Cross-entropy method: Difference between revisions

Revision as of 08:26, 20 July 2018

Revision as of 13:08, 26 August 2018

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===Pseudo-code===

1. mu:=-6; sigma2:=100; t:=0; maxits=100; // Initialize parameters

mu:=-6

2. N:=100; Ne:=10; //

sigma2:=100

3. while t < maxits and sigma2 > epsilon // While maxits not exceeded and not converged

t:=0

4. X = SampleGaussian(mu,sigma2,N); // Obtain N samples from current sampling distribution

maxits:=100

5. S = exp(-(X-2)^2) + 0.8 exp(-(X+2)^2); // Evaluate objective function at sampled points

N:=100

6. X = sort(X,S); // Sort X by objective function values (in descending order)

Ne:=10

7. mu = mean(X(1:Ne)); sigma2=var(X(1:Ne)); // Update parameters of sampling distribution

3. while t < maxits and sigma2 > epsilon // While maxits not exceeded and not converged

8. t = t+1; // Increment iteration counter

'''while''' t < maxits and sigma2 > epsilon

9. return mu // Return mean of final sampling distribution as solution

4. X = SampleGaussian(mu,sigma2,N); // Obtain N samples from current sampling distribution

X:=SampleGaussian(mu,sigma2,N)

5. S = exp(-(X-2)^2) + 0.8 exp(-(X+2)^2); // Evaluate objective function at sampled points

S:=exp(-(X-2)^2) + 0.8 exp(-(X+2)^2)

6. X = sort(X,S); // Sort X by objective function values (in descending order)

X:=sort(X,S)

7. mu = mean(X(1:Ne)); sigma2=var(X(1:Ne)); // Update parameters of sampling distribution

mu:=mean(X(1:Ne))

sigma2:=var(X(1:Ne))

t:=t+1

9. return mu // Return mean of final sampling distribution as solution

2.'''return''' mu N:=100; Ne:=10; //

==Related methods==