Package 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 Details

    • FLETCHER_REEVES_UPDATE

      public static final int FLETCHER_REEVES_UPDATE
      See Also:
    • POLAK_RIBIERE_UPDATE

      public static final int POLAK_RIBIERE_UPDATE
      See Also:
    • BEALE_SORENSON_HESTENES_STIEFEL_UPDATE

      public static final int BEALE_SORENSON_HESTENES_STIEFEL_UPDATE
      See Also:
    • 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 Details

    • ConjugateGradientSearch

      public ConjugateGradientSearch()
    • ConjugateGradientSearch

      public ConjugateGradientSearch(int conGradStyle)
  • Method Details

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