ConjugateGradientSearch (Fiji Javadocs 0-SNAPSHOT API)

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).

Author:

Korbinian Strimmer

Field Summary

Fields
Modifier and Type	Field and Description
`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

Fields inherited from class pal.math.MultivariateMinimum
maxFun, numFun, numFuncStops

Constructor Summary

Constructors
Constructor and Description

ConjugateGradientSearch()

ConjugateGradientSearch(int conGradStyle)

Constructors
Constructor and Description
`ConjugateGradientSearch()`
`ConjugateGradientSearch(int conGradStyle)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`optimize(MultivariateFunction f, double[] x, double tolfx, double tolx)` The actual optimization routine (needs to be implemented in a subclass of MultivariateMinimum).

Methods inherited from class pal.math.MultivariateMinimum
copy, findMinimum, findMinimum, stopCondition

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - 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.
- Constructor Detail
  - ConjugateGradientSearch
```
public ConjugateGradientSearch()
```
  - ConjugateGradientSearch
```
public ConjugateGradientSearch(int conGradStyle)
```
- Method Detail
  - 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 function
    
    x - initial guesses for the minimum (contains the location of the minimum on return)
    
    tolfx - absolute tolerance of function value
    
    tolx - absolute tolerance of each parameter

Class ConjugateGradientSearch

Field Summary

Fields inherited from class pal.math.MultivariateMinimum

Constructor Summary

Method Summary

Methods inherited from class pal.math.MultivariateMinimum

Methods inherited from class java.lang.Object

Field Detail

FLETCHER_REEVES_UPDATE

POLAK_RIBIERE_UPDATE

BEALE_SORENSON_HESTENES_STIEFEL_UPDATE

prin

defaultStep

conjugateGradientStyle

Constructor Detail

ConjugateGradientSearch

ConjugateGradientSearch

Method Detail

optimize