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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 *****
*/
#ifndef NAN_INCLUDED_IK_ConjugateGradientSolver_h
#define NAN_INCLUDED_IK_ConjugateGradientSolver_h
/**
* @author Laurence Bourn
* @date 28/6/2001
*/
#include "TNT/cmat.h"
#include "MT_Scalar.h"
#include "IK_LineMinimizer.h"
/**
* These classes locally minimize n dimensional potenial functions.
* See Numerical Recipes in C www.nr.com for more details.
* If a n dimensionable potenial function is
* differentiable, then it is diferentiable along
* any vector x. This can be found by the dot product
* of the gradient operator with x.
* The conjugate gradient solver boils down to
* a collection of line minimizations along various lines
* defined by position,direction pairs. There are
* methods in this class to set the lines along which
* minimizations via the DiffentiablePotenialFunction1d interface
* are to be performed.
*
* @warning I don't like data inheritance but it is the most efficient
* wasy to do this here.
*/
class DifferentiablePotenialFunctionNd
: public DifferentiablePotenialFunction1d
{
public :
/**
* Inherited from DiffentiablePotenialFunction1d
*
* virtual
* MT_Scalar
* Evaluate1d(
* MT_Scalar x
* ) = 0;
*
* virtual
* MT_Scalar
* Derivative1d(
* MT_Scalar x
* ) = 0;
*/
/// Methods to set the current line in N dimensions
void
SetLineVector(
const TNT::Vector<MT_Scalar> &pos,
const TNT::Vector<MT_Scalar> &dir
){
m_line_pos = pos;
m_line_dir = dir;
};
virtual
MT_Scalar
Evaluate(
const TNT::Vector<MT_Scalar> &x
) = 0;
virtual
void
Derivative(
const TNT::Vector<MT_Scalar> &x,
TNT::Vector<MT_Scalar> &dy
) = 0;
/// @return The dimension of the domain of the potenial function
virtual
int
Dimension(
) const =0;
virtual
~DifferentiablePotenialFunctionNd(
){
};
protected :
DifferentiablePotenialFunctionNd(){};
TNT::Vector<MT_Scalar> m_line_pos;
TNT::Vector<MT_Scalar> m_line_dir;
};
class IK_ConjugateGradientSolver
: public MEM_NonCopyable
{
public :
/**
* This class necessarily needs some (potenially large)
* temporary vectors to aid computation. We therefore
* insist creation of these objects on the heap.
*/
static
IK_ConjugateGradientSolver *
New(
);
/**
* 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
Solve(
TNT::Vector<MT_Scalar> &p,
MT_Scalar ftol,
int &iter,
MT_Scalar &fret,
DifferentiablePotenialFunctionNd &potenial,
int max_its = 200
);
~IK_ConjugateGradientSolver(
);
private :
void
LineMinimize(
TNT::Vector<MT_Scalar> & p,
const TNT::Vector<MT_Scalar> & xi,
MT_Scalar &fret,
DifferentiablePotenialFunctionNd &potenial
);
IK_ConjugateGradientSolver(
);
void
ArmVectors(
int dimension
);
TNT::Vector<MT_Scalar> m_g;
TNT::Vector<MT_Scalar> m_h;
TNT::Vector<MT_Scalar> m_xi;
TNT::Vector<MT_Scalar> m_xi_temp;
};
#endif
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