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MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
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/* Copyright (C) 2002 Univ. of Massachusetts Amherst, Computer Science Dept.
   This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
   http://www.cs.umass.edu/~mccallum/mallet
   This software is provided under the terms of the Common Public License,
   version 1.0, as published by http://www.opensource.org.  For further
   information, see the file `LICENSE' included with this distribution. */




/** 
   @author Andrew McCallum [email protected]
 */

package cc.mallet.optimize;

import java.util.logging.*;

import cc.mallet.types.MatrixOps;
import cc.mallet.util.MalletLogger;


public class GradientAscent implements Optimizer
{
	private static Logger logger = MalletLogger.getLogger(GradientAscent.class.getName());

	boolean converged = false;
	Optimizable.ByGradientValue optimizable;
  private double maxStep = 1.0;
  private OptimizerEvaluator.ByGradient eval;

  static final double initialStepSize = 0.2;
	double tolerance = 0.001;
	int maxIterations = 200;
	LineOptimizer.ByGradient lineMaximizer;
  double stpmax = 100;

	// "eps" is a small number to rectify the special case of converging
	// to exactly zero function value
	final double eps = 1.0e-10;
  double step = initialStepSize;

	public GradientAscent (Optimizable.ByGradientValue function)
	{
		optimizable = function;
		lineMaximizer = new BackTrackLineSearch(function);
	}

	public Optimizable getOptimizable () { return this.optimizable; }
	public boolean isConverged () { return converged; }

	
  public LineOptimizer.ByGradient getLineMaximizer ()
  {
    return lineMaximizer;
  }

  /* Tricky: this is now set at GradientAscent construction time.  How to set it later?  
   * What to pass as an argument here?  The lineMaximizer needs the function at the time of its construction!
  public void setLineMaximizer (LineOptimizer.ByGradient lineMaximizer)
  {
    this.lineMaximizer = lineMaximizer;
  }*/

  
  /**
   * Sets the tolerance in the convergence test:
   * 2.0*|value-old_value| <= tolerance*(|value|+|old_value|+eps)
   * Default value is 0.001.
   * @param tolerance tolerance for convergence test
   */
  public void setTolerance(double tolerance) {
    this.tolerance = tolerance;
  }
  
  public double getInitialStepSize ()
  {
    return initialStepSize;
  }

  public void setInitialStepSize (double initialStepSize)
  {
    step = initialStepSize;
  }

  public double getStpmax ()
  {
    return stpmax;
  }

  public void setStpmax (double stpmax)
  {
    this.stpmax = stpmax;
  }

	public boolean optimize ()
	{
		return optimize (maxIterations);
	}

	public boolean optimize (int numIterations)
	{
		int iterations;
		double fret;
		double fp = optimizable.getValue ();
    double[] xi = new double [optimizable.getNumParameters()];
		optimizable.getValueGradient(xi);

		for (iterations = 0; iterations < numIterations; iterations++) {
			logger.info ("At iteration "+iterations+", cost = "+fp+", scaled = "+maxStep+" step = "+step+", gradient infty-norm = "+MatrixOps.infinityNorm (xi));

      // Ensure step not too large
      double sum = MatrixOps.twoNorm (xi);
      if (sum > stpmax) {
        logger.info ("*** Step 2-norm "+sum+" greater than max "+stpmax+"  Scaling...");
        MatrixOps.timesEquals (xi,stpmax/sum);
      }

      step = lineMaximizer.optimize (xi, step);
			fret = optimizable.getValue ();
			if (2.0*Math.abs(fret-fp) <= tolerance*(Math.abs(fret)+Math.abs(fp)+eps)) {
        logger.info ("Gradient Ascent: Value difference "+Math.abs(fret-fp)+" below " +
                "tolerance; saying converged.");
        converged = true;
       	return true;
      }
			fp = fret;
			optimizable.getValueGradient(xi);
			if (eval != null) {
			  eval.evaluate (optimizable, iterations);
      }
    }
		return false;
	}

  public void setMaxStepSize (double v)
  {
    maxStep = v;
  }

  public void setEvaluator (OptimizerEvaluator.ByGradient eval)
  {
    this.eval = eval;
  }
}