/usr/include/sofa/helper/PolarDecompose.h is in libsofa1-dev 1.0~beta4-6.
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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 :: Framework *
* *
* Authors: M. Adam, J. Allard, B. Andre, P-J. Bensoussan, S. Cotin, C. Duriez,*
* H. Delingette, F. Falipou, F. Faure, S. Fonteneau, L. Heigeas, C. Mendoza, *
* M. Nesme, P. Neumann, J-P. de la Plata Alcade, F. Poyer and F. Roy *
* *
* Contact information: contact@sofa-framework.org *
******************************************************************************/
#ifndef SOFA_HELPER_POLARDECOMPOSE_H
#define SOFA_HELPER_POLARDECOMPOSE_H
#include <sofa/defaulttype/Mat.h>
#include <sofa/helper/helper.h>
namespace sofa
{
namespace helper
{
/**** FROM Decompose.c ****/
/* Ken Shoemake, 1993 */
/******* Matrix Preliminaries *******/
/** Set MadjT to transpose of inverse of M times determinant of M **/
template<class Real>
void adjoint_transpose(const defaulttype::Mat<3,3,Real>& M, defaulttype::Mat<3,3,Real>& MadjT)
{
MadjT[0] = cross(M[1],M[2]);
MadjT[1] = cross(M[2],M[0]);
MadjT[2] = cross(M[0],M[1]);
}
/** Compute the infinity norm of M **/
template<class Real>
Real norm_inf(const defaulttype::Mat<3,3,Real>& M)
{
Real sum, max = 0;
for (int i=0;i<3;i++)
{
sum = (Real)(fabs(M[i][0])+fabs(M[i][1])+fabs(M[i][2]));
if (max<sum) max = sum;
}
return max;
}
/** Compute the 1 norm of M **/
template<class Real>
Real norm_one(const defaulttype::Mat<3,3,Real>& M)
{
Real sum, max = 0;
for (int i=0;i<3;i++)
{
sum = (Real)(fabs(M[0][i])+fabs(M[1][i])+fabs(M[2][i]));
if (max<sum) max = sum;
}
return max;
}
/** Return index of column of M containing maximum abs entry, or -1 if M=0 **/
template<class Real>
int find_max_col(const defaulttype::Mat<3,3,Real>& M)
{
Real abs, max = 0;
int col = -1;
for (int i=0; i<3; i++)
for (int j=0; j<3; j++) {
abs = M[i][j]; if (abs<0.0) abs = -abs;
if (abs>max) {max = abs; col = j;}
}
return col;
}
/** Setup u for Household reflection to zero all v components but first **/
template<class Real>
void make_reflector(const defaulttype::Vec<3,Real>& v, defaulttype::Vec<3,Real>& u)
{
Real s = (Real)sqrt(dot(v, v));
u[0] = v[0]; u[1] = v[1];
u[2] = v[2] + ((v[2]<0.0) ? -s : s);
s = (Real)sqrt(2.0/dot(u, u));
u[0] = u[0]*s; u[1] = u[1]*s; u[2] = u[2]*s;
}
/** Apply Householder reflection represented by u to column vectors of M **/
template<class Real>
void reflect_cols(defaulttype::Mat<3,3,Real>& M, const defaulttype::Vec<3,Real>& u)
{
for (int i=0; i<3; i++)
{
Real s = u[0]*M[0][i] + u[1]*M[1][i] + u[2]*M[2][i];
for (int j=0; j<3; j++)
M[j][i] -= u[j]*s;
}
}
/** Apply Householder reflection represented by u to row vectors of M **/
template<class Real>
void reflect_rows(defaulttype::Mat<3,3,Real>& M, const defaulttype::Vec<3,Real>& u)
{
for (int i=0; i<3; i++)
{
Real s = dot(u, M[i]);
for (int j=0; j<3; j++)
M[i][j] -= u[j]*s;
}
}
/** Find orthogonal factor Q of rank 1 (or less) M **/
template<class Real>
void do_rank1(defaulttype::Mat<3,3,Real>& M, defaulttype::Mat<3,3,Real>& Q)
{
defaulttype::Vec<3,Real> v1, v2;
Real s;
int col;
Q.identity();
/* If rank(M) is 1, we should find a non-zero column in M */
col = find_max_col(M);
if (col<0) return; /* Rank is 0 */
v1[0] = M[0][col]; v1[1] = M[1][col]; v1[2] = M[2][col];
make_reflector(v1, v1); reflect_cols(M, v1);
v2[0] = M[2][0]; v2[1] = M[2][1]; v2[2] = M[2][2];
make_reflector(v2, v2); reflect_rows(M, v2);
s = M[2][2];
if (s<0.0) Q[2][2] = -1.0;
reflect_cols(Q, v1); reflect_rows(Q, v2);
}
/** Find orthogonal factor Q of rank 2 (or less) M using adjoint transpose **/
template<class Real>
void do_rank2(defaulttype::Mat<3,3,Real>& M, defaulttype::Mat<3,3,Real>& MadjT, defaulttype::Mat<3,3,Real>& Q)
{
defaulttype::Vec<3,Real> v1, v2;
Real w, x, y, z, c, s, d;
int col;
/* If rank(M) is 2, we should find a non-zero column in MadjT */
col = find_max_col(MadjT);
if (col<0) {do_rank1(M, Q); return;} /* Rank<2 */
v1[0] = MadjT[0][col]; v1[1] = MadjT[1][col]; v1[2] = MadjT[2][col];
make_reflector(v1, v1); reflect_cols(M, v1);
v2 = cross(M[0], M[1]);
make_reflector(v2, v2); reflect_rows(M, v2);
w = M[0][0]; x = M[0][1]; y = M[1][0]; z = M[1][1];
if (w*z>x*y)
{
c = z+w; s = y-x; d = sqrt(c*c+s*s); c = c/d; s = s/d;
Q[0][0] = Q[1][1] = c; Q[0][1] = -(Q[1][0] = s);
}
else
{
c = z-w; s = y+x; d = sqrt(c*c+s*s); c = c/d; s = s/d;
Q[0][0] = -(Q[1][1] = c); Q[0][1] = Q[1][0] = s;
}
Q[0][2] = Q[2][0] = Q[1][2] = Q[2][1] = 0.0; Q[2][2] = 1.0;
reflect_cols(Q, v1); reflect_rows(Q, v2);
}
/******* Polar Decomposition *******/
/* Polar Decomposition of 3x3 matrix,
* M = QS. See Nicholas Higham and Robert S. Schreiber,
* Fast Polar Decomposition of An Arbitrary Matrix,
* Technical Report 88-942, October 1988,
* Department of Computer Science, Cornell University.
*/
template<class Real>
Real polar_decomp(const defaulttype::Mat<3,3,Real>& M, defaulttype::Mat<3,3,Real>& Q, defaulttype::Mat<3,3,Real>& S)
{
defaulttype::Mat<3,3,Real> Mk, MadjTk, Ek;
Real det, M_one, M_inf, MadjT_one, MadjT_inf, E_one, gamma, g1, g2;
Mk.transpose(M);
M_one = norm_one(Mk); M_inf = norm_inf(Mk);
do
{
adjoint_transpose(Mk, MadjTk);
det = dot(Mk[0], MadjTk[0]);
if (det==0.0)
{
do_rank2(Mk, MadjTk, Mk);
break;
}
MadjT_one = norm_one(MadjTk); MadjT_inf = norm_inf(MadjTk);
gamma = (Real)sqrt(sqrt((MadjT_one*MadjT_inf)/(M_one*M_inf))/fabs(det));
g1 = gamma*((Real)0.5);
g2 = ((Real)0.5)/(gamma*det);
Ek = Mk;
Mk = Mk*g1 + MadjTk*g2;
Ek -= Mk;
E_one = norm_one(Ek);
M_one = norm_one(Mk); M_inf = norm_inf(Mk);
}
while (E_one>(M_one*1.0e-6));
Q.transpose(Mk);
S = Mk*M;
for (int i=0; i<3; i++)
for (int j=i+1; j<3; j++)
S[i][j] = S[j][i] = ((Real)0.5)*(S[i][j]+S[j][i]);
return (det);
}
} // namespace helper
} // namespace sofa
#endif
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