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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));
}
}
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