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diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
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+++ b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
@@ -0,0 +1,128 @@
+package optimization.examples;
+
+
+import optimization.gradientBasedMethods.ConjugateGradient;
+
+import optimization.gradientBasedMethods.GradientDescent;
+import optimization.gradientBasedMethods.LBFGS;
+import optimization.gradientBasedMethods.Objective;
+import optimization.gradientBasedMethods.stats.OptimizerStats;
+import optimization.linesearch.GenericPickFirstStep;
+import optimization.linesearch.LineSearchMethod;
+import optimization.linesearch.WolfRuleLineSearch;
+import optimization.stopCriteria.GradientL2Norm;
+import optimization.stopCriteria.StopingCriteria;
+
+
+/**
+ * @author javg
+ *
+ */
+public class x2y2 extends Objective{
+
+	
+	//Implements function ax2+ by2 
+	double a, b;
+	public x2y2(double a, double b){
+		this.a = a;
+		this.b = b;
+		parameters = new double[2];
+		parameters[0] = 4;
+		parameters[1] = 4;
+		gradient = new double[2];
+	}
+	
+	public double getValue() {
+		functionCalls++;
+		return a*parameters[0]*parameters[0]+b*parameters[1]*parameters[1];
+	}
+
+	public double[] getGradient() {
+		gradientCalls++;
+		gradient[0]=2*a*parameters[0];
+		gradient[1]=2*b*parameters[1];
+		return gradient;
+//		if(debugLevel >=2){
+//			double[] numericalGradient = DebugHelpers.getNumericalGradient(this, parameters, 0.000001);
+//			for(int i = 0; i < parameters.length; i++){
+//				double diff = Math.abs(gradient[i]-numericalGradient[i]);
+//				if(diff > 0.00001){
+//					System.out.println("Numerical Gradient does not match");
+//					System.exit(1);
+//				}
+//			}
+//		}
+	}
+
+	
+	
+	public void optimizeWithGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
+		GradientDescent optimizer = new GradientDescent(ls);
+		StopingCriteria stop = new GradientL2Norm(0.001);
+//		optimizer.setGradientConvergenceValue(0.001);
+		optimizer.setMaxIterations(100);
+		boolean succed = optimizer.optimize(o,stats,stop);
+		System.out.println("Ended optimzation Gradient Descent\n" + stats.prettyPrint(1));
+		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
+		if(succed){
+			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
+		}else{
+			System.out.println("Failed to optimize");
+		}
+	}
+	
+	public void optimizeWithConjugateGradient(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
+		ConjugateGradient optimizer = new ConjugateGradient(ls);
+		StopingCriteria stop = new GradientL2Norm(0.001);
+
+		optimizer.setMaxIterations(10);
+		boolean succed = optimizer.optimize(o,stats,stop);
+		System.out.println("Ended optimzation Conjugate Gradient\n" + stats.prettyPrint(1));
+		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
+		if(succed){
+			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
+		}else{
+			System.out.println("Failed to optimize");
+		}
+	}
+	
+	public void optimizeWithLBFGS(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
+		LBFGS optimizer = new LBFGS(ls,10);
+		StopingCriteria stop = new GradientL2Norm(0.001);
+		optimizer.setMaxIterations(10);
+		boolean succed = optimizer.optimize(o,stats,stop);
+		System.out.println("Ended optimzation LBFGS\n" + stats.prettyPrint(1));
+		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
+		if(succed){
+			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
+		}else{
+			System.out.println("Failed to optimize");
+		}
+	}
+	
+	public static void main(String[] args) {
+		x2y2 o = new x2y2(1,10);
+		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
+		o.setDebugLevel(4);
+		LineSearchMethod wolfe = new WolfRuleLineSearch(new GenericPickFirstStep(1),0.001,0.9);;
+//		LineSearchMethod ls = new ArmijoLineSearchMinimization();
+		OptimizerStats stats = new OptimizerStats();
+		o.optimizeWithGradientDescent(wolfe, stats, o);
+		o = new x2y2(1,10);
+		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
+//		ls = new ArmijoLineSearchMinimization();
+		stats = new OptimizerStats();
+		o.optimizeWithConjugateGradient(wolfe, stats, o);
+		o = new x2y2(1,10);
+		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
+//		ls = new ArmijoLineSearchMinimization();
+		stats = new OptimizerStats();
+		o.optimizeWithLBFGS(wolfe, stats, o);	
+	}
+	
+	public String toString(){
+		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue();
+	}
+	
+	
+}