Package weka.core

Class ConjugateGradientOptimization

java.lang.Object
weka.core.Optimization
weka.core.ConjugateGradientOptimization
All Implemented Interfaces:
RevisionHandler, TechnicalInformationHandler

public abstract class ConjugateGradientOptimization extends Optimization implements RevisionHandler
This subclass of Optimization.java implements conjugate gradient descent rather than BFGS updates, by overriding findArgmin(), with the same tests for convergence, and applies the same line search code. Note that constraints are NOT actually supported. Using this class instead of Optimization.java can reduce runtime when there are many parameters. Uses the second hybrid method proposed in "An Efficient Hybrid Conjugate Gradient Method for Unconstrained Optimization" by Dai and Yuan (2001). See also information in the getTechnicalInformation() method.
Version:
$Revision: 10203 $
Author:
Eibe Frank
  • Constructor Details

    • ConjugateGradientOptimization

      public ConjugateGradientOptimization()
      Constructor that sets MAXITS to 2000 by default and the parameter in the second weak Wolfe condition to 0.1.
  • Method Details

    • getTechnicalInformation

      public TechnicalInformation getTechnicalInformation()
      Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
      Specified by:
      getTechnicalInformation in interface TechnicalInformationHandler
      Overrides:
      getTechnicalInformation in class Optimization
      Returns:
      the technical information about this class
    • findArgmin

      public double[] findArgmin(double[] initX, double[][] constraints) throws Exception
      Main algorithm. NOTE: constraints are not actually supported.
      Overrides:
      findArgmin in class Optimization
      Parameters:
      initX - initial point of x, assuming no value's on the bound!
      constraints - both arrays must contain Double.NaN
      Returns:
      the solution of x, null if number of iterations not enough
      Throws:
      Exception - if an error occurs