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Matrices.cpp
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Matrices.cpp
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/*
* This file is part of qpOASES.
*
* qpOASES -- An Implementation of the Online Active Set Strategy.
* Copyright (C) 2007-2017 by Hans Joachim Ferreau, Andreas Potschka,
* Christian Kirches et al. All rights reserved.
*
* qpOASES is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* qpOASES is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
* See the GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with qpOASES; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*
*/
/**
* \file src/Matrices.cpp
* \author Hans Joachim Ferreau, Andreas Potschka, Christian Kirches
* \version 3.2
* \date 2007-2017
*
* Implementation of the matrix classes.
*/
#include <qpOASES/Matrices.hpp>
#include <qpOASES/LapackBlasReplacement.hpp>
BEGIN_NAMESPACE_QPOASES
/*****************************************************************************
* P U B L I C *
*****************************************************************************/
returnValue Matrix::getSparseSubmatrix(
const Indexlist* const irows,
const Indexlist* const icols,
int_t rowoffset,
int_t coloffset,
int_t& numNonzeros,
int_t* irn,
int_t* jcn,
real_t* avals,
BooleanType only_lower_triangular) const
{
int_t* rowsNumbers;
irows->getNumberArray( &rowsNumbers );
int_t* colsNumbers;
icols->getNumberArray( &colsNumbers );
return getSparseSubmatrix(irows->getLength(), rowsNumbers, icols->getLength(), colsNumbers, rowoffset, coloffset, numNonzeros, irn, jcn, avals, only_lower_triangular);
}
returnValue Matrix::getSparseSubmatrix(
const Indexlist* const irows,
int_t idx_icol,
int_t rowoffset,
int_t coloffset,
int_t& numNonzeros,
int_t* irn,
int_t* jcn,
real_t* avals,
BooleanType only_lower_triangular) const
{
int_t* rowsNumbers;
irows->getNumberArray( &rowsNumbers );
return getSparseSubmatrix(irows->getLength(), rowsNumbers, 1, &idx_icol, rowoffset, coloffset, numNonzeros, irn, jcn, avals, only_lower_triangular);
}
returnValue Matrix::getSparseSubmatrix(
int_t idx_row,
const Indexlist* const icols,
int_t rowoffset,
int_t coloffset,
int_t& numNonzeros,
int_t* irn,
int_t* jcn,
real_t* avals,
BooleanType only_lower_triangular) const
{
int_t* colsNumbers;
icols->getNumberArray( &colsNumbers );
return getSparseSubmatrix(1, &idx_row, icols->getLength(), colsNumbers, rowoffset, coloffset, numNonzeros, irn, jcn, avals, only_lower_triangular);
}
DenseMatrix::~DenseMatrix()
{
if ( needToFreeMemory( ) == BT_TRUE )
free( );
}
void DenseMatrix::free( )
{
if (val != 0)
delete[] val;
val = 0;
}
Matrix *DenseMatrix::duplicate( ) const
{
DenseMatrix *dupl = 0;
if ( needToFreeMemory( ) == BT_TRUE )
{
real_t* val_new = new real_t[nRows*nCols];
memcpy( val_new,val, ((uint_t)(nRows*nCols))*sizeof(real_t) );
dupl = new DenseMatrix(nRows, nCols, nCols, val_new);
dupl->doFreeMemory( );
}
else
{
dupl = new DenseMatrix(nRows, nCols, nCols, val);
}
return dupl;
}
real_t DenseMatrix::diag( int_t i
) const
{
return val[i*(leaDim+1)];
}
BooleanType DenseMatrix::isDiag( ) const
{
int_t i, j;
if (nRows != nCols)
return BT_FALSE;
for ( i=0; i<nRows; ++i )
for ( j=0; j<i; ++j )
if ( ( getAbs( val[i*leaDim+j] ) > EPS ) || ( getAbs( val[j*leaDim+i] ) > EPS ) )
return BT_FALSE;
return BT_TRUE;
}
real_t DenseMatrix::getNorm( int_t type
) const
{
return REFER_NAMESPACE_QPOASES getNorm( val,nCols*nRows,type );
}
real_t DenseMatrix::getRowNorm( int_t rNum, int_t type ) const
{
return REFER_NAMESPACE_QPOASES getNorm( &(val[rNum*leaDim]),nCols,type );
}
returnValue DenseMatrix::getRowNorm( real_t* norm, int_t type ) const
{
int_t i;
for (i = 0; i < nRows; ++i)
{
norm[i] = REFER_NAMESPACE_QPOASES getNorm( &(val[i*leaDim]),nCols,type );
}
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::getRow(int_t rNum, const Indexlist* const icols, real_t alpha, real_t* row) const
{
int_t i;
if (icols != 0)
{
if ( isEqual(alpha,1.0) == BT_TRUE )
for (i = 0; i < icols->length; i++)
row[i] = val[rNum*leaDim+icols->number[i]];
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (i = 0; i < icols->length; i++)
row[i] = -val[rNum*leaDim+icols->number[i]];
else
for (i = 0; i < icols->length; i++)
row[i] = alpha*val[rNum*leaDim+icols->number[i]];
}
else
{
if ( isEqual(alpha,1.0) == BT_TRUE )
for (i = 0; i < nCols; i++)
row[i] = val[rNum*leaDim+i];
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (i = 0; i < nCols; i++)
row[i] = -val[rNum*leaDim+i];
else
for (i = 0; i < nCols; i++)
row[i] = alpha*val[rNum*leaDim+i];
}
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::getCol(int_t cNum, const Indexlist* const irows, real_t alpha, real_t* col) const
{
int_t i;
if ( isEqual(alpha,1.0) == BT_TRUE )
for (i = 0; i < irows->length; i++)
col[i] = val[irows->number[i]*leaDim+cNum];
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (i = 0; i < irows->length; i++)
col[i] = -val[irows->number[i]*leaDim+cNum];
else
for (i = 0; i < irows->length; i++)
col[i] = alpha*val[irows->number[i]*leaDim+cNum];
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::getSparseSubmatrix (int_t irowsLength, const int_t* const irowsNumber,
int_t icolsLength, const int_t* const icolsNumber,
int_t rowoffset, int_t coloffset, int_t& numNonzeros, int_t* irn,
int_t* jcn, real_t* avals,
BooleanType only_lower_triangular /*= BT_FALSE */) const
{
int_t i, j, irA;
real_t v;
numNonzeros = 0;
if ( only_lower_triangular == BT_FALSE )
{
if (irn == 0)
{
if (jcn != 0 || avals != 0)
return THROWERROR( RET_INVALID_ARGUMENTS );
for (j = 0; j<irowsLength; j++)
{
irA = irowsNumber[j] * leaDim;
for (i = 0; i<icolsLength; i++)
if (isZero( val[irA+icolsNumber[i]] ) == BT_FALSE)
numNonzeros++;
}
}
else
{
for (j = 0; j<irowsLength; j++)
{
irA = irowsNumber[j] * leaDim;
for (i = 0; i<icolsLength; i++)
{
v = val[irA+icolsNumber[i]];
if (isZero( v ) == BT_FALSE)
{
irn[numNonzeros] = j+rowoffset;
jcn[numNonzeros] = i+coloffset;
avals[numNonzeros] = v;
numNonzeros++;
}
}
}
}
}
else
{
if (irn == 0)
{
if (jcn != 0 || avals != 0)
return THROWERROR( RET_INVALID_ARGUMENTS );
for (j = 0; j<irowsLength; j++)
{
irA = irowsNumber[j] * leaDim;
for (i = 0; i<=j; i++)
if (isZero( val[irA+irowsNumber[i]] ) == BT_FALSE)
numNonzeros++;
}
}
else
{
for (j = 0; j<irowsLength; j++)
{
irA = irowsNumber[j] * leaDim;
for (i = 0; i<=j; i++)
{
v = val[irA+irowsNumber[i]];
if (isZero( v ) == BT_FALSE)
{
irn[numNonzeros] = j+rowoffset;
jcn[numNonzeros] = i+coloffset;
avals[numNonzeros] = v;
numNonzeros++;
}
}
}
}
}
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::times( int_t xN, real_t alpha, const real_t* x, int_t xLD, real_t beta, real_t* y, int_t yLD ) const
{
la_uint_t _xN = (la_uint_t)xN;
la_uint_t _nRows = (la_uint_t)nRows;
la_uint_t _nCols = (la_uint_t)nCols;
la_uint_t _leaDim = (la_uint_t)getMax(1,nCols);
la_uint_t _xLD = (la_uint_t)getMax(1,xLD);
la_uint_t _yLD = (la_uint_t)getMax(1,yLD);
/* Call BLAS. Mind row major format! */
GEMM( "TRANS", "NOTRANS", &_nRows, &_xN, &_nCols, &alpha, val, &_leaDim, x, &_xLD, &beta, y, &_yLD );
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::transTimes( int_t xN, real_t alpha, const real_t* x, int_t xLD, real_t beta, real_t* y, int_t yLD ) const
{
la_uint_t _xN = (la_uint_t)xN;
la_uint_t _nRows = (la_uint_t)nRows;
la_uint_t _nCols = (la_uint_t)nCols;
la_uint_t _leaDim = (la_uint_t)getMax(1,nCols);
la_uint_t _xLD = (la_uint_t)getMax(1,xLD);
la_uint_t _yLD = (la_uint_t)getMax(1,yLD);
/* Call BLAS. Mind row major format! */
GEMM( "NOTRANS", "NOTRANS", &_nCols, &_xN, &_nRows, &alpha, val, &_leaDim, x, &_xLD, &beta, y, &_yLD );
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::times( const Indexlist* const irows, const Indexlist* const icols,
int_t xN, real_t alpha, const real_t* x, int_t xLD, real_t beta, real_t* y, int_t yLD,
BooleanType yCompr ) const
{
int_t i, j, k, row, col, iy, irA;
if (yCompr == BT_TRUE)
{
if ( isZero(beta) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
y[j+k*yLD] = 0.0;
else if ( isEqual(beta,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
y[j+k*yLD] = -y[j+k*yLD];
else if ( isEqual(beta,1.0) == BT_FALSE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
y[j+k*yLD] *= beta;
if (icols == 0)
if ( isEqual(alpha,1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
iy = row + k * yLD;
irA = irows->number[row] * leaDim;
for (i = 0; i < nCols; i++)
y[iy] += val[irA+i] * x[k*xLD+i];
}
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
iy = row + k * yLD;
irA = irows->number[row] * leaDim;
for (i = 0; i < nCols; i++)
y[iy] -= val[irA+i] * x[k*xLD+i];
}
else
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
iy = row + k * yLD;
irA = irows->number[row] * leaDim;
for (i = 0; i < nCols; i++)
y[iy] += alpha * val[irA+i] * x[k*xLD+i];
}
else /* icols != 0 */
if ( isEqual(alpha,1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
iy = row + k * yLD;
irA = irows->number[row] * leaDim;
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[iy] += val[irA+icols->number[col]] * x[k*xLD+col];
}
}
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
iy = row + k * yLD;
irA = irows->number[row] * leaDim;
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[iy] -= val[irA+icols->number[col]] * x[k*xLD+col];
}
}
else
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
iy = row + k * yLD;
irA = irows->number[row] * leaDim;
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[iy] += alpha * val[irA+icols->number[col]] * x[k*xLD+col];
}
}
}
else /* y not compressed */
{
if ( isZero(beta) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
y[irows->number[j]+k*yLD] = 0.0;
else if ( isEqual(beta,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
y[irows->number[j]+k*yLD] = -y[j+k*yLD];
else if ( isEqual(beta,1.0) == BT_FALSE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
y[irows->number[j]+k*yLD] *= beta;
if (icols == 0)
if ( isEqual(alpha,1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->number[irows->iSort[j]];
iy = row + k * yLD;
irA = row * leaDim;
for (i = 0; i < nCols; i++)
y[iy] += val[irA+i] * x[k*xLD+i];
}
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->number[irows->iSort[j]];
iy = row + k * yLD;
irA = row * leaDim;
for (i = 0; i < nCols; i++)
y[iy] -= val[irA+i] * x[k*xLD+i];
}
else
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->number[irows->iSort[j]];
iy = row + k * yLD;
irA = row * leaDim;
for (i = 0; i < nCols; i++)
y[iy] += alpha * val[irA+i] * x[k*xLD+i];
}
else /* icols != 0 */
if ( isEqual(alpha,1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->number[irows->iSort[j]];
iy = row + k * yLD;
irA = row * leaDim;
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[iy] += val[irA+icols->number[col]] * x[k*xLD+col];
}
}
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->number[irows->iSort[j]];
iy = row + k * yLD;
irA = row * leaDim;
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[iy] -= val[irA+icols->number[col]] * x[k*xLD+col];
}
}
else
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->number[irows->iSort[j]];
iy = row + k * yLD;
irA = row * leaDim;
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[iy] += alpha * val[irA+icols->number[col]] * x[k*xLD+col];
}
}
}
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::transTimes( const Indexlist* const irows, const Indexlist* const icols,
int_t xN, real_t alpha, const real_t* x, int_t xLD, real_t beta, real_t* y, int_t yLD ) const
{
int_t i, j, k, row, col;
if ( isZero(beta) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < icols->length; j++)
y[j+k*yLD] = 0.0;
else if ( isEqual(beta,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < icols->length; j++)
y[j+k*yLD] = -y[j+k*yLD];
else if ( isEqual(beta,1.0) == BT_FALSE )
for (k = 0; k < xN; k++)
for (j = 0; j < icols->length; j++)
y[j+k*yLD] *= beta;
if ( isEqual(alpha,1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[col+k*yLD] += val[irows->number[row]*leaDim+icols->number[col]] * x[row+k*xLD];
}
}
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[col+k*yLD] -= val[irows->number[row]*leaDim+icols->number[col]] * x[row+k*xLD];
}
}
else
for (k = 0; k < xN; k++)
for (j = 0; j < irows->length; j++)
{
row = irows->iSort[j];
for (i = 0; i < icols->length; i++)
{
col = icols->iSort[i];
y[col+k*yLD] += alpha * val[irows->number[row]*leaDim+icols->number[col]] * x[row+k*xLD];
}
}
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::addToDiag( real_t alpha )
{
int_t i;
for (i = 0; i < nRows && i < nCols; i++)
val[i*(leaDim+1)] += alpha;
return SUCCESSFUL_RETURN;
}
returnValue DenseMatrix::writeToFile( FILE* output_file, const char* prefix ) const
{
return THROWERROR( RET_NOT_YET_IMPLEMENTED );
}
real_t* DenseMatrix::full() const
{
real_t* v = new real_t[nRows*nCols];
memcpy( v,val, ((uint_t)(nRows*nCols))*sizeof(real_t) );
return v;
}
returnValue DenseMatrix::print( const char* name ) const
{
return REFER_NAMESPACE_QPOASES print( val,nRows,nCols,name );
}
Matrix *SymDenseMat::duplicate( ) const
{
return duplicateSym();
}
SymmetricMatrix *SymDenseMat::duplicateSym( ) const
{
/* "same" as duplicate() in DenseMatrix */
SymDenseMat *dupl = 0;
if ( needToFreeMemory( ) == BT_TRUE )
{
real_t* val_new = new real_t[nRows*nCols];
memcpy( val_new,val, ((uint_t)(nRows*nCols))*sizeof(real_t) );
dupl = new SymDenseMat(nRows, nCols, nCols, val_new);
dupl->doFreeMemory( );
}
else
{
dupl = new SymDenseMat(nRows, nCols, nCols, val);
}
return dupl;
}
returnValue SymDenseMat::bilinear( const Indexlist* const icols,
int_t xN, const real_t* x, int_t xLD, real_t* y, int_t yLD ) const
{
int_t ii, jj, kk, col;
int_t i,j,k,irA;
for (ii = 0; ii < xN; ii++)
for (jj = 0; jj < xN; jj++)
y[ii*yLD+jj] = 0.0;
real_t* Ax = new real_t[icols->length * xN];
for (i=0;i<icols->length * xN;++i)
Ax[i]=0.0;
/* exploit symmetry of A ! */
for (j = 0; j < icols->length; j++) {
irA = icols->number[j] * leaDim;
for (i = 0; i < icols->length; i++)
{
real_t h = val[irA+icols->number[i]];
for (k = 0; k < xN; k++)
Ax[j + k * icols->length] += h * x[k*xLD+icols->number[i]];
}
}
for (ii = 0; ii < icols->length; ++ii) {
col = icols->number[ii];
for (jj = 0; jj < xN; ++jj) {
for (kk = 0; kk < xN; ++kk) {
y[kk + jj*yLD] += x[col + jj*xLD] * Ax[ii + kk*icols->length];
}
}
}
delete[] Ax;
return SUCCESSFUL_RETURN;
}
SparseMatrix::SparseMatrix() : nRows(0), nCols(0), ir(0), jc(0), jd(0), val(0) {}
SparseMatrix::SparseMatrix( int_t nr, int_t nc, sparse_int_t* r, sparse_int_t* c, real_t* v )
: nRows(nr), nCols(nc), ir(r), jc(c), jd(0), val(v) { doNotFreeMemory(); }
SparseMatrix::SparseMatrix( int_t nr, int_t nc, int_t ld, const real_t* const v )
: nRows(nr), nCols(nc), jd(0)
{
int_t i, j, nnz;
jc = new sparse_int_t[nc+1];
ir = new sparse_int_t[nr*nc];
val = new real_t[nr*nc];
nnz = 0;
for (j = 0; j < nCols; j++)
{
jc[j] = nnz;
for (i = 0; i < nRows; i++)
if ( ( isZero( v[i*ld+j],0.0 ) == BT_FALSE ) || ( i == j ) ) /* also include zero diagonal elemets! */
{
ir[nnz] = i;
val[nnz++] = v[i*ld+j];
}
}
jc[nCols] = nnz;
doFreeMemory( );
}
SparseMatrix::~SparseMatrix()
{
if (jd != 0)
{
delete[] jd;
jd = 0;
}
if ( needToFreeMemory() == BT_TRUE )
free( );
}
void SparseMatrix::free( )
{
if (ir != 0) delete[] ir;
ir = 0;
if (jc != 0) delete[] jc;
jc = 0;
if (val != 0) delete[] val;
val = 0;
doNotFreeMemory( );
}
Matrix *SparseMatrix::duplicate( ) const
{
long i, length = jc[nCols];
SparseMatrix *dupl = new SparseMatrix;
dupl->nRows = nRows;
dupl->nCols = nCols;
dupl->ir = new sparse_int_t[length];
dupl->jc = new sparse_int_t[nCols+1];
dupl->val = new real_t[length];
for (i = 0; i < length; i++) dupl->ir[i] = ir[i];
for (i = 0; i <= nCols; i++) dupl->jc[i] = jc[i];
for (i = 0; i < length; i++) dupl->val[i] = val[i];
if ( jd != 0 )
{
dupl->jd = new sparse_int_t[nCols];
for (i = 0; i < nCols; i++) dupl->jd[i] = jd[i];
}
else
dupl->jd = 0;
dupl->doFreeMemory( );
return dupl;
}
void SparseMatrix::setVal( const real_t* newVal )
{
long length = jc[nCols]; /* [nCols+1] ?? */
for (long index = 0; index < length; ++index)
{
val[index] = newVal[index];
}
}
real_t SparseMatrix::diag( int_t i ) const
{
if ( jd == 0 )
{
THROWERROR( RET_DIAGONAL_NOT_INITIALISED );
return INFTY;
}
int_t entry = jd[i];
return (entry < jc[i+1] && ir[entry] == i) ? val[entry] : 0.0;
}
BooleanType SparseMatrix::isDiag( ) const
{
int_t j;
if ( nCols != nRows )
return BT_FALSE;
for (j = 0; j < nCols; ++j)
{
if ( jc[j+1] > jc[j]+1 )
return BT_FALSE;
if ( ( jc[j+1] == jc[j]+1 ) && ( ir[jc[j]] != j ) )
return BT_FALSE;
}
return BT_TRUE;
}
real_t SparseMatrix::getNorm( int_t type
) const
{
int_t length = jc[nCols];
return REFER_NAMESPACE_QPOASES getNorm( val,length,type );
}
real_t SparseMatrix::getRowNorm( int_t rNum, int_t type ) const
{
int_t i,j;
real_t norm = 0.0;
switch( type )
{
case 2:
for ( j=0; j < nCols; ++j ) {
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++) {};
if (i < jc[j+1] && ir[i] == rNum) norm += val[i]*val[i];
}
return getSqrt(norm);
case 1:
for ( j=0; j < nCols; ++j ) {
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++) {};
if (i < jc[j+1] && ir[i] == rNum) norm += getAbs( val[i] );
}
return norm;
default:
THROWERROR( RET_INVALID_ARGUMENTS );
return -INFTY;
}
}
returnValue SparseMatrix::getRowNorm( real_t* norm, int_t type ) const
{
int_t i,j;
for ( j=0; j < nRows; ++j ) norm[j] = 0.0;
switch( type )
{
case 2:
for ( j=0; j < nCols; ++j ) {
for (i = jc[j]; i < jc[j+1]; i++)
norm[ir[i]] += val[i]*val[i];
}
for ( j=0; j < nRows; ++j ) norm[j] = getSqrt(norm[j]);
break;
case 1:
for ( j=0; j < nCols; ++j ) {
for (i = jc[j]; i < jc[j+1]; i++);
norm[ir[i]] += getAbs( val[i] );
}
break;
default:
return RET_INVALID_ARGUMENTS;
}
return SUCCESSFUL_RETURN;
}
returnValue SparseMatrix::getRow( int_t rNum, const Indexlist* const icols, real_t alpha, real_t* row ) const
{
long i, j, k;
if (icols != 0)
{
if ( isEqual(alpha,1.0) == BT_TRUE )
for (k = 0; k < icols->length; k++)
{
j = icols->number[icols->iSort[k]];
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++);
row[icols->iSort[k]] = (i < jc[j+1] && ir[i] == rNum) ? val[i] : 0.0;
}
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (k = 0; k < icols->length; k++)
{
j = icols->number[icols->iSort[k]];
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++);
row[icols->iSort[k]] = (i < jc[j+1] && ir[i] == rNum) ? -val[i] : 0.0;
}
else
for (k = 0; k < icols->length; k++)
{
j = icols->number[icols->iSort[k]];
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++);
row[icols->iSort[k]] = (i < jc[j+1] && ir[i] == rNum) ? alpha*val[i] : 0.0;
}
}
else
{
if ( isEqual(alpha,1.0) == BT_TRUE )
for (j = 0; j < nCols; j++)
{
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++);
row[j] = (i < jc[j+1] && ir[i] == rNum) ? val[i] : 0.0;
}
else if ( isEqual(alpha,-1.0) == BT_TRUE )
for (j = 0; j < icols->length; j++)
{
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++);
row[j] = (i < jc[j+1] && ir[i] == rNum) ? -val[i] : 0.0;
}
else
for (j = 0; j < icols->length; j++)
{
for (i = jc[j]; i < jc[j+1] && ir[i] < rNum; i++);
row[j] = (i < jc[j+1] && ir[i] == rNum) ? alpha*val[i] : 0.0;
}
}
return SUCCESSFUL_RETURN;
}
returnValue SparseMatrix::getCol( int_t cNum, const Indexlist* const irows, real_t alpha, real_t* col ) const
{
long i, j;
i = jc[cNum];
j = 0;
if ( isEqual(alpha,1.0) == BT_TRUE )
while (i < jc[cNum+1] && j < irows->length)
if (ir[i] == irows->number[irows->iSort[j]])
col[irows->iSort[j++]] = val[i++];
else if (ir[i] > irows->number[irows->iSort[j]])
col[irows->iSort[j++]] = 0.0;
else
i++;
else if ( isEqual(alpha,-1.0) == BT_TRUE )
while (i < jc[cNum+1] && j < irows->length)
if (ir[i] == irows->number[irows->iSort[j]])
col[irows->iSort[j++]] = -val[i++];
else if (ir[i] > irows->number[irows->iSort[j]])
col[irows->iSort[j++]] = 0.0;
else
i++;
else
while (i < jc[cNum+1] && j < irows->length)
if (ir[i] == irows->number[irows->iSort[j]])
col[irows->iSort[j++]] = alpha * val[i++];
else if (ir[i] > irows->number[irows->iSort[j]])
col[irows->iSort[j++]] = 0.0;
else
i++;
/* fill in remaining zeros */
while (j < irows->length)
col[irows->iSort[j++]] = 0.0;
return SUCCESSFUL_RETURN;
}
returnValue SparseMatrix::getSparseSubmatrix( int_t irowsLength, const int_t* const irowsNumber,
int_t icolsLength, const int_t* const icolsNumber,
int_t rowoffset, int_t coloffset, int_t& numNonzeros, int_t* irn,
int_t* jcn, real_t* avals,
BooleanType only_lower_triangular /*= BT_FALSE */ ) const
{
int_t i, j, k, l;
// Compute the "inverse" of the irows->number array
// TODO: Ideally this should be a part of Indexlist
int_t* rowNumberInv = new int_t[nRows];
for (i=0; i<nRows; i++)
rowNumberInv[i] = -1;
for (i=0; i<irowsLength; i++)
rowNumberInv[irowsNumber[i]] = i;
numNonzeros = 0;
if ( only_lower_triangular == BT_FALSE )
{
if (irn == 0)
{
if (jcn != 0 || avals != 0)
return THROWERROR( RET_INVALID_ARGUMENTS );
for (k = 0; k < icolsLength; k++)
{
j = icolsNumber[k];
for (i = jc[j]; i < jc[j+1]; i++)
{
l = rowNumberInv[ir[i]];
if (l >= 0)
numNonzeros++;
}
}
}
else
{
for (k = 0; k < icolsLength; k++)
{
j = icolsNumber[k];
for (i = jc[j]; i < jc[j+1]; i++)
{
l = rowNumberInv[ir[i]];
if (l >= 0)
{
irn[numNonzeros] = l+rowoffset;
jcn[numNonzeros] = k+coloffset;