You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
128 lines
3.5 KiB
128 lines
3.5 KiB
/*
|
|
* This file is part of ACADO Toolkit.
|
|
*
|
|
* ACADO Toolkit -- A Toolkit for Automatic Control and Dynamic Optimization.
|
|
* Copyright (C) 2008-2014 by Boris Houska, Hans Joachim Ferreau,
|
|
* Milan Vukov, Rien Quirynen, KU Leuven.
|
|
* Developed within the Optimization in Engineering Center (OPTEC)
|
|
* under supervision of Moritz Diehl. All rights reserved.
|
|
*
|
|
* ACADO Toolkit 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 3 of the License, or (at your option) any later version.
|
|
*
|
|
* ACADO Toolkit 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 ACADO Toolkit; if not, write to the Free Software
|
|
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
|
*
|
|
*/
|
|
|
|
|
|
/**
|
|
* \file include/acado/nlp_derivative_approximation/constant_hessian.hpp
|
|
* \author Boris Houska, Hans Joachim Ferreau
|
|
*
|
|
*/
|
|
|
|
|
|
#ifndef ACADO_TOOLKIT_CONSTANT_HESSIAN_HPP
|
|
#define ACADO_TOOLKIT_CONSTANT_HESSIAN_HPP
|
|
|
|
|
|
#include <acado/utils/acado_utils.hpp>
|
|
#include <acado/nlp_derivative_approximation/nlp_derivative_approximation.hpp>
|
|
|
|
|
|
BEGIN_NAMESPACE_ACADO
|
|
|
|
|
|
/**
|
|
* \brief Implements a constant Hessian as approximation of second-order derivatives within NLPsolvers.
|
|
*
|
|
* \ingroup NumericalAlgorithms
|
|
*
|
|
* The class ConstantHessian implements a constant Hessian as approximation of
|
|
* second-order derivatives within iterative NLPsolvers.
|
|
*
|
|
* \author Boris Houska, Hans Joachim Ferreau
|
|
*/
|
|
class ConstantHessian : public NLPderivativeApproximation
|
|
{
|
|
|
|
//
|
|
// PUBLIC MEMBER FUNCTIONS:
|
|
//
|
|
public:
|
|
|
|
/** Default constructor. */
|
|
ConstantHessian( );
|
|
|
|
/** Default constructor. */
|
|
ConstantHessian( UserInteraction* _userInteraction
|
|
);
|
|
|
|
/** Copy constructor (deep copy). */
|
|
ConstantHessian( const ConstantHessian& rhs );
|
|
|
|
/** Destructor. */
|
|
virtual ~ConstantHessian( );
|
|
|
|
/** Assignment operator (deep copy). */
|
|
ConstantHessian& operator=( const ConstantHessian& rhs );
|
|
|
|
virtual NLPderivativeApproximation* clone( ) const;
|
|
|
|
|
|
|
|
virtual returnValue initHessian( BlockMatrix& B, /**< matrix to be initialised */
|
|
uint N, /**< number of intervals */
|
|
const OCPiterate& iter /**< current iterate */
|
|
);
|
|
|
|
virtual returnValue initScaling( BlockMatrix& B, /**< matrix to be updated */
|
|
const BlockMatrix& x, /**< direction x */
|
|
const BlockMatrix& y /**< residuum */
|
|
);
|
|
|
|
|
|
virtual returnValue apply( BlockMatrix &B, /**< matrix to be updated */
|
|
const BlockMatrix &x, /**< direction x */
|
|
const BlockMatrix &y /**< residuum */
|
|
);
|
|
|
|
|
|
|
|
//
|
|
// PROTECTED MEMBER FUNCTIONS:
|
|
//
|
|
protected:
|
|
|
|
|
|
|
|
//
|
|
// PROTECTED DATA MEMBERS:
|
|
//
|
|
protected:
|
|
|
|
};
|
|
|
|
|
|
CLOSE_NAMESPACE_ACADO
|
|
|
|
//#include <acado/nlp_derivative_approximation/constant_hessian.ipp>
|
|
|
|
|
|
// collect remaining headers
|
|
#include <acado/nlp_derivative_approximation/bfgs_update.hpp>
|
|
|
|
#endif // ACADO_TOOLKIT_CONSTANT_HESSIAN_HPP
|
|
|
|
/*
|
|
* end of file
|
|
*/
|
|
|