add optimization library source code

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@204 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 110 insertions, 0 deletions

diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
new file mode 100644
index 00000000..25fa7f09
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
@@ -0,0 +1,110 @@
+package optimization.examples;
+
+
+import optimization.gradientBasedMethods.ConjugateGradient;
+import optimization.gradientBasedMethods.GradientDescent;
+import optimization.gradientBasedMethods.LBFGS;
+import optimization.gradientBasedMethods.Objective;
+import optimization.gradientBasedMethods.Optimizer;
+import optimization.gradientBasedMethods.stats.OptimizerStats;
+import optimization.linesearch.ArmijoLineSearchMinimization;
+import optimization.linesearch.LineSearchMethod;
+import optimization.stopCriteria.GradientL2Norm;
+import optimization.stopCriteria.StopingCriteria;
+import optimization.util.MathUtils;
+
+/**
+ * 
+ * @author javg
+ * f(x) = \sum_{i=1}^{N-1} \left[ (1-x_i)^2+ 100 (x_{i+1} - x_i^2 )^2 \right] \quad \forall x\in\mathbb{R}^N.
+ */
+public class GeneralizedRosenbrock extends Objective{
+
+	
+	
+	public GeneralizedRosenbrock(int dimensions){
+		parameters = new double[dimensions];		
+		java.util.Arrays.fill(parameters, 0);
+		gradient = new double[dimensions];
+		
+	}
+	
+	public GeneralizedRosenbrock(int dimensions, double[] params){
+		parameters = params;	
+		gradient = new double[dimensions];
+	}
+	
+	
+	public double getValue() {
+		functionCalls++;
+		double value = 0;
+		for(int i = 0; i < parameters.length-1; i++){
+			value += MathUtils.square(1-parameters[i]) + 100*MathUtils.square(parameters[i+1] - MathUtils.square(parameters[i]));
+		}
+		
+		return value;
+	}
+
+	/**
+	 * gx = -2(1-x) -2x200(y-x^2)
+	 * gy = 200(y-x^2)
+	 */
+	public double[] getGradient() {
+		gradientCalls++;
+		java.util.Arrays.fill(gradient,0);
+		for(int i = 0; i < parameters.length-1; i++){
+			gradient[i]+=-2*(1-parameters[i]) - 400*parameters[i]*(parameters[i+1] - MathUtils.square(parameters[i]));
+			gradient[i+1]+=200*(parameters[i+1] - MathUtils.square(parameters[i]));
+		}	
+		return gradient;
+	}
+
+	
+
+	
+
+	
+	
+	public String toString(){
+		String  res ="";
+		for(int i = 0; i < parameters.length; i++){
+			res += "P" + i+ " " + parameters[i];
+		}
+		res += " Value " + getValue();
+		return res;
+	}
+	
+	public static void main(String[] args) {
+		
+		GeneralizedRosenbrock o = new GeneralizedRosenbrock(2);
+		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
+		;
+
+		System.out.println("Doing Gradient descent");
+		//LineSearchMethod wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
+		StopingCriteria stop = new GradientL2Norm(0.001);		
+		LineSearchMethod ls = new ArmijoLineSearchMinimization();
+		Optimizer optimizer = new GradientDescent(ls);		
+		OptimizerStats stats = new OptimizerStats();
+		optimizer.setMaxIterations(1000);
+		boolean succed = optimizer.optimize(o,stats, stop);
+		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
+		System.out.println("Doing Conjugate Gradient descent");
+		o = new GeneralizedRosenbrock(2);
+	//	wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
+		optimizer = new ConjugateGradient(ls);
+		stats = new OptimizerStats();
+		optimizer.setMaxIterations(1000);
+		succed = optimizer.optimize(o,stats,stop);
+		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
+		System.out.println("Doing Quasi newton descent");
+		o = new GeneralizedRosenbrock(2);
+		optimizer = new LBFGS(ls,10);
+		stats = new OptimizerStats();
+		optimizer.setMaxIterations(1000);
+		succed = optimizer.optimize(o,stats,stop);
+		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
+
+	}
+	
+}