pal.math
Class ConjugateGradientSearch
java.lang.Object
pal.math.MultivariateMinimum
pal.math.ConjugateGradientSearch
public class ConjugateGradientSearch
- extends MultivariateMinimum
minimization of a real-valued function of
several variables using a the nonlinear
conjugate gradient method where several variants of the direction
update are available (Fletcher-Reeves, Polak-Ribiere,
Beale-Sorenson, Hestenes-Stiefel) and bounds are respected.
Gradients are computed numerically if they are not supplied by the
user. The line search is entirely based on derivative
evaluation, similar to the strategy used in macopt (Mackay).
- Version:
- $Id: ConjugateGradientSearch.java,v 1.7 2002/10/27 05:46:28 matt Exp $
- Author:
- Korbinian Strimmer
Field Summary |
static int |
BEALE_SORENSON_HESTENES_STIEFEL_UPDATE
|
int |
conjugateGradientStyle
conjugateGradientStyle determines the method for the
conjugate gradient direction update
update (0 -> Fletcher-Reeves, 1 -> Polak-Ribiere,
2 -> Beale-Sorenson, Hestenes-Stiefel), the default is 2. |
double |
defaultStep
defaultStep is a steplength parameter and should be set equal
to the expected distance from the solution (in a line search)
exceptionally small or large values of defaultStep lead to
slower convergence on the first few iterations (the step length
itself is adapted during search), the default value is 1.0 |
static int |
FLETCHER_REEVES_UPDATE
|
static int |
POLAK_RIBIERE_UPDATE
|
int |
prin
controls the printed output from the routine
(0 -> no output, 1 -> print only starting and final values,
2 -> detailed map of the minimisation process),
the default value is 0 |
Method Summary |
void |
optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx)
The actual optimization routine
(needs to be implemented in a subclass of MultivariateMinimum). |
void |
optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx,
MinimiserMonitor monitor)
The actual optimization routine
It finds a minimum close to vector x when the
absolute tolerance for each parameter is specified. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
FLETCHER_REEVES_UPDATE
public static final int FLETCHER_REEVES_UPDATE
- See Also:
- Constant Field Values
POLAK_RIBIERE_UPDATE
public static final int POLAK_RIBIERE_UPDATE
- See Also:
- Constant Field Values
BEALE_SORENSON_HESTENES_STIEFEL_UPDATE
public static final int BEALE_SORENSON_HESTENES_STIEFEL_UPDATE
- See Also:
- Constant Field Values
prin
public int prin
- controls the printed output from the routine
(0 -> no output, 1 -> print only starting and final values,
2 -> detailed map of the minimisation process),
the default value is 0
defaultStep
public double defaultStep
- defaultStep is a steplength parameter and should be set equal
to the expected distance from the solution (in a line search)
exceptionally small or large values of defaultStep lead to
slower convergence on the first few iterations (the step length
itself is adapted during search), the default value is 1.0
conjugateGradientStyle
public int conjugateGradientStyle
- conjugateGradientStyle determines the method for the
conjugate gradient direction update
update (0 -> Fletcher-Reeves, 1 -> Polak-Ribiere,
2 -> Beale-Sorenson, Hestenes-Stiefel), the default is 2.
ConjugateGradientSearch
public ConjugateGradientSearch()
ConjugateGradientSearch
public ConjugateGradientSearch(int conGradStyle)
optimize
public void optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx)
- Description copied from class:
MultivariateMinimum
- The actual optimization routine
(needs to be implemented in a subclass of MultivariateMinimum).
It finds a minimum close to vector x when the
absolute tolerance for each parameter is specified.
- Specified by:
optimize
in class MultivariateMinimum
- Parameters:
f
- multivariate functionx
- initial guesses for the minimum
(contains the location of the minimum on return)tolfx
- absolute tolerance of function valuetolx
- absolute tolerance of each parameter
optimize
public void optimize(MultivariateFunction f,
double[] x,
double tolfx,
double tolx,
MinimiserMonitor monitor)
- Description copied from class:
MultivariateMinimum
- The actual optimization routine
It finds a minimum close to vector x when the
absolute tolerance for each parameter is specified.
- Overrides:
optimize
in class MultivariateMinimum
- Parameters:
f
- multivariate functionx
- initial guesses for the minimum
(contains the location of the minimum on return)tolfx
- absolute tolerance of function valuetolx
- absolute tolerance of each parametermonitor
- A monitor object that receives information about the minimising process (for display purposes)