructor
CoinAbcDenseFactorization::~CoinAbcDenseFactorization()
{
  gutsOfDestructor();
}
//  =
CoinAbcDenseFactorization &CoinAbcDenseFactorization::operator=(const CoinAbcDenseFactorization &other)
{
  if (this != &other) {
    gutsOfDestructor();
    gutsOfInitialize();
    gutsOfCopy(other);
  }
  return *this;
}
#define WORK_MULT 2
void CoinAbcDenseFactorization::gutsOfCopy(const CoinAbcDenseFactorization &other)
{
  pivotTolerance_ = other.pivotTolerance_;
  minimumPivotTolerance_ = other.minimumPivotTolerance_;
  zeroTolerance_ = other.zeroTolerance_;
  areaFactor_ = other.areaFactor_;
  numberDense_ = other.numberDense_;
  relaxCheck_ = other.relaxCheck_;
  numberRows_ = other.numberRows_;
  maximumRows_ = other.maximumRows_;
#if ABC_PARALLEL == 2
  parallelMode_ = other.parallelMode_;
#endif
  maximumSpace_ = other.maximumSpace_;
  maximumRowsAdjusted_ = other.maximumRowsAdjusted_;
  solveMode_ = other.solveMode_;
  numberGoodU_ = other.numberGoodU_;
  maximumPivots_ = other.maximumPivots_;
  numberPivots_ = other.numberPivots_;
  factorElements_ = other.factorElements_;
  status_ = other.status_;
  numberSlacks_ = other.numberSlacks_;
  if (other.pivotRow_) {
    pivotRow_ = new int[2 * maximumRowsAdjusted_ + maximumPivots_];
    CoinMemcpyN(other.pivotRow_, (2 * maximumRowsAdjusted_ + numberPivots_), pivotRow_);
    elements_ = new CoinFactorizationDouble[maximumSpace_];
    CoinMemcpyN(other.elements_, (maximumRowsAdjusted_ + numberPivots_) * maximumRowsAdjusted_, elements_);
    workArea_ = new CoinFactorizationDouble[maximumRowsAdjusted_ * WORK_MULT];
    CoinZeroN(workArea_, maximumRowsAdjusted_ * WORK_MULT);
  } else {
    elements_ = NULL;
    pivotRow_ = NULL;
    workArea_ = NULL;
  }
}

//  getAreas.  Gets space for a factorization
//called by constructors
void CoinAbcDenseFactorization::getAreas(int numberOfRows,
  int /*numberOfColumns*/,
  CoinBigIndex,
  CoinBigIndex)
{

  numberRows_ = numberOfRows;
  numberDense_ = numberRows_;
  if ((numberRows_ & (BLOCKING8 - 1)) != 0)
    numberDense_ += (BLOCKING8 - (numberRows_ & (BLOCKING8 - 1)));
  CoinBigIndex size = numberDense_ * (2 * numberDense_ + CoinMax(maximumPivots_, (numberDense_ + 1) >> 1));
  if (size > maximumSpace_) {
    delete[] elements_;
    elements_ = new CoinFactorizationDouble[size];
    maximumSpace_ = size;
  }
  if (numberRows_ > maximumRows_) {
    maximumRows_ = numberRows_;
    maximumRowsAdjusted_ = maximumRows_;
    if ((maximumRows_ & (BLOCKING8 - 1)) != 0)
      maximumRowsAdjusted_ += (BLOCKING8 - (maximumRows_ & (BLOCKING8 - 1)));
    delete[] pivotRow_;
    delete[] workArea_;
    pivotRow_ = new int[2 * maximumRowsAdjusted_ + maximumPivots_];
    workArea_ = new CoinFactorizationDouble[maximumRowsAdjusted_ * WORK_MULT];
  }
}

//  preProcess.
void CoinAbcDenseFactorization::preProcess()
{
  CoinBigIndex put = numberDense_ * numberDense_;
  CoinFactorizationDouble *COIN_RESTRICT area = elements_ + maximumSpace_ - put;
  CoinAbcMemset0(area, put);
  int *indexRow = reinterpret_cast< int * >(elements_ + numberRows_ * numberRows_);
  CoinBigIndex *starts = reinterpret_cast< CoinBigIndex * >(pivotRow_);
  CoinFactorizationDouble *COIN_RESTRICT column = area;
  //if (solveMode_==10)
  //solveMode_=1;
  if ((solveMode_ % 10) != 0) {
    for (int i = 0; i < numberRows_; i++) {
      for (CoinBigIndex j = starts[i]; j < starts[i + 1]; j++) {
        int iRow = indexRow[j];
        int iBlock = iRow >> 3;
        iRow = iRow & (BLOCKING8 - 1);
        column[iRow + BLOCKING8X8 * iBlock] = elements_[j];
      }
      if ((i & (BLOCKING8 - 1)) != (BLOCKING8 - 1))
        column += BLOCKING8;
      else
        column += numberDense_ * BLOCKING8 - (BLOCKING8 - 1) * BLOCKING8;
    }
    for (int i = numberRows_; i < numberDense_; i++) {
      int iRow = i;
      int iBlock = iRow >> 3;
      iRow = iRow & (BLOCKING8 - 1);
      column[iRow + BLOCKING8X8 * iBlock] = 1.0;
      if ((i & (BLOCKING8 - 1)) != (BLOCKING8 - 1))
        column += BLOCKING8;
      //else
      //column += numberDense_*BLOCKING8-(BLOCKING8-1)*BLOCKING8;
    }
  } else {
    // first or bad
    for (int i = 0; i < numberRows_; i++) {
      for (CoinBigIndex j = starts[i]; j < starts[i + 1]; j++) {
        int iRow = indexRow[j];
        column[iRow] = elements_[j];
      }
      column += numberDense_;
    }
    for (int i = numberRows_; i < numberDense_; i++) {
      column[i] = 1.0;
      column += numberDense_;
    }
  }
}

//Does factorization
int CoinAbcDenseFactorization::factor(AbcSimplex * /*model*/)
{
  numberPivots_ = 0;
  // ? take out
  //printf("Debug non lapack dense factor\n");
  //solveMode_&=~1;
  CoinBigIndex put = numberDense_ * numberDense_;
  CoinFactorizationDouble *COIN_RESTRICT area = elements_ + maximumSpace_ - put;
  if ((solveMode_ % 10) != 0) {
    // save last start
    CoinBigIndex lastStart = pivotRow_[numberRows_];
    status_ = CoinAbcDgetrf(numberDense_, numberDense_, area, numberDense_, pivotRow_ + numberDense_
#if ABC_PARALLEL == 2
      ,
      parallelMode_
#endif
    );
    // need to check size of pivots
    if (!status_) {
      // OK
      solveMode_ = 1 + 10 * (solveMode_ / 10);
      numberGoodU_ = numberRows_;
      CoinZeroN(workArea_, 2 * numberRows_);
#if 0 //ndef NDEBUG
      const CoinFactorizationDouble * column = area;
      double smallest=COIN_DBL_MAX;
      for (int i=0;i<numberRows_;i++) {
	if (fabs(column[i])<smallest)
	  smallest = fabs(column[i]);
	column += numberRows_;
      }
      if (smallest<1.0e-8)
	printf("small el %g\n",smallest);
#endif
      return 0;
    } else {
      solveMode_ = 10 * (solveMode_ / 10);
      // redo matrix
      // last start
      pivotRow_[numberRows_] = lastStart;
      preProcess();
    }
  }
  status_ = 0;
  for (int j = 0; j < numberRows_; j++) {
    pivotRow_[j + numberDense_] = j;
  }
  CoinFactorizationDouble *elements = area;
  numberGoodU_ = 0;
  for (int i = 0; i < numberRows_; i++) {
    int iRow = -1;
    // Find largest
    double largest = zeroTolerance_;
    for (int j = i; j < numberRows_; j++) {
      double value = fabs(elements[j]);
      if (value > largest) {
        largest = value;
        iRow = j;
      }
    }
    if (iRow >= 0) {
      if (iRow != i) {
        // swap
        assert(iRow > i);
        CoinFactorizationDouble *elementsA = area;
        for (int k = 0; k <= i; k++) {
          // swap
          CoinFactorizationDouble value = elementsA[i];
          elementsA[i] = elementsA[iRow];
          elementsA[iRow] = value;
          elementsA += numberDense_;
        }
        int iPivot = pivotRow_[i + numberDense_];
        pivotRow_[i + numberDense_] = pivotRow_[iRow + numberDense_];
        pivotRow_[iRow + numberDense_] = iPivot;
      }
      CoinFactorizationDouble pivotValue = 1.0 / elements[i];
      elements[i] = pivotValue;
      for (int j = i + 1; j < numberRows_; j++) {
        elements[j] *= pivotValue;
      }
      // Update rest
      CoinFactorizationDouble *elementsA = elements;
      for (int k = i + 1; k < numberRows_; k++) {
        elementsA += numberDense_;
        // swap
        if (iRow != i) {
          CoinFactorizationDouble value = elementsA[i];
          elementsA[i] = elementsA[iRow];
          elementsA[iRow] = value;
        }
        CoinFactorizationDouble value = elementsA[i];
        for (int j = i + 1; j < numberRows_; j++) {
          elementsA[j] -= value * elements[j];
        }
      }
    } else {
      status_ = -1;
      break;
    }
    numberGoodU_++;
    elements += numberDense_;
  }
  for (int j = 0; j < numberRows_; j++) {
    int k = pivotRow_[j + numberDense_];
    pivotRow_[k] = j;
  }
  //assert (status_);
  //if (!status_)
  //solveMode_=10; // for next time
  //for (int j=0;j<numberRows_;j++) {
  //int k = pivotRow_[j+numberDense_];
  //pivotRow_[k]=j;
  //}
  return status_;
}
// Makes a non-singular basis by replacing variables
void CoinAbcDenseFactorization::makeNonSingular(int *sequence)
{
  // Replace bad ones by correct slack
  int *workArea = reinterpret_cast< int * >(workArea_);
  int i;
  for (i = 0; i < numberRows_; i++)
    workArea[i] = -1;
  for (i = 0; i < numberGoodU_; i++) {
    int iOriginal = pivotRow_[i + numberDense_];
    workArea[iOriginal] = i;
    //workArea[i]=iOriginal;
  }
  int lastRow = -1;
  for (i = 0; i < numberRows_; i++) {
    if (workArea[i] == -1) {
      lastRow = i;
      break;
    }
  }
  assert(lastRow >= 0);
  for (i = numberGoodU_; i < numberRows_; i++) {
    assert(lastRow < numberRows_);
    // Put slack in basis
    sequence[i] = lastRow;
    lastRow++;
    for (; lastRow < numberRows_; lastRow++) {
      if (workArea[lastRow] == -1)
        break;
    }
  }
}
//#define DENSE_PERMUTE
// Does post processing on valid factorization - putting variables on correct rows
void CoinAbcDenseFactorization::postProcess(const int *sequence, int *pivotVariable)
{
  if ((solveMode_ % 10) == 0) {
    for (int i = 0; i < numberRows_; i++) {
      int k = sequence[i];
#ifdef DENSE_PERMUTE
      pivotVariable[pivotRow_[i + numberDense_]] = k;
#else
      //pivotVariable[pivotRow_[i]]=k;
      //pivotVariable[pivotRow_[i]]=k;
      pivotVariable[i] = k;
      k = pivotRow_[i];
      pivotRow_[i] = pivotRow_[i + numberDense_];
      pivotRow_[i + numberDense_] = k;
#endif
    }
  } else {
    // lapack
    for (int i = 0; i < numberRows_; i++) {
      int k = sequence[i];
      pivotVariable[i] = k;
    }
  }
}
/* Replaces one Column to basis,
   returns 0=OK, 1=Probably OK, 2=singular, 3=no room
   partial update already in U */
int CoinAbcDenseFactorization::replaceColumn(CoinIndexedVector *regionSparse,
  int pivotRow,
  double pivotCheck,
  bool /*skipBtranU*/,
  double /*acceptablePivot*/)
{
  if (numberPivots_ == maximumPivots_)
    return 3;
  CoinFactorizationDouble *elements = elements_ + numberDense_ * numberPivots_;
  double *region = regionSparse->denseVector();
  int *regionIndex = regionSparse->getIndices();
  int numberNonZero = regionSparse->getNumElements();
  int i;
  memset(elements, 0, numberRows_ * sizeof(CoinFactorizationDouble));
  CoinFactorizationDouble pivotValue = pivotCheck;
  if (fabs(pivotValue) < zeroTolerance_)
    return 2;
  pivotValue = 1.0 / pivotValue;
  if ((solveMode_ % 10) == 0) {
    if (regionSparse->packedMode()) {
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[i];
#ifdef DENSE_PERMUTE
        iRow = pivotRow_[iRow]; // permute
#endif
        elements[iRow] = value;
        ;
      }
    } else {
      // not packed! - from user pivot?
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[iRow];
#ifdef DENSE_PERMUTE
        iRow = pivotRow_[iRow]; // permute
#endif
        elements[iRow] = value;
        ;
      }
    }
    int realPivotRow = pivotRow_[pivotRow];
    elements[realPivotRow] = pivotValue;
    pivotRow_[2 * numberDense_ + numberPivots_] = realPivotRow;
  } else {
    // lapack
    if (regionSparse->packedMode()) {
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[i];
        elements[iRow] = value;
        ;
      }
    } else {
      // not packed! - from user pivot?
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[iRow];
        elements[iRow] = value;
        ;
      }
    }
    elements[pivotRow] = pivotValue;
    pivotRow_[2 * numberDense_ + numberPivots_] = pivotRow;
  }
  numberPivots_++;
  return 0;
}
/* Checks if can replace one Column to basis,
   returns 0=OK, 1=Probably OK, 2=singular, 3=no room, 5 max pivots */
int CoinAbcDenseFactorization::checkReplacePart2(int pivotRow,
  double btranAlpha,
  double ftranAlpha,
#ifdef ABC_LONG_FACTORIZATION
  long
#endif
  double ftAlpha,
  double acceptablePivot)
{
  if (numberPivots_ == maximumPivots_)
    return 3;
  if (fabs(ftranAlpha) < zeroTolerance_)
    return 2;
  return 0;
}
/* Replaces one Column to basis,
   partial update already in U */
void CoinAbcDenseFactorization::replaceColumnPart3(const AbcSimplex *model,
  CoinIndexedVector *regionSparse,
  CoinIndexedVector *tableauColumn,
  int pivotRow,
#ifdef ABC_LONG_FACTORIZATION
  long
#endif
  double alpha)
{
  CoinFactorizationDouble *elements = elements_ + numberDense_ * numberPivots_;
  double *region = tableauColumn->denseVector();
  int *regionIndex = tableauColumn->getIndices();
  int numberNonZero = tableauColumn->getNumElements();
  int i;
  memset(elements, 0, numberRows_ * sizeof(CoinFactorizationDouble));
  double pivotValue = 1.0 / alpha;
  if ((solveMode_ % 10) == 0) {
    if (tableauColumn->packedMode()) {
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[i];
#ifdef DENSE_PERMUTE
        iRow = pivotRow_[iRow]; // permute
#endif
        elements[iRow] = value;
        ;
      }
    } else {
      // not packed! - from user pivot?
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[iRow];
#ifdef DENSE_PERMUTE
        iRow = pivotRow_[iRow]; // permute
#endif
        elements[iRow] = value;
        ;
      }
    }
    int realPivotRow = pivotRow_[pivotRow];
    elements[realPivotRow] = pivotValue;
    pivotRow_[2 * numberDense_ + numberPivots_] = realPivotRow;
  } else {
    // lapack
    if (tableauColumn->packedMode()) {
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[i];
        elements[iRow] = value;
        ;
      }
    } else {
      // not packed! - from user pivot?
      for (i = 0; i < numberNonZero; i++) {
        int iRow = regionIndex[i];
        double value = region[iRow];
        elements[iRow] = value;
        ;
      }
    }
    elements[pivotRow] = pivotValue;
    pivotRow_[2 * numberDense_ + numberPivots_] = pivotRow;
  }
  numberPivots_++;
}
static void
CoinAbcDlaswp1(double *COIN_RESTRICT a, int m, int *ipiv)
{
  for (int i = 0; i < m; i++) {
    int ip = ipiv[i];
    if (ip != i) {
      double temp = a[i];
      a[i] = a[ip];
      a[ip] = temp;
    }
  }
}
static void
CoinAbcDlaswp1Backwards(double *COIN_RESTRICT a, int m, int *ipiv)
{
  for (int i = m - 1; i >= 0; i--) {
    int ip = ipiv[i];
    if (ip != i) {
      double temp = a[i];
      a[i] = a[ip];
      a[ip] = temp;
    }
  }
}
/* This version has same effect as above with FTUpdate==false
   so number returned is always >=0 */
int CoinAbcDenseFactorization::updateColumn(CoinIndexedVector &regionSparse) const
{
  double *region = regionSparse.denseVector();
  CoinBigIndex put = numberDense_ * numberDense_;
  CoinFactorizationDouble *COIN_RESTRICT area = elements_ + maximumSpace_ - put;
  CoinFactorizationDouble *COIN_RESTRICT elements = area;
  if ((solveMode_ % 10) == 0) {
    // base factorization L
    for (int i = 0; i < numberRows_; i++) {
      double value = region[i];
      for (int j = i + 1; j < numberRows_; j++) {
        region[j] -= value * elements[j];
      }
      elements += numberDense_;
    }
    elements = area + numberRows_ * numberDense_;
    // base factorization U
    for (int i = numberRows_ - 1; i >= 0; i--) {
      elements -= numberDense_;
      CoinFactorizationDouble value = region[i] * elements[i];
      region[i] = value;
      for (int j = 0; j < i; j++) {
        region[j] -= value * elements[j];
      }
    }
  } else {
    CoinAbcDlaswp1(region, numberRows_, pivotRow_ + numberDense_);
    CoinAbcDgetrs('N', numberDense_, area, region);
  }
  // now updates
  elements = elements_;
  for (int i = 0; i < numberPivots_; i++) {
    int iPivot = pivotRow_[i + 2 * numberDense_];
    CoinFactorizationDouble value = region[iPivot] * elements[iPivot];
    for (int j = 0; j < numberRows_; j++) {
      region[j] -= value * elements[j];
    }
    region[iPivot] = value;
    elements += numberDense_;
  }
  regionSparse.setNumElements(0);
  regionSparse.scan(0, numberRows_);
  return 0;
}

/* Updates one column for dual steepest edge weights (FTRAN) */
void CoinAbcDenseFactorization::updateWeights(CoinIndexedVector &regionSparse) const
{
  updateColumn(regionSparse);
}

int CoinAbcDenseFactorization::updateTwoColumnsFT(CoinIndexedVector &regionSparse2,
  CoinIndexedVector &regionSparse3)
{
  updateColumn(regionSparse2);
  updateColumn(regionSparse3);
  return 0;
}

/* Updates one column (BTRAN) from regionSparse2
   regionSparse starts as zero and is zero at end 
   Note - if regionSparse2 packed on input - will be packed on output
*/
int CoinAbcDenseFactorization::updateColumnTranspose(CoinIndexedVector &regionSparse2) const
{
  double *region = regionSparse2.denseVector();
  CoinFactorizationDouble *elements = elements_ + numberDense_ * numberPivots_;
  // updates
  for (int i = numberPivots_ - 1; i >= 0; i--) {
    elements -= numberDense_;
    int iPivot = pivotRow_[i + 2 * numberDense_];
    CoinFactorizationDouble value = region[iPivot]; //*elements[iPivot];
    for (int j = 0; j < iPivot; j++) {
      value -= region[j] * elements[j];
    }
    for (int j = iPivot + 1; j < numberRows_; j++) {
      value -= region[j] * elements[j];
    }
    region[iPivot] = value * elements[iPivot];
  }
  CoinBigIndex put = numberDense_ * numberDense_;
  CoinFactorizationDouble *COIN_RESTRICT area = elements_ + maximumSpace_ - put;
  if ((solveMode_ % 10) == 0) {
    // base factorization U
    elements = area;
    for (int i = 0; i < numberRows_; i++) {
      //CoinFactorizationDouble value = region[i]*elements[i];
      CoinFactorizationDouble value = region[i];
      for (int j = 0; j < i; j++) {
        value -= region[j] * elements[j];
      }
      //region[i] = value;
      region[i] = value * elements[i];
      elements += numberDense_;
    }
    // base factorization L
    elements = area + numberDense_ * numberRows_;
    for (int i = numberRows_ - 1; i >= 0; i--) {
      elements -= numberDense_;
      CoinFactorizationDouble value = region[i];
      for (int j = i + 1; j < numberRows_; j++) {
        value -= region[j] * elements[j];
      }
      region[i] = value;
    }
  } else {
    CoinAbcDgetrs('T', numberDense_, area, region);
    CoinAbcDlaswp1Backwards(region, numberRows_, pivotRow_ + numberDense_);
  }
  regionSparse2.setNumElements(0);
  regionSparse2.scan(0, numberRows_);
  return 0;
}
/* Updates one column (FTRAN) */
void CoinAbcAnyFactorization::updateColumnCpu(CoinIndexedVector &regionSparse, int whichCpu) const
{
  updateColumn(regionSparse);
}
/* Updates one column (BTRAN) */
void CoinAbcAnyFactorization::updateColumnTransposeCpu(CoinIndexedVector &regionSparse, int whichCpu) const
{
  updateColumnTranspose(regionSparse);
}
// Default constructor
CoinAbcAnyFactorization::CoinAbcAnyFactorization()
  : pivotTolerance_(1.0e-1)
  , minimumPivotTolerance_(1.0e-1)
  , zeroTolerance_(1.0e-13)
  , relaxCheck_(1.0)
  , factorElements_(0)
  , numberRows_(0)
  , numberGoodU_(0)
  , maximumPivots_(200)
  , numberPivots_(0)
  , status_(-1)
  , solveMode_(0)
{
}
// Copy constructor
CoinAbcAnyFactorization::CoinAbcAnyFactorization(const CoinAbcAnyFactorization &other)
  : pivotTolerance_(other.pivotTolerance_)
  , minimumPivotTolerance_(other.minimumPivotTolerance_)
  , zeroTolerance_(other.zeroTolerance_)
  , relaxCheck_(other.relaxCheck_)
  , factorElements_(other.factorElements_)
  , numberRows_(other.numberRows_)
  , numberGoodU_(other.numberGoodU_)
  , maximumPivots_(other.maximumPivots_)
  , numberPivots_(other.numberPivots_)
  , status_(other.status_)
  , solveMode_(other.solveMode_)
{
}
// Destructor
CoinAbcAnyFactorization::~CoinAbcAnyFactorization()
{
}
// = copy
CoinAbcAnyFactorization &CoinAbcAnyFactorization::operator=(const CoinAbcAnyFactorization &other)
{
  if (this != &other) {
    pivotTolerance_ = other.pivotTolerance_;
    minimumPivotTolerance_ = other.minimumPivotTolerance_;
    zeroTolerance_ = other.zeroTolerance_;
    relaxCheck_ = other.relaxCheck_;
    factorElements_ = other.factorElements_;
    numberRows_ = other.numberRows_;
    numberGoodU_ = other.numberGoodU_;
    maximumPivots_ = other.maximumPivots_;
    numberPivots_ = other.numberPivots_;
    status_ = other.status_;
    solveMode_ = other.solveMode_;
  }
  return *this;
}
void CoinAbcAnyFactorization::pivotTolerance(double value)
{
  if (value > 0.0 && value <= 1.0) {
    pivotTolerance_ = CoinMax(value, minimumPivotTolerance_);
  }
}
void CoinAbcAnyFactorization::minimumPivotTolerance(double value)
{
  if (value > 0.0 && value <= 1.0) {
    minimumPivotTolerance_ = value;
  }
}
void CoinAbcAnyFactorization::zeroTolerance(double value)
{
  if (value > 0.0 && value < 1.0) {
    zeroTolerance_ = value;
  }
}
void CoinAbcAnyFactorization::maximumPivots(int value)
{
  if (value > maximumPivots_) {
    delete[] pivotRow_;
    int n = maximumRows_;
    if ((n & (BLOCKING8 - 1)) != 0)
      n += (BLOCKING8 - (n & (BLOCKING8 - 1)));
    pivotRow_ = new int[2 * n + value];
  }
  maximumPivots_ = value;
}
// Number of entries in each row
int *CoinAbcAnyFactorization::numberInRow() const
{
  return reinterpret_cast< int * >(workArea_);
}
// Number of entries in each column
int *CoinAbcAnyFactorization::numberInColumn() const
{
  return (reinterpret_cast< int * >(workArea_)) + numberRows_;
}
// Returns array to put basis starts in
CoinBigIndex *
CoinAbcAnyFactorization::starts() const
{
  return reinterpret_cast< CoinBigIndex * >(pivotRow_);
}
// Returns array to put basis elements in
CoinFactorizationDouble *
CoinAbcAnyFactorization::elements() const
{
  return elements_;
}
// Returns pivot row
int *CoinAbcAnyFactorization::pivotRow() const
{
  return pivotRow_;
}
// Returns work area
CoinFactorizationDouble *
CoinAbcAnyFactorization::workArea() const
{
  return workArea_;
}
// Returns int work area
int *CoinAbcAnyFactorization::intWorkArea() const
{
  return reinterpret_cast< int * >(workArea_);
}
// Returns permute back
int *CoinAbcAnyFactorization::permuteBack() const
{
  return pivotRow_ + numberRows_;
}
// Returns pivot column
int *CoinAbcAnyFactorization::pivotColumn() const
{
  return permute();
}
// Returns true if wants tableauColumn in replaceColumn
bool CoinAbcAnyFactorization::wantsTableauColumn() const
{
  return true;
}
/* Useful information for factorization
   0 - iteration number
   whereFrom is 0 for factorize and 1 for replaceColumn
*/
void CoinAbcAnyFactorization::setUsefulInformation(const int *, int)
{
}

/* vi: softtabstop=2 shiftwidth=2 expandtab tabstop=2
*/
