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Diffstat (limited to 'gi/posterior-regularisation/prjava/src/optimization/examples')
3 files changed, 0 insertions, 365 deletions
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java deleted file mode 100644 index 25fa7f09..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java +++ /dev/null @@ -1,110 +0,0 @@ -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)); - - } - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java deleted file mode 100644 index f087681e..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java +++ /dev/null @@ -1,128 +0,0 @@ -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(); - } - - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java deleted file mode 100644 index 391775b7..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java +++ /dev/null @@ -1,127 +0,0 @@ -package optimization.examples; - - -import optimization.gradientBasedMethods.ProjectedGradientDescent; -import optimization.gradientBasedMethods.ProjectedObjective; -import optimization.gradientBasedMethods.stats.OptimizerStats; -import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc; -import optimization.linesearch.InterpolationPickFirstStep; -import optimization.linesearch.LineSearchMethod; -import optimization.projections.BoundsProjection; -import optimization.projections.Projection; -import optimization.projections.SimplexProjection; -import optimization.stopCriteria.CompositeStopingCriteria; -import optimization.stopCriteria.GradientL2Norm; -import optimization.stopCriteria.ProjectedGradientL2Norm; -import optimization.stopCriteria.StopingCriteria; -import optimization.stopCriteria.ValueDifference; - - -/** - * @author javg - * - * - *ax2+ b(y2 -displacement) - */ -public class x2y2WithConstraints extends ProjectedObjective{ - - - double a, b; - double dx; - double dy; - Projection projection; - - - public x2y2WithConstraints(double a, double b, double[] params, double dx, double dy, Projection proj){ - //projection = new BoundsProjection(0.2,Double.MAX_VALUE); - super(); - projection = proj; - this.a = a; - this.b = b; - this.dx = dx; - this.dy = dy; - setInitialParameters(params); - System.out.println("Function " +a+"(x-"+dx+")^2 + "+b+"(y-"+dy+")^2"); - System.out.println("Gradient " +(2*a)+"(x-"+dx+") ; "+(b*2)+"(y-"+dy+")"); - printParameters(); - projection.project(parameters); - printParameters(); - gradient = new double[2]; - } - - public double getValue() { - functionCalls++; - return a*(parameters[0]-dx)*(parameters[0]-dx)+b*((parameters[1]-dy)*(parameters[1]-dy)); - } - - public double[] getGradient() { - if(gradient == null){ - gradient = new double[2]; - } - gradientCalls++; - gradient[0]=2*a*(parameters[0]-dx); - gradient[1]=2*b*(parameters[1]-dy); - return gradient; - } - - - public double[] projectPoint(double[] point) { - double[] newPoint = point.clone(); - projection.project(newPoint); - return newPoint; - } - - public void optimizeWithProjectedGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2WithConstraints o){ - ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls); - StopingCriteria stopGrad = new ProjectedGradientL2Norm(0.001); - StopingCriteria stopValue = new ValueDifference(0.001); - CompositeStopingCriteria compositeStop = new CompositeStopingCriteria(); - compositeStop.add(stopGrad); - compositeStop.add(stopValue); - - optimizer.setMaxIterations(5); - boolean succed = optimizer.optimize(o,stats,compositeStop); - System.out.println("Ended optimzation Projected 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 String toString(){ - - return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue() + " grad (" + getGradient()[0] + ":" + getGradient()[1]+")"; - } - - public static void main(String[] args) { - double a = 1; - double b=1; - double x0 = 0; - double y0 =1; - double dx = 0.5; - double dy = 0.5 ; - double [] parameters = new double[2]; - parameters[0] = x0; - parameters[1] = y0; - x2y2WithConstraints o = new x2y2WithConstraints(a,b,parameters,dx,dy, new SimplexProjection(0.5)); - System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1] + " a " + a + " b "+b ); - o.setDebugLevel(4); - - LineSearchMethod ls = new ArmijoLineSearchMinimizationAlongProjectionArc(new InterpolationPickFirstStep(1)); - - OptimizerStats stats = new OptimizerStats(); - o.optimizeWithProjectedGradientDescent(ls, stats, o); - -// o = new x2y2WithConstraints(a,b,x0,y0,dx,dy); -// stats = new OptimizerStats(); -// o.optimizeWithSpectralProjectedGradientDescent(stats, o); - } - - - - -} |