From 7f69c868c41e4b36eecf9d3b1dc22f3f3aa1540c Mon Sep 17 00:00:00 2001
From: desaicwtf <desaicwtf@ec762483-ff6d-05da-a07a-a48fb63a330f>
Date: Fri, 9 Jul 2010 16:59:55 +0000
Subject: add optimization library source code

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@204 ec762483-ff6d-05da-a07a-a48fb63a330f
---
 .../linesearch/ArmijoLineSearchMinimization.java   | 102 +++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java

(limited to 'gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java')

diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
new file mode 100644
index 00000000..c9f9b8df
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
@@ -0,0 +1,102 @@
+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;
+	}
+		
+}
-- 
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