pal.math
Class ConjugateDirectionSearch

java.lang.Object
  pal.math.MultivariateMinimum
      pal.math.ConjugateDirectionSearch

public class ConjugateDirectionSearch
extends MultivariateMinimum

methods for minimization of a real-valued function of several variables without using derivatives (Brent's modification of a conjugate direction search method proposed by Powell)

Author:

Korbinian Strimmer

Nested Class Summary

Nested classes/interfaces inherited from class pal.math.MultivariateMinimum

MultivariateMinimum.Factory

Field Summary

boolean	illc illc should be set to true if the problem is known to be ill-conditioned.
int	prin controls the printed output from the routine (0 -> no output, 1 -> print only starting and final values, 2 -> detailed map of the minimization process, 3 -> print also eigenvalues and vectors of the search directions), the default value is 0
double	scbd scbd is a scaling parameter.
double	step step is a steplength parameter and should be set equal to the expected distance from the solution.

Fields inherited from class pal.math.MultivariateMinimum

maxFun, numFun, numFuncStops

Constructor Summary

ConjugateDirectionSearch()
constructor

Method Summary

static MultivariateMinimum.Factory	generateFactory() Generate a MultivariateMinimum.Factory for a ConjugateDirectionSearch
void	optimize(MultivariateFunction f, double[] xvector, double tolfx, double tolx) The actual optimization routine (needs to be implemented in a subclass of MultivariateMinimum).
void	optimize(MultivariateFunction f, double[] xvector, 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 pal.math.MultivariateMinimum

copy, findMinimum, findMinimum, findMinimum, stopCondition

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

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 minimization process, 3 -> print also eigenvalues and vectors of the search directions), the default value is 0

step

public double step

step is a steplength parameter and should be set equal to the expected distance from the solution. exceptionally small or large values of step lead to slower convergence on the first few iterations the default value for step is 1.0

scbd

public double scbd

scbd is a scaling parameter. 1.0 is the default and indicates no scaling. if the scales for the different parameters are very different, scbd should be set to a value of about 10.0.

illc

public boolean illc

illc should be set to true if the problem is known to be ill-conditioned. the default is false. this variable is automatically set, when the problem is found to to be ill-conditioned during iterations.

Constructor Detail

ConjugateDirectionSearch

public ConjugateDirectionSearch()

constructor

Method Detail

optimize

public void optimize(MultivariateFunction f,
                     double[] xvector,
                     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

xvector - 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

optimize

public void optimize(MultivariateFunction f,
                     double[] xvector,
                     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 function

xvector - 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

monitor - A monitor object that receives information about the minimising process (for display purposes)

generateFactory

public static final MultivariateMinimum.Factory generateFactory()

Generate a MultivariateMinimum.Factory for a ConjugateDirectionSearch