/usr/include/sofa/component/linearsolver/LULinearSolver.h is in libsofa1-dev 1.0~beta4-9.
This file is owned by root:root, with mode 0o644.
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* SOFA, Simulation Open-Framework Architecture, version 1.0 beta 4 *
* (c) 2006-2009 MGH, INRIA, USTL, UJF, CNRS *
* *
* This library 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. *
* *
* This library 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 this library; if not, write to the Free Software Foundation, *
* Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. *
*******************************************************************************
* SOFA :: Modules *
* *
* Authors: The SOFA Team and external contributors (see Authors.txt) *
* *
* Contact information: contact@sofa-framework.org *
******************************************************************************/
#ifndef SOFA_COMPONENT_LINEARSOLVER_LULINEARSOLVER_H
#define SOFA_COMPONENT_LINEARSOLVER_LULINEARSOLVER_H
#include <sofa/core/componentmodel/behavior/LinearSolver.h>
#include <sofa/component/linearsolver/MatrixLinearSolver.h>
#include <sofa/component/linearsolver/SparseMatrix.h>
#include <sofa/component/linearsolver/FullMatrix.h>
#include <math.h>
namespace sofa
{
namespace component
{
namespace linearsolver
{
/// Linear system solver using the default (LU factorization) algorithm
template<class Matrix, class Vector>
class LULinearSolver : public sofa::component::linearsolver::MatrixLinearSolver<Matrix,Vector>, public virtual sofa::core::objectmodel::BaseObject
{
public:
Data<bool> f_verbose;
typename Matrix::LUSolver* solver;
typename Matrix::InvMatrixType Minv;
bool computedMinv;
LULinearSolver()
: f_verbose( initData(&f_verbose,false,"verbose","Dump system state at each iteration") )
, solver(NULL), computedMinv(false)
{
}
~LULinearSolver()
{
if (solver != NULL)
delete solver;
}
/// Invert M
void invert (Matrix& M)
{
if (solver != NULL)
delete solver;
solver = M.makeLUSolver();
computedMinv = false;
}
/// Solve Mx=b
void solve (Matrix& M, Vector& x, Vector& b)
{
const bool verbose = f_verbose.getValue();
if( verbose )
{
serr<<"LULinearSolver, b = "<< b <<sendl;
serr<<"LULinearSolver, M = "<< M <<sendl;
}
if (solver)
M.solve(&x,&b, solver);
else
M.solve(&x,&b);
// x is the solution of the system
if( verbose )
{
serr<<"LULinearSolver::solve, solution = "<<x<<sendl;
}
}
void computeMinv()
{
if (!computedMinv)
{
if (solver)
Minv = solver->i();
else
Minv = this->systemMatrix->i();
computedMinv = true;
}
/*typename Matrix::InvMatrixType I;
I = ((*this->systemMatrix)*Minv);
for (int i=0;i<I.rowSize();++i)
for (int j=0;j<I.rowSize();++j)
{
double err = I.element(i,j)-((i==j)?1.0:0.0);
if (fabs(err) > 1.0e-6)
serr << "ERROR: I("<<i<<","<<j<<") error "<<err<<sendl;
}*/
}
double getMinvElement(int i, int j)
{
return Minv.element(i,j);
}
template<class RMatrix, class JMatrix>
bool addJMInvJt(RMatrix& result, JMatrix& J, double fact)
{
const unsigned int Jrows = J.rowSize();
const unsigned int Jcols = J.colSize();
if (Jcols != this->systemMatrix->rowSize())
{
serr << "LULinearSolver::addJMInvJt ERROR: incompatible J matrix size." << sendl;
return false;
}
if (!Jrows) return false;
computeMinv();
const typename JMatrix::LineConstIterator jitend = J.end();
for (typename JMatrix::LineConstIterator jit1 = J.begin(); jit1 != jitend; ++jit1)
{
int row1 = jit1->first;
for (typename JMatrix::LineConstIterator jit2 = jit1; jit2 != jitend; ++jit2)
{
int row2 = jit2->first;
double acc = 0.0;
for (typename JMatrix::LElementConstIterator i1 = jit1->second.begin(), i1end = jit1->second.end(); i1 != i1end; ++i1)
{
int col1 = i1->first;
double val1 = i1->second;
for (typename JMatrix::LElementConstIterator i2 = jit2->second.begin(), i2end = jit2->second.end(); i2 != i2end; ++i2)
{
int col2 = i2->first;
double val2 = i2->second;
acc += val1 * getMinvElement(col1,col2) * val2;
}
}
acc *= fact;
//sout << "W("<<row1<<","<<row2<<") += "<<acc<<" * "<<fact<<sendl;
result.add(row1,row2,acc);
if (row1!=row2)
result.add(row2,row1,acc);
}
}
return true;
}
/// Multiply the inverse of the system matrix by the transpose of the given matrix, and multiply the result with the given matrix J
///
/// @param result the variable where the result will be added
/// @param J the matrix J to use
/// @return false if the solver does not support this operation, of it the system matrix is not invertible
bool addJMInvJt(defaulttype::BaseMatrix* result, defaulttype::BaseMatrix* J, double fact)
{
if (FullMatrix<double>* r = dynamic_cast<FullMatrix<double>*>(result))
{
if (SparseMatrix<double>* j = dynamic_cast<SparseMatrix<double>*>(J))
{
return addJMInvJt(*r,*j,fact);
}
else if (SparseMatrix<float>* j = dynamic_cast<SparseMatrix<float>*>(J))
{
return addJMInvJt(*r,*j,fact);
}
}
else if (FullMatrix<double>* r = dynamic_cast<FullMatrix<double>*>(result))
{
if (SparseMatrix<double>* j = dynamic_cast<SparseMatrix<double>*>(J))
{
return addJMInvJt(*r,*j,fact);
}
else if (SparseMatrix<float>* j = dynamic_cast<SparseMatrix<float>*>(J))
{
return addJMInvJt(*r,*j,fact);
}
}
else if (defaulttype::BaseMatrix* r = result)
{
if (SparseMatrix<double>* j = dynamic_cast<SparseMatrix<double>*>(J))
{
return addJMInvJt(*r,*j,fact);
}
else if (SparseMatrix<float>* j = dynamic_cast<SparseMatrix<float>*>(J))
{
return addJMInvJt(*r,*j,fact);
}
}
return false;
}
};
} // namespace linearsolver
} // namespace component
} // namespace sofa
#endif
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