From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Tue, 2 Oct 2012 00:19:43 -0400 Subject: cdec cleanup, remove bayesian stuff, parsing stuff --- .../ProjectedGradientDescent.java | 154 --------------------- 1 file changed, 154 deletions(-) delete mode 100644 gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java (limited to 'gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java') diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java deleted file mode 100644 index 0186e945..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java +++ /dev/null @@ -1,154 +0,0 @@ -package optimization.gradientBasedMethods; - -import java.io.IOException; - -import optimization.gradientBasedMethods.stats.OptimizerStats; -import optimization.linesearch.DifferentiableLineSearchObjective; -import optimization.linesearch.LineSearchMethod; -import optimization.linesearch.ProjectedDifferentiableLineSearchObjective; -import optimization.stopCriteria.StopingCriteria; -import optimization.util.MathUtils; - - -/** - * This class implements the projected gradiend - * as described in Bertsekas "Non Linear Programming" - * section 2.3. - * - * The update is given by: - * x_k+1 = x_k + alpha^k(xbar_k-x_k) - * Where xbar is: - * xbar = [x_k -s_k grad(f(x_k))]+ - * where []+ is the projection into the feasibility set - * - * alpha is the step size - * s_k - is a positive scalar which can be view as a step size as well, by - * setting alpha to 1, then x_k+1 = [x_k -s_k grad(f(x_k))]+ - * This is called taking a step size along the projection arc (Bertsekas) which - * we will use by default. - * - * Note that the only place where we actually take a step size is on pick a step size - * so this is going to be just like a normal gradient descent but use a different - * armijo line search where we project after taking a step. - * - * - * @author javg - * - */ -public class ProjectedGradientDescent extends ProjectedAbstractGradientBaseMethod{ - - - - - public ProjectedGradientDescent(LineSearchMethod lineSearch) { - this.lineSearch = lineSearch; - } - - //Use projected differential objective instead - public void initializeStructures(Objective o, OptimizerStats stats, StopingCriteria stop) { - lso = new ProjectedDifferentiableLineSearchObjective(o); - }; - - - ProjectedObjective obj; - public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){ - obj = o; - return super.optimize(o, stats, stop); - } - - public double[] getDirection(){ - for(int i = 0; i< gradient.length; i++){ - direction[i] = -gradient[i]; - } - return direction; - } - - - - -} - - - - - - - -///OLD CODE - -//Use projected gradient norm -//public boolean stopCriteria(double[] gradient){ -// if(originalDirenctionL2Norm == 0){ -// System.out.println("Leaving original direction norm is zero"); -// return true; -// } -// if(MathUtils.L2Norm(direction)/originalDirenctionL2Norm < gradientConvergenceValue){ -// System.out.println("Leaving projected gradient Norm smaller than epsilon"); -// return true; -// } -// if((previousValue - currValue)/Math.abs(previousValue) < valueConvergenceValue) { -// System.out.println("Leaving value change below treshold " + previousValue + " - " + currValue); -// System.out.println(previousValue/currValue + " - " + currValue/currValue -// + " = " + (previousValue - currValue)/Math.abs(previousValue)); -// return true; -// } -// return false; -//} -// - -//public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){ -// stats.collectInitStats(this, o); -// obj = o; -// step = 0; -// currValue = o.getValue(); -// previousValue = Double.MAX_VALUE; -// gradient = o.getGradient(); -// originalGradientL2Norm = MathUtils.L2Norm(gradient); -// parameterChange = new double[gradient.length]; -// getDirection(); -// ProjectedDifferentiableLineSearchObjective lso = new ProjectedDifferentiableLineSearchObjective(o,direction); -// -// originalDirenctionL2Norm = MathUtils.L2Norm(direction); -// //MatrixOutput.printDoubleArray(currParameters, "parameters"); -// for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){ -// // System.out.println("Iter " + currentProjectionIteration); -// //o.printParameters(); -// -// -// -// if(stop.stopOptimization(gradient)){ -// stats.collectFinalStats(this, o); -// lastStepUsed = step; -// return true; -// } -// lso.reset(direction); -// step = lineSearch.getStepSize(lso); -// if(step==-1){ -// System.out.println("Failed to find step"); -// stats.collectFinalStats(this, o); -// return false; -// -// } -// -// //Update the direction for stopping criteria -// previousValue = currValue; -// currValue = o.getValue(); -// gradient = o.getGradient(); -// direction = getDirection(); -// if(MathUtils.dotProduct(gradient, direction) > 0){ -// System.out.println("Not a descent direction"); -// System.out.println(" current stats " + stats.prettyPrint(1)); -// System.exit(-1); -// } -// stats.collectIterationStats(this, o); -// } -// lastStepUsed = step; -// stats.collectFinalStats(this, o); -// return false; -// } - -//public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){ -// System.out.println("Objective is not a projected objective"); -// throw new RuntimeException(); -//} - -- cgit v1.2.3