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ClpNetworkMatrix.cpp
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/* $Id$ */
// Copyright (C) 2003, International Business Machines
// Corporation and others. All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).
#include <cstdio>
#include "CoinPragma.hpp"
#include "CoinIndexedVector.hpp"
#include "CoinHelperFunctions.hpp"
#include "CoinPackedVector.hpp"
#include "ClpSimplex.hpp"
#include "ClpFactorization.hpp"
// at end to get min/max!
#include "ClpNetworkMatrix.hpp"
#include "ClpPlusMinusOneMatrix.hpp"
#include "ClpMessage.hpp"
#include <iostream>
#include <cassert>
//#############################################################################
// Constructors / Destructor / Assignment
//#############################################################################
//-------------------------------------------------------------------
// Default Constructor
//-------------------------------------------------------------------
ClpNetworkMatrix::ClpNetworkMatrix()
: ClpMatrixBase()
{
setType(11);
matrix_ = NULL;
lengths_ = NULL;
indices_ = NULL;
numberRows_ = 0;
numberColumns_ = 0;
trueNetwork_ = false;
}
/* Constructor from two arrays */
ClpNetworkMatrix::ClpNetworkMatrix(int numberColumns, const int *head,
const int *tail)
: ClpMatrixBase()
{
setType(11);
matrix_ = NULL;
lengths_ = NULL;
indices_ = new int[2 * numberColumns];
;
numberRows_ = -1;
numberColumns_ = numberColumns;
trueNetwork_ = true;
int iColumn;
CoinBigIndex j = 0;
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
int iRow = head[iColumn];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j] = iRow;
iRow = tail[iColumn];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j + 1] = iRow;
}
numberRows_++;
}
//-------------------------------------------------------------------
// Copy constructor
//-------------------------------------------------------------------
ClpNetworkMatrix::ClpNetworkMatrix(const ClpNetworkMatrix &rhs)
: ClpMatrixBase(rhs)
{
matrix_ = NULL;
lengths_ = NULL;
indices_ = NULL;
numberRows_ = rhs.numberRows_;
numberColumns_ = rhs.numberColumns_;
trueNetwork_ = rhs.trueNetwork_;
if (numberColumns_) {
indices_ = new int[2 * numberColumns_];
CoinMemcpyN(rhs.indices_, 2 * numberColumns_, indices_);
}
int numberRows = getNumRows();
if (rhs.rhsOffset_ && numberRows) {
rhsOffset_ = ClpCopyOfArray(rhs.rhsOffset_, numberRows);
} else {
rhsOffset_ = NULL;
}
}
ClpNetworkMatrix::ClpNetworkMatrix(const CoinPackedMatrix &rhs)
: ClpMatrixBase()
{
setType(11);
matrix_ = NULL;
lengths_ = NULL;
indices_ = NULL;
int iColumn;
assert(rhs.isColOrdered());
// get matrix data pointers
const int *row = rhs.getIndices();
const CoinBigIndex *columnStart = rhs.getVectorStarts();
const int *columnLength = rhs.getVectorLengths();
const double *elementByColumn = rhs.getElements();
numberColumns_ = rhs.getNumCols();
int goodNetwork = 1;
numberRows_ = -1;
indices_ = new int[2 * numberColumns_];
CoinBigIndex j = 0;
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
CoinBigIndex k = columnStart[iColumn];
int iRow;
switch (columnLength[iColumn]) {
case 0:
goodNetwork = -1; // not classic network
indices_[j] = -1;
indices_[j + 1] = -1;
break;
case 1:
goodNetwork = -1; // not classic network
if (fabs(elementByColumn[k] - 1.0) < 1.0e-10) {
indices_[j] = -1;
iRow = row[k];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j + 1] = iRow;
} else if (fabs(elementByColumn[k] + 1.0) < 1.0e-10) {
indices_[j + 1] = -1;
iRow = row[k];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j] = iRow;
} else {
goodNetwork = 0; // not a network
}
break;
case 2:
if (fabs(elementByColumn[k] - 1.0) < 1.0e-10) {
if (fabs(elementByColumn[k + 1] + 1.0) < 1.0e-10) {
iRow = row[k];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j + 1] = iRow;
iRow = row[k + 1];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j] = iRow;
} else {
goodNetwork = 0; // not a network
}
} else if (fabs(elementByColumn[k] + 1.0) < 1.0e-10) {
if (fabs(elementByColumn[k + 1] - 1.0) < 1.0e-10) {
iRow = row[k];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j] = iRow;
iRow = row[k + 1];
numberRows_ = CoinMax(numberRows_, iRow);
indices_[j + 1] = iRow;
} else {
goodNetwork = 0; // not a network
}
} else {
goodNetwork = 0; // not a network
}
break;
default:
goodNetwork = 0; // not a network
break;
}
if (!goodNetwork)
break;
}
if (!goodNetwork) {
delete[] indices_;
// put in message
printf("Not a network - can test if indices_ null\n");
indices_ = NULL;
numberRows_ = 0;
numberColumns_ = 0;
} else {
numberRows_++; // correct
trueNetwork_ = goodNetwork > 0;
}
}
//-------------------------------------------------------------------
// Destructor
//-------------------------------------------------------------------
ClpNetworkMatrix::~ClpNetworkMatrix()
{
delete matrix_;
delete[] lengths_;
delete[] indices_;
}
//----------------------------------------------------------------
// Assignment operator
//-------------------------------------------------------------------
ClpNetworkMatrix &
ClpNetworkMatrix::operator=(const ClpNetworkMatrix &rhs)
{
if (this != &rhs) {
ClpMatrixBase::operator=(rhs);
delete matrix_;
delete[] lengths_;
delete[] indices_;
matrix_ = NULL;
lengths_ = NULL;
indices_ = NULL;
numberRows_ = rhs.numberRows_;
numberColumns_ = rhs.numberColumns_;
trueNetwork_ = rhs.trueNetwork_;
if (numberColumns_) {
indices_ = new int[2 * numberColumns_];
CoinMemcpyN(rhs.indices_, 2 * numberColumns_, indices_);
}
}
return *this;
}
//-------------------------------------------------------------------
// Clone
//-------------------------------------------------------------------
ClpMatrixBase *ClpNetworkMatrix::clone() const
{
return new ClpNetworkMatrix(*this);
}
/* Returns a new matrix in reverse order without gaps */
ClpMatrixBase *
ClpNetworkMatrix::reverseOrderedCopy() const
{
// count number in each row
CoinBigIndex *tempP = new CoinBigIndex[numberRows_];
CoinBigIndex *tempN = new CoinBigIndex[numberRows_];
memset(tempP, 0, numberRows_ * sizeof(CoinBigIndex));
memset(tempN, 0, numberRows_ * sizeof(CoinBigIndex));
CoinBigIndex j = 0;
int i;
for (i = 0; i < numberColumns_; i++, j += 2) {
int iRow = indices_[j];
tempN[iRow]++;
iRow = indices_[j + 1];
tempP[iRow]++;
}
int *newIndices = new int[2 * numberColumns_];
CoinBigIndex *newP = new CoinBigIndex[numberRows_ + 1];
CoinBigIndex *newN = new CoinBigIndex[numberRows_];
int iRow;
j = 0;
// do starts
for (iRow = 0; iRow < numberRows_; iRow++) {
newP[iRow] = j;
j += tempP[iRow];
tempP[iRow] = newP[iRow];
newN[iRow] = j;
j += tempN[iRow];
tempN[iRow] = newN[iRow];
}
newP[numberRows_] = j;
j = 0;
for (i = 0; i < numberColumns_; i++, j += 2) {
int iRow = indices_[j];
CoinBigIndex put = tempN[iRow];
newIndices[put++] = i;
tempN[iRow] = put;
iRow = indices_[j + 1];
put = tempP[iRow];
newIndices[put++] = i;
tempP[iRow] = put;
}
delete[] tempP;
delete[] tempN;
ClpPlusMinusOneMatrix *newCopy = new ClpPlusMinusOneMatrix();
newCopy->passInCopy(numberRows_, numberColumns_,
false, newIndices, newP, newN);
return newCopy;
}
//unscaled versions
void ClpNetworkMatrix::times(double scalar,
const double *x, double *y) const
{
int iColumn;
CoinBigIndex j = 0;
if (trueNetwork_) {
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = scalar * x[iColumn];
if (value) {
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
y[iRowM] -= value;
y[iRowP] += value;
}
}
} else {
// skip negative rows
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = scalar * x[iColumn];
if (value) {
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
y[iRowM] -= value;
if (iRowP >= 0)
y[iRowP] += value;
}
}
}
}
void ClpNetworkMatrix::transposeTimes(double scalar,
const double *x, double *y) const
{
int iColumn;
CoinBigIndex j = 0;
if (trueNetwork_) {
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = y[iColumn];
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
value -= scalar * x[iRowM];
value += scalar * x[iRowP];
y[iColumn] = value;
}
} else {
// skip negative rows
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = y[iColumn];
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
value -= scalar * x[iRowM];
if (iRowP >= 0)
value += scalar * x[iRowP];
y[iColumn] = value;
}
}
}
void ClpNetworkMatrix::times(double scalar,
const double *x, double *y,
const double * /*rowScale*/,
const double * /*columnScale*/) const
{
// we know it is not scaled
times(scalar, x, y);
}
void ClpNetworkMatrix::transposeTimes(double scalar,
const double *x, double *y,
const double * /*rowScale*/,
const double * /*columnScale*/,
double * /*spare*/) const
{
// we know it is not scaled
transposeTimes(scalar, x, y);
}
/* Return <code>x * A + y</code> in <code>z</code>.
Squashes small elements and knows about ClpSimplex */
void ClpNetworkMatrix::transposeTimes(const ClpSimplex *model, double scalar,
const CoinIndexedVector *rowArray,
CoinIndexedVector *y,
CoinIndexedVector *columnArray) const
{
// we know it is not scaled
columnArray->clear();
double *pi = rowArray->denseVector();
int numberNonZero = 0;
int *index = columnArray->getIndices();
double *array = columnArray->denseVector();
int numberInRowArray = rowArray->getNumElements();
// maybe I need one in OsiSimplex
double zeroTolerance = model->zeroTolerance();
int numberRows = model->numberRows();
#ifndef NO_RTTI
ClpPlusMinusOneMatrix *rowCopy = dynamic_cast< ClpPlusMinusOneMatrix * >(model->rowCopy());
#else
ClpPlusMinusOneMatrix *rowCopy = static_cast< ClpPlusMinusOneMatrix * >(model->rowCopy());
#endif
bool packed = rowArray->packedMode();
double factor = 0.3;
// We may not want to do by row if there may be cache problems
int numberColumns = model->numberColumns();
// It would be nice to find L2 cache size - for moment 512K
// Be slightly optimistic
if (numberColumns * sizeof(double) > 1000000) {
if (numberRows * 10 < numberColumns)
factor = 0.1;
else if (numberRows * 4 < numberColumns)
factor = 0.15;
else if (numberRows * 2 < numberColumns)
factor = 0.2;
//if (model->numberIterations()%50==0)
//printf("%d nonzero\n",numberInRowArray);
}
if (numberInRowArray > factor * numberRows || !rowCopy) {
// do by column
int iColumn;
assert(!y->getNumElements());
CoinBigIndex j = 0;
if (packed) {
// need to expand pi into y
assert(y->capacity() >= numberRows);
double *piOld = pi;
pi = y->denseVector();
const int *whichRow = rowArray->getIndices();
int i;
// modify pi so can collapse to one loop
for (i = 0; i < numberInRowArray; i++) {
int iRow = whichRow[i];
pi[iRow] = scalar * piOld[i];
}
if (trueNetwork_) {
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = 0.0;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
value -= pi[iRowM];
value += pi[iRowP];
if (fabs(value) > zeroTolerance) {
array[numberNonZero] = value;
index[numberNonZero++] = iColumn;
}
}
} else {
// skip negative rows
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = 0.0;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
value -= pi[iRowM];
if (iRowP >= 0)
value += pi[iRowP];
if (fabs(value) > zeroTolerance) {
array[numberNonZero] = value;
index[numberNonZero++] = iColumn;
}
}
}
for (i = 0; i < numberInRowArray; i++) {
int iRow = whichRow[i];
pi[iRow] = 0.0;
}
} else {
if (trueNetwork_) {
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = 0.0;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
value -= scalar * pi[iRowM];
value += scalar * pi[iRowP];
if (fabs(value) > zeroTolerance) {
index[numberNonZero++] = iColumn;
array[iColumn] = value;
}
}
} else {
// skip negative rows
for (iColumn = 0; iColumn < numberColumns_; iColumn++, j += 2) {
double value = 0.0;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
value -= scalar * pi[iRowM];
if (iRowP >= 0)
value += scalar * pi[iRowP];
if (fabs(value) > zeroTolerance) {
index[numberNonZero++] = iColumn;
array[iColumn] = value;
}
}
}
}
columnArray->setNumElements(numberNonZero);
} else {
// do by row
rowCopy->transposeTimesByRow(model, scalar, rowArray, y, columnArray);
}
}
/* Return <code>x *A in <code>z</code> but
just for indices in y. */
void ClpNetworkMatrix::subsetTransposeTimes(const ClpSimplex * /*model*/,
const CoinIndexedVector *rowArray,
const CoinIndexedVector *y,
CoinIndexedVector *columnArray) const
{
columnArray->clear();
double *pi = rowArray->denseVector();
double *array = columnArray->denseVector();
int jColumn;
int numberToDo = y->getNumElements();
const int *which = y->getIndices();
assert(!rowArray->packedMode());
columnArray->setPacked();
if (trueNetwork_) {
for (jColumn = 0; jColumn < numberToDo; jColumn++) {
int iColumn = which[jColumn];
double value = 0.0;
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
value -= pi[iRowM];
value += pi[iRowP];
array[jColumn] = value;
}
} else {
// skip negative rows
for (jColumn = 0; jColumn < numberToDo; jColumn++) {
int iColumn = which[jColumn];
double value = 0.0;
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
value -= pi[iRowM];
if (iRowP >= 0)
value += pi[iRowP];
array[jColumn] = value;
}
}
}
/// returns number of elements in column part of basis,
int ClpNetworkMatrix::countBasis(const int *whichColumn,
int &numberColumnBasic)
{
int i;
CoinBigIndex numberElements = 0;
if (trueNetwork_) {
numberElements = 2 * numberColumnBasic;
} else {
for (i = 0; i < numberColumnBasic; i++) {
int iColumn = whichColumn[i];
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
numberElements++;
if (iRowP >= 0)
numberElements++;
}
}
#if COIN_BIG_INDEX
if (numberElements > COIN_INT_MAX) {
printf("Factorization too large\n");
abort();
}
#endif
return static_cast< int >(numberElements);
}
void ClpNetworkMatrix::fillBasis(ClpSimplex * /*model*/,
const int *whichColumn,
int &numberColumnBasic,
int *indexRowU, int *start,
int *rowCount, int *columnCount,
CoinFactorizationDouble *elementU)
{
int i;
CoinBigIndex numberElements = start[0];
if (trueNetwork_) {
for (i = 0; i < numberColumnBasic; i++) {
int iColumn = whichColumn[i];
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
indexRowU[numberElements] = iRowM;
rowCount[iRowM]++;
elementU[numberElements] = -1.0;
indexRowU[numberElements + 1] = iRowP;
rowCount[iRowP]++;
elementU[numberElements + 1] = 1.0;
numberElements += 2;
start[i + 1] = static_cast< int >(numberElements);
columnCount[i] = 2;
}
} else {
for (i = 0; i < numberColumnBasic; i++) {
int iColumn = whichColumn[i];
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0) {
indexRowU[numberElements] = iRowM;
rowCount[iRowM]++;
elementU[numberElements++] = -1.0;
}
if (iRowP >= 0) {
indexRowU[numberElements] = iRowP;
rowCount[iRowP]++;
elementU[numberElements++] = 1.0;
}
start[i + 1] = static_cast< int >(numberElements);
columnCount[i] = static_cast< int >(numberElements) - start[i];
}
}
#if COIN_BIG_INDEX
if (numberElements > COIN_INT_MAX)
abort();
#endif
}
/* Unpacks a column into an CoinIndexedvector
*/
void ClpNetworkMatrix::unpack(const ClpSimplex * /*model*/, CoinIndexedVector *rowArray,
int iColumn) const
{
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
rowArray->add(iRowM, -1.0);
if (iRowP >= 0)
rowArray->add(iRowP, 1.0);
}
/* Unpacks a column into an CoinIndexedvector
** in packed foramt
Note that model is NOT const. Bounds and objective could
be modified if doing column generation (just for this variable) */
void ClpNetworkMatrix::unpackPacked(ClpSimplex * /*model*/,
CoinIndexedVector *rowArray,
int iColumn) const
{
int *index = rowArray->getIndices();
double *array = rowArray->denseVector();
int number = 0;
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0) {
array[number] = -1.0;
index[number++] = iRowM;
}
if (iRowP >= 0) {
array[number] = 1.0;
index[number++] = iRowP;
}
rowArray->setNumElements(number);
rowArray->setPackedMode(true);
}
/* Adds multiple of a column into an CoinIndexedvector
You can use quickAdd to add to vector */
void ClpNetworkMatrix::add(const ClpSimplex * /*model*/, CoinIndexedVector *rowArray,
int iColumn, double multiplier) const
{
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
rowArray->quickAdd(iRowM, -multiplier);
if (iRowP >= 0)
rowArray->quickAdd(iRowP, multiplier);
}
/* Adds multiple of a column into an array */
void ClpNetworkMatrix::add(const ClpSimplex * /*model*/, double *array,
int iColumn, double multiplier) const
{
CoinBigIndex j = iColumn << 1;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
array[iRowM] -= multiplier;
if (iRowP >= 0)
array[iRowP] += multiplier;
}
// Return a complete CoinPackedMatrix
CoinPackedMatrix *
ClpNetworkMatrix::getPackedMatrix() const
{
if (!matrix_) {
assert(trueNetwork_); // fix later
int numberElements = 2 * numberColumns_;
double *elements = new double[numberElements];
CoinBigIndex i;
for (i = 0; i < 2 * numberColumns_; i += 2) {
elements[i] = -1.0;
elements[i + 1] = 1.0;
}
CoinBigIndex *starts = new CoinBigIndex[numberColumns_ + 1];
for (i = 0; i < numberColumns_ + 1; i++) {
starts[i] = 2 * i;
}
// use assignMatrix to save space
delete[] lengths_;
lengths_ = NULL;
matrix_ = new CoinPackedMatrix();
int *indices = CoinCopyOfArray(indices_, 2 * numberColumns_);
matrix_->assignMatrix(true, numberRows_, numberColumns_,
getNumElements(),
elements, indices,
starts, lengths_);
assert(!elements);
assert(!starts);
assert(!indices);
assert(!lengths_);
}
return matrix_;
}
/* A vector containing the elements in the packed matrix. Note that there
might be gaps in this list, entries that do not belong to any
major-dimension vector. To get the actual elements one should look at
this vector together with vectorStarts and vectorLengths. */
const double *
ClpNetworkMatrix::getElements() const
{
if (!matrix_)
getPackedMatrix();
return matrix_->getElements();
}
const CoinBigIndex *
ClpNetworkMatrix::getVectorStarts() const
{
if (!matrix_)
getPackedMatrix();
return matrix_->getVectorStarts();
}
/* The lengths of the major-dimension vectors. */
const int *
ClpNetworkMatrix::getVectorLengths() const
{
assert(trueNetwork_); // fix later
if (!lengths_) {
lengths_ = new int[numberColumns_];
int i;
for (i = 0; i < numberColumns_; i++) {
lengths_[i] = 2;
}
}
return lengths_;
}
/* Delete the columns whose indices are listed in <code>indDel</code>. */
void ClpNetworkMatrix::deleteCols(const int numDel, const int *indDel)
{
assert(trueNetwork_);
int iColumn;
int numberBad = 0;
// Use array to make sure we can have duplicates
char *which = new char[numberColumns_];
memset(which, 0, numberColumns_);
int nDuplicate = 0;
for (iColumn = 0; iColumn < numDel; iColumn++) {
int jColumn = indDel[iColumn];
if (jColumn < 0 || jColumn >= numberColumns_) {
numberBad++;
} else {
if (which[jColumn])
nDuplicate++;
else
which[jColumn] = 1;
}
}
if (numberBad)
throw CoinError("Indices out of range", "deleteCols", "ClpNetworkMatrix");
int newNumber = numberColumns_ - numDel + nDuplicate;
// Get rid of temporary arrays
delete[] lengths_;
lengths_ = NULL;
delete matrix_;
matrix_ = NULL;
int newSize = 2 * newNumber;
int *newIndices = new int[newSize];
newSize = 0;
for (iColumn = 0; iColumn < numberColumns_; iColumn++) {
if (!which[iColumn]) {
CoinBigIndex start;
CoinBigIndex i;
start = 2 * iColumn;
for (i = start; i < start + 2; i++)
newIndices[newSize++] = indices_[i];
}
}
delete[] which;
delete[] indices_;
indices_ = newIndices;
numberColumns_ = newNumber;
}
/* Delete the rows whose indices are listed in <code>indDel</code>. */
void ClpNetworkMatrix::deleteRows(const int numDel, const int *indDel)
{
int iRow;
int numberBad = 0;
// Use array to make sure we can have duplicates
int *which = new int[numberRows_];
memset(which, 0, numberRows_ * sizeof(int));
for (iRow = 0; iRow < numDel; iRow++) {
int jRow = indDel[iRow];
if (jRow < 0 || jRow >= numberRows_) {
numberBad++;
} else {
which[jRow] = 1;
}
}
if (numberBad)
throw CoinError("Indices out of range", "deleteRows", "ClpNetworkMatrix");
// Only valid of all columns have 0 entries
int iColumn;
for (iColumn = 0; iColumn < numberColumns_; iColumn++) {
CoinBigIndex start;
CoinBigIndex i;
start = 2 * iColumn;
for (i = start; i < start + 2; i++) {
int iRow = indices_[i];
if (which[iRow])
numberBad++;
}
}
if (numberBad)
throw CoinError("Row has entries", "deleteRows", "ClpNetworkMatrix");
int newNumber = 0;
for (iRow = 0; iRow < numberRows_; iRow++) {
if (!which[iRow])
which[iRow] = newNumber++;
else
which[iRow] = -1;
}
for (iColumn = 0; iColumn < numberColumns_; iColumn++) {
CoinBigIndex start;
CoinBigIndex i;
start = 2 * iColumn;
for (i = start; i < start + 2; i++) {
int iRow = indices_[i];
indices_[i] = which[iRow];
}
}
delete[] which;
numberRows_ = newNumber;
}
/* Given positive integer weights for each row fills in sum of weights
for each column (and slack).
Returns weights vector
*/
CoinBigIndex *
ClpNetworkMatrix::dubiousWeights(const ClpSimplex *model, int *inputWeights) const
{
int numberRows = model->numberRows();
int numberColumns = model->numberColumns();
int number = numberRows + numberColumns;
CoinBigIndex *weights = new CoinBigIndex[number];
int i;
for (i = 0; i < numberColumns; i++) {
CoinBigIndex j = i << 1;
CoinBigIndex count = 0;
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0) {
count += inputWeights[iRowM];
}
if (iRowP >= 0) {
count += inputWeights[iRowP];
}
weights[i] = count;
}
for (i = 0; i < numberRows; i++) {
weights[i + numberColumns] = inputWeights[i];
}
return weights;
}
/* Returns largest and smallest elements of both signs.
Largest refers to largest absolute value.
*/
void ClpNetworkMatrix::rangeOfElements(double &smallestNegative, double &largestNegative,
double &smallestPositive, double &largestPositive)
{
smallestNegative = -1.0;
largestNegative = -1.0;
smallestPositive = 1.0;
largestPositive = 1.0;
}
// Says whether it can do partial pricing
bool ClpNetworkMatrix::canDoPartialPricing() const
{
return true;
}
// Partial pricing
void ClpNetworkMatrix::partialPricing(ClpSimplex *model, double startFraction, double endFraction,
int &bestSequence, int &numberWanted)
{
numberWanted = currentWanted_;
int j;
int start = static_cast< int >(startFraction * numberColumns_);
int end = CoinMin(static_cast< int >(endFraction * numberColumns_ + 1), numberColumns_);
double tolerance = model->currentDualTolerance();
double *reducedCost = model->djRegion();
const double *duals = model->dualRowSolution();
const double *cost = model->costRegion();
double bestDj;
if (bestSequence >= 0)
bestDj = fabs(reducedCost[bestSequence]);
else
bestDj = tolerance;
int sequenceOut = model->sequenceOut();
int saveSequence = bestSequence;
if (!trueNetwork_) {
// Not true network
int iSequence;
for (iSequence = start; iSequence < end; iSequence++) {
if (iSequence != sequenceOut) {
double value;
int iRowM, iRowP;
ClpSimplex::Status status = model->getStatus(iSequence);
switch (status) {
case ClpSimplex::basic:
case ClpSimplex::isFixed:
break;
case ClpSimplex::isFree:
case ClpSimplex::superBasic:
value = cost[iSequence];
j = iSequence << 1;
// skip negative rows
iRowM = indices_[j];
iRowP = indices_[j + 1];
if (iRowM >= 0)
value += duals[iRowM];
if (iRowP >= 0)
value -= duals[iRowP];
value = fabs(value);
if (value > FREE_ACCEPT * tolerance) {
numberWanted--;
// we are going to bias towards free (but only if reasonable)
value *= FREE_BIAS;
if (value > bestDj) {
// check flagged variable and correct dj
if (!model->flagged(iSequence)) {
bestDj = value;
bestSequence = iSequence;
} else {
// just to make sure we don't exit before got something
numberWanted++;
}
}
}
break;
case ClpSimplex::atUpperBound:
value = cost[iSequence];
j = iSequence << 1;
// skip negative rows
iRowM = indices_[j];
iRowP = indices_[j + 1];
if (iRowM >= 0)
value += duals[iRowM];
if (iRowP >= 0)
value -= duals[iRowP];
if (value > tolerance) {
numberWanted--;
if (value > bestDj) {
// check flagged variable and correct dj
if (!model->flagged(iSequence)) {
bestDj = value;
bestSequence = iSequence;
} else {
// just to make sure we don't exit before got something
numberWanted++;
}
}
}
break;
case ClpSimplex::atLowerBound:
value = cost[iSequence];
j = iSequence << 1;
// skip negative rows
iRowM = indices_[j];
iRowP = indices_[j + 1];
if (iRowM >= 0)
value += duals[iRowM];
if (iRowP >= 0)
value -= duals[iRowP];
value = -value;
if (value > tolerance) {
numberWanted--;
if (value > bestDj) {
// check flagged variable and correct dj
if (!model->flagged(iSequence)) {
bestDj = value;
bestSequence = iSequence;
} else {
// just to make sure we don't exit before got something
numberWanted++;
}
}
}
break;
}
}
if (!numberWanted)
break;
}
if (bestSequence != saveSequence) {
// recompute dj
double value = cost[bestSequence];
j = bestSequence << 1;
// skip negative rows
int iRowM = indices_[j];
int iRowP = indices_[j + 1];
if (iRowM >= 0)
value += duals[iRowM];
if (iRowP >= 0)
value -= duals[iRowP];
reducedCost[bestSequence] = value;
savedBestSequence_ = bestSequence;
savedBestDj_ = reducedCost[savedBestSequence_];
}
} else {
// true network
int iSequence;