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/**
* $Id$
* ***** BEGIN GPL/BL DUAL LICENSE BLOCK *****
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License
* as published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version. The Blender
* Foundation also sells licenses for use in proprietary software under
* the Blender License. See http://www.blender.org/BL/ for information
* about this.
*
* This program 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software Foundation,
* Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
*
* The Original Code is Copyright (C) 2001-2002 by NaN Holding BV.
* All rights reserved.
*
* The Original Code is: all of this file.
*
* Contributor(s): none yet.
*
* ***** END GPL/BL DUAL LICENSE BLOCK *****
*/
#include "IK_ConjugateGradientSolver.h"
#define EPS 1.0e-10
IK_ConjugateGradientSolver *
IK_ConjugateGradientSolver::
New(
){
return new IK_ConjugateGradientSolver();
};
IK_ConjugateGradientSolver::
~IK_ConjugateGradientSolver(
){
//nothing to do
};
// Compute the minimum of the potenial function
// starting at point p. On return p contains the
// computed minima, iter the number of iterations performed,
// fret the potenial value at the minima
void
IK_ConjugateGradientSolver::
Solve(
TNT::Vector<MT_Scalar> &p,
MT_Scalar ftol,
int &iter,
MT_Scalar &fret,
DifferentiablePotenialFunctionNd &potenial,
int max_its
){
int j;
MT_Scalar gg,gam,fp,dgg;
int n = potenial.Dimension();
ArmVectors(n);
// initialize --- FIXME we probably have these
// values to hand already.
fp = potenial.Evaluate(p);
potenial.Derivative(p,m_xi);
for (j = 1; j<=n; j++) {
m_g(j) = -m_xi(j);
m_xi(j) = m_h(j) = m_g(j);
}
for (iter =1;iter <= max_its; iter++) {
LineMinimize(p,m_xi,fret,potenial);
if (2 *TNT::abs(fret-fp) <= ftol*(TNT::abs(fret) + TNT::abs(fp) + EPS)) {
return;
}
fp = fret;
potenial.Derivative(p,m_xi);
dgg = gg = 0.0;
for (j = 1; j<=n;j++) {
gg += m_g(j)*m_g(j);
//dgg+= xi(j)*xi(j);
dgg += (m_xi(j) + m_g(j))*m_xi(j);
}
if (gg == 0.0) {
return;
}
gam = dgg/gg;
for (j = 1; j<=n; j++) {
m_g(j) = -m_xi(j);
m_xi(j) = m_h(j) = m_g(j) + gam*m_h(j);
}
}
// FIXME throw exception
//assert(false);
};
IK_ConjugateGradientSolver::
IK_ConjugateGradientSolver(
){
//nothing to do
}
void
IK_ConjugateGradientSolver::
ArmVectors(
int dimension
){
m_g.newsize(dimension);
m_h.newsize(dimension);
m_xi.newsize(dimension);
m_xi_temp.newsize(dimension);
};
void
IK_ConjugateGradientSolver::
LineMinimize(
TNT::Vector<MT_Scalar> & p,
const TNT::Vector<MT_Scalar> & xi,
MT_Scalar &fret,
DifferentiablePotenialFunctionNd &potenial
){
MT_Scalar ax(0),bx(1); // initial bracket guess
MT_Scalar cx,fa,fb,fc;
MT_Scalar xmin(0); // the 1d function minima
potenial.SetLineVector(p,xi);
IK_LineMinimizer::InitialBracket1d(ax,bx,cx,fa,fb,fc,potenial);
fret = IK_LineMinimizer::DerivativeBrent1d(ax,bx,cx,potenial,xmin,0.001);
// x_min in minimum along line and corresponds with
// p[] + x_min *xi[]
TNT::vectorscale(m_xi_temp,xi,xmin);
TNT::vectoradd(p,m_xi_temp);
};
#undef EPS
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