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authordesaicwtf <desaicwtf@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-07-09 16:59:55 +0000
committerdesaicwtf <desaicwtf@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-07-09 16:59:55 +0000
commitbdea91300c85539ab7153ccba58689612f66bb4d (patch)
treee778ffa1ea4d04a239b58c6e6191c0d4549006f0 /gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
parent0d1d84630a08f1c901cf09b4bcc9356c4165302f (diff)
add optimization library source code
git-svn-id: https://ws10smt.googlecode.com/svn/trunk@204 ec762483-ff6d-05da-a07a-a48fb63a330f
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diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
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+package optimization.linesearch;
+
+import optimization.util.Interpolation;
+
+
+/**
+ * Implements Back Tracking Line Search as described on page 37 of Numerical Optimization.
+ * Also known as armijo rule
+ * @author javg
+ *
+ */
+public class ArmijoLineSearchMinimization implements LineSearchMethod{
+
+ /**
+ * How much should the step size decrease at each iteration.
+ */
+ double contractionFactor = 0.5;
+ double c1 = 0.0001;
+
+ double sigma1 = 0.1;
+ double sigma2 = 0.9;
+
+
+
+ double initialStep;
+ int maxIterations = 10;
+
+
+ public ArmijoLineSearchMinimization(){
+ this.initialStep = 1;
+ }
+
+ //Experiment
+ double previousStepPicked = -1;;
+ double previousInitGradientDot = -1;
+ double currentInitGradientDot = -1;
+
+
+ public void reset(){
+ previousStepPicked = -1;;
+ previousInitGradientDot = -1;
+ currentInitGradientDot = -1;
+ }
+
+ public void setInitialStep(double initial){
+ initialStep = initial;
+ }
+
+ /**
+ *
+ */
+
+ public double getStepSize(DifferentiableLineSearchObjective o) {
+ currentInitGradientDot = o.getInitialGradient();
+ //Should update all in the objective
+ o.updateAlpha(initialStep);
+ int nrIterations = 0;
+ //System.out.println("tried alpha" + initialStep + " value " + o.getCurrentValue());
+ while(!WolfeConditions.suficientDecrease(o,c1)){
+ if(nrIterations >= maxIterations){
+ o.printLineSearchSteps();
+ return -1;
+ }
+ double alpha=o.getAlpha();
+ double alphaTemp =
+ Interpolation.quadraticInterpolation(o.getOriginalValue(), o.getInitialGradient(), alpha, o.getCurrentValue());
+ if(alphaTemp >= sigma1 || alphaTemp <= sigma2*o.getAlpha()){
+// System.out.println("using alpha temp " + alphaTemp);
+ alpha = alphaTemp;
+ }else{
+// System.out.println("Discarding alpha temp " + alphaTemp);
+ alpha = alpha*contractionFactor;
+ }
+// double alpha =o.getAlpha()*contractionFactor;
+
+ o.updateAlpha(alpha);
+ //System.out.println("tried alpha" + alpha+ " value " + o.getCurrentValue());
+ nrIterations++;
+ }
+
+ //System.out.println("Leavning line search used:");
+ //o.printLineSearchSteps();
+
+ previousInitGradientDot = currentInitGradientDot;
+ previousStepPicked = o.getAlpha();
+ return o.getAlpha();
+ }
+
+ public double getInitialGradient() {
+ return currentInitGradientDot;
+
+ }
+
+ public double getPreviousInitialGradient() {
+ return previousInitGradientDot;
+ }
+
+ public double getPreviousStepUsed() {
+ return previousStepPicked;
+ }
+
+}