/usr/include/trilinos/GlobiPack_LineSearchBase.hpp is in libtrilinos-globipack-dev 12.10.1-3.
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// GlobiPack: Collection of Scalar 1D globalizaton utilities
// Copyright (2009) Sandia Corporation
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#ifndef GLOBIPACK_LINE_SEARCH_BASE_HPP
#define GLOBIPACK_LINE_SEARCH_BASE_HPP
#include "GlobiPack_MeritFunc1DBase.hpp"
#include "Teuchos_Describable.hpp"
#include "Teuchos_VerboseObject.hpp"
#include "Teuchos_ParameterListAcceptor.hpp"
namespace GlobiPack {
/** \brief Base class for 1D linearsearch algorithms.
*
* ToDo: Finish Documentation!
*/
template<typename Scalar>
class LineSearchBase
: virtual public Teuchos::Describable,
virtual public Teuchos::VerboseObject<LineSearchBase<Scalar> >,
virtual public Teuchos::ParameterListAcceptor
{
public:
/** \brief Determines if the linesearch algorithm requires the base
* derivative at <tt>Dphi(0)</tt> or not.
*/
virtual bool requiresBaseDeriv() const = 0;
/** \brief Determines if the linesearch algorithm requires that
* <tt>Dphi(alpha)</tt> can be computed or not.
*/
virtual bool requiresDerivEvals() const = 0;
/** \brief Called to perform a linesearch.
*
* \param phi [in] The merit function object that will compute the merit
* function value <tt>phi(alpha)</tt> and/or derivative <tt>Dphi(alpha) at
* different points <tt>alpha</tt>. The last call to <tt>phi.eval(...)</tt>
* will always be at the value of <tt>point_kp1->alpha</tt> returned.
*
* \param point_k [in] The evaluation of the merit function and optionally
* its derivative at <tt>alpha=0.0</tt>.
*
* \param point_kp1 [in/out] On input, <tt>point_kp1->alpha</tt> is the
* initial value to try out (usually 1.0 for most Newton-based algorithms).
* Also, <tt>point_kp1->phi</tt> must be computed at this value for alpha as
* well as <tt>point_kp1->Dphi</tt> if required. On output,
* <tt>point_kp1->alpha</tt> is the accepted value for a successful line
* search, or it will be the <tt>alpha</tt> for the minimum
* <tt>phi(alpha)</tt> found during a failed line search algorithm.
*
* <b>Preconditions:</b><ul>
*
* <tt>point_k.alpha == 0.0</tt>
*
* <tt>point_k.phi != PointEval1D<Scalar>::valNotGiven()</tt>
*
* <li> [<tt>this->requiresBaseDeriv()==true</tt>]
* <tt>point_k.Dphi != PointEval1D<Scalar>::valNotGiven()</tt>
*
* <li> [<tt>this->requiresBaseDeriv()==true</tt>]
* <tt>point_k.Dphi < 0.0</tt> (throw <tt>Exceptions::NotDescentDirection</tt>)
*
* <li> [<tt>this->requiresDerivEvals()==true</tt>]
* <tt>phi.supportsDerivEvals()==true</tt>
*
* <li> <tt>!is_null(point_kp1)</tt>
*
* <tt>point_kp1->phi != PointEval1D<Scalar>::valNotGiven()</tt>
*
* <li> [<tt>this->requiresDerivEvals()==true</tt>]
* <tt>point_kp1->Dphi != PointEval1D<Scalar>::valNotGiven()</tt>
*
* </ul>
*
* \returns <tt>true</tt> for successful line search or <tt>false</tt> for a
* line search failure.
*
* This function computes the approximate minimum to 1D merit function
* <tt>phi(alpha)</tt>. More specifically the following problem is
* approximately solved:
\verbatim
min phi(alpha) s.t. alpha = [0, alpha_upper]<br>
\endverbatim
* For many lineserach algorithms, if the initial <tt>point_kp1->alpha</tt>
* satisfies the internally defined descent requirement, then it will
* typically be choosen over smaller values of <tt>point_kp1->alpha</tt>
* that may result in a greater reduction in the given merit function.
* Other linesearch implementions will actually seek an approximate
* minimizer.
*
* If the maximum number of iterations is exceeded without finding an
* acceptable point, then the subclass object will return <tt>false</tt> and
* will return values of <tt>point_kp1->alpha</tt> and
* <tt>point_kp1->phi</tt> will be for the lowest value of <tt>phi_kp1 =
* phi(alpha_k)</tt> found. In this case, the last call to
* <tt>phi(alpha_k)</tt> will be this best value of <tt>phi_kp1</tt>.
*/
virtual bool doLineSearch(
const MeritFunc1DBase<Scalar> &phi,
const PointEval1D<Scalar> &point_k,
const Ptr<PointEval1D<Scalar> > &point_kp1,
const Ptr<int> &numIters
) const = 0;
};
} // namespace GlobiPack
#endif // GLOBIPACK_LINE_SEARCH_BASE_HPP
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