//=====================
// matrices comparison
//=====================
bool isComparableTo(const SiconosMatrix& m1, const SiconosMatrix& m2)
{
// return:
// - true if one of the matrices is a Simple and if they have the same dimensions.
// - true if both are block but with blocks which are facing each other of the same size.
// - false in other cases
if ((!m1.isBlock() || !m2.isBlock()) && (m1.size(0) == m2.size(0)) && (m1.size(1) == m2.size(1)))
return true;
const SP::Index I1R = m1.tabRow();
const SP::Index I2R = m2.tabRow();
const SP::Index I1C = m1.tabCol();
const SP::Index I2C = m2.tabCol();
return ((*I1R == *I2R) && (*I1C == *I2C));
}
void SimpleMatrix::trans(const SiconosMatrix &m)
{
if (m.isBlock())
SiconosMatrixException::selfThrow("SimpleMatrix::trans(m) failed, not yet implemented for m being a BlockMatrix.");
if (&m == this)
trans();//SiconosMatrixException::selfThrow("SimpleMatrix::trans(m) failed, m = this, use this->trans().");
else
{
unsigned int numM = m.num();
switch (numM)
{
case 1:
if (_num != 1)
SiconosMatrixException::selfThrow("SimpleMatrix::trans(m) failed, try to transpose a dense matrix into another type.");
noalias(*mat.Dense) = ublas::trans(*m.dense());
break;
case 2:
if (_num != 1)
SiconosMatrixException::selfThrow("SimpleMatrix::trans(m) failed, try to transpose a triangular matrix into a non-dense one.");
noalias(*mat.Dense) = ublas::trans(*m.triang());
break;
case 3:
*this = m;
break;
case 4:
if (_num == 1)
noalias(*mat.Dense) = ublas::trans(*m.sparse());
else if (_num == 4)
noalias(*mat.Sparse) = ublas::trans(*m.sparse());
else
SiconosMatrixException::selfThrow("SimpleMatrix::trans(m) failed, try to transpose a sparse matrix into a forbidden type (not dense nor sparse).");
break;
case 5:
if (_num == 1)
noalias(*mat.Dense) = ublas::trans(*m.banded());
else if (_num == 5)
noalias(*mat.Banded) = ublas::trans(*m.banded());
else
SiconosMatrixException::selfThrow("SimpleMatrix::trans(m) failed, try to transpose a banded matrix into a forbidden type (not dense nor banded).");
break;
case 6:
*this = m;
break;
case 7:
*this = m;
}
// unsigned int tmp = _dimRow;
// _dimRow = _dimCol;
// _dimCol = tmp;
resetLU();
}
}
void SimpleMatrix::SolveByLeastSquares(SiconosMatrix &B)
{
if (B.isBlock())
SiconosMatrixException::selfThrow("SimpleMatrix::SolveByLeastSquares(Siconos Matrix &B) failed. Not yet implemented for M being a BlockMatrix.");
int info = 0;
#ifdef USE_OPTIMAL_WORKSPACE
info += lapack::gels(*mat.Dense, *(B.dense()), lapack::optimal_workspace());
#endif
#ifdef USE_MINIMAL_WORKSPACE
info += lapack::gels(*mat.Dense, *(B.dense()), lapack::minimal_workspace());
#endif
if (info != 0)
SiconosMatrixException::selfThrow("SimpleMatrix::SolveByLeastSquares failed.");
}
void private_addprod(const SiconosMatrix& A, unsigned int startRow, unsigned int startCol, const BlockVector& x, SiconosVector& y)
{
assert(!(A.isPLUFactorized()) && "A is PLUFactorized in prod !!");
assert(!A.isBlock() && "private_addprod(A,start,x,y) error: not yet implemented for block matrix.");
VectorOfVectors::const_iterator it;
unsigned int startColBis = startCol;
for (it = x.begin(); it != x.end(); ++it)
{
private_addprod(A, startRow, startColBis, **it, y);
startColBis += (*it)->size();
}
}
void private_addprod(const SiconosMatrix& A, unsigned startRow, unsigned int startCol, const SiconosVector& x, SiconosVector& y)
{
assert(!(A.isPLUFactorized()) && "A is PLUFactorized in prod !!");
assert(!A.isBlock() && "private_addprod(A,start,x,y) error: not yet implemented for block matrix.");
// we take a submatrix subA of A, starting from row startRow to row (startRow+sizeY) and between columns startCol and (startCol+sizeX).
// Then computation of y = subA*x + y.
unsigned int numA = A.getNum();
unsigned int numY = y.getNum();
unsigned int numX = x.getNum();
unsigned int sizeX = x.size();
unsigned int sizeY = y.size();
assert(numX == numY && "private_addprod(A,start,x,y) error: not yet implemented for x and y of different types.");
if (numY == 1 && numX == 1)
{
assert(y.dense() != x.dense());
if (numA == 1)
noalias(*y.dense()) += prod(ublas::subrange(*A.dense(), startRow, startRow + sizeY, startCol, startCol + sizeX), *x.dense());
else if (numA == 2)
noalias(*y.dense()) += prod(ublas::subrange(*A.triang(), startRow, startRow + sizeY, startCol, startCol + sizeX), *x.dense());
else if (numA == 3)
noalias(*y.dense()) += prod(ublas::subrange(*A.sym(), startRow, startRow + sizeY, startCol, startCol + sizeX), *x.dense());
else if (numA == 4)
noalias(*y.dense()) += prod(ublas::subrange(*A.sparse(), startRow, startRow + sizeY, startCol, startCol + sizeX), *x.dense());
else //if(numA==5)
noalias(*y.dense()) += prod(ublas::subrange(*A.banded(), startRow, startRow + sizeY, startCol, startCol + sizeX), *x.dense());
}
else // x and y sparse
{
if (numA == 4)
*y.sparse() += prod(ublas::subrange(*A.sparse(), startRow, startRow + sizeY, startCol, startCol + sizeX), *x.sparse());
else
SiconosMatrixException::selfThrow("private_addprod(A,start,x,y) error: not yet implemented for x, y sparse and A not sparse.");
}
}
bool write(const std::string& fileName, const std::string& mode, const SiconosMatrix& m, const std::string& outputType)
{
// Open file and various checks
std::ofstream outfile;
if (mode == "ascii")
outfile.open(fileName.c_str(), std::ofstream::out);
else if (mode == "binary")
outfile.open(fileName.c_str(), std::ofstream::binary);
else
SiconosMatrixException::selfThrow("ioMatrix::write Incorrect mode for writing");
if (!outfile.good())
SiconosMatrixException::selfThrow("ioMatrix:: write error : Fail to open \"" + fileName + "\"");
if (m.isBlock())
SiconosMatrixException::selfThrow("ioMatrix:: write error : not yet implemented for BlockMatrix");
outfile.precision(15);
outfile.setf(std::ios::scientific);
// Writing
if (outputType != "noDim")
outfile << m.size(0) << " " << m.size(1) << std::endl;
if (m.getNum() == 1)
{
// DenseMat * p = m.dense();
DenseMat::iterator1 row;
DenseMat::iterator2 col;
double tmp;
for (unsigned int i = 0; i < m.size(0); i++)
{
for (unsigned int j = 0; j < m.size(1); j++)
{
tmp = m(i, j);
if (fabs(tmp) < std::numeric_limits<double>::min()) tmp = 0.0;
outfile << tmp << " " ;
assert(outfile.good());
}
outfile << std::endl;
}
}
else if (m.getNum() == 2)
{
TriangMat * p = m.triang();
TriangMat::iterator1 row;
for (row = p->begin1(); row != p->end1() ; ++row)
{
std::copy(row.begin(), row.end(), std::ostream_iterator<double>(outfile, " "));
outfile << std::endl;
}
}
else if (m.getNum() == 3)
{
SymMat * p = m.sym();
SymMat::iterator1 row;
for (row = p->begin1(); row != p->end1() ; ++row)
{
std::copy(row.begin(), row.end(), std::ostream_iterator<double>(outfile, " "));
outfile << std::endl;
}
}
else if (m.getNum() == 4)
{
SparseMat * p = m.sparse();
SparseMat::iterator1 row;
for (row = p->begin1(); row != p->end1() ; ++row)
{
std::copy(row.begin(), row.end(), std::ostream_iterator<double>(outfile, " "));
outfile << std::endl;
}
}
else
{
BandedMat * p = m.banded();
BandedMat::iterator1 row;
for (row = p->begin1(); row != p->end1() ; ++row)
{
std::copy(row.begin(), row.end(), std::ostream_iterator<double>(outfile, " "));
outfile << std::endl;
}
}
outfile.close();
return true;
}
void SimpleMatrix::PLUForwardBackwardInPlace(SiconosMatrix &B)
{
if (B.isBlock())
SiconosMatrixException::selfThrow("SimpleMatrix PLUForwardBackwardInPlace(B) failed at solving Ax = B. Not yet implemented for a BlockMatrix B.");
int info = 0;
if (_num == 1)
{
if (!_isPLUFactorized) // call gesv => LU-factorize+solve
{
// solve system:
if (!_ipiv)
_ipiv.reset(new VInt(size(0)));
else
_ipiv->resize(size(0));
info = lapack::gesv(*mat.Dense, *_ipiv, *(B.dense()));
_isPLUFactorized = true;
/*
ublas::vector<double> S(std::max(size(0),size(1)));
ublas::matrix<double, ublas::column_major> U(size(0),size(1));
ublas::matrix<double, ublas::column_major> VT(size(0),size(1));
int ierr = lapack::gesdd(*mat.Dense, S, U, VT);
printf("info = %d, ierr = %d, emax = %f, emin = %f , cond = %f\n",info,ierr,S(0),S(2),S(0)/S(2));
*/
// B now contains solution:
}
else // call getrs: only solve using previous lu-factorization
if (B.num() == 1)
info = lapack::getrs(*mat.Dense, *_ipiv, *(B.dense()));
else
SiconosMatrixException::selfThrow(" SimpleMatrix::PLUInverseInPlace: only implemented for dense matrices in RHS.");
}
else
{
if (!_isPLUFactorized) // call first PLUFactorizationInPlace
{
PLUFactorizationInPlace();
}
// and then solve
if (B.num() == 1)
{
inplace_solve(*sparse(), *(B.dense()), ublas::lower_tag());
inplace_solve(ublas::trans(*sparse()), *(B.dense()), ublas::upper_tag());
}
else if (B.num() == 4)
{
inplace_solve(*sparse(), *(B.sparse()), ublas::lower_tag());
inplace_solve(ublas::trans(*sparse()), *(B.sparse()), ublas::upper_tag());
}
else
SiconosMatrixException::selfThrow(" SimpleMatrix::PLUInverseInPlace: only implemented for dense ans sparse matrices in RHS.");
info = 0 ;
}
// SiconosMatrixException::selfThrow(" SimpleMatrix::PLUInverseInPlace: only implemented for dense matrices.");
if (info != 0)
SiconosMatrixException::selfThrow("SimpleMatrix::PLUForwardBackwardInPlace failed.");
}