Illustrative Example Problems
Problem Description
Program Descriptions
 toyexample.m
This program runs an interactive demonstration of the convergence of the CE method using Normal updating in 1 dimension. Click the mouse or press a key to advance the display by one iteration.Usage:
Call the program from MATLAB, with the following syntax: toyexample  toyexample2.m
This program runs an alternate interactive demonstration of the convergence of the CE method using Normal updating in 1 dimension. Click the mouse or press a key to advance the display by one iteration.Usage:
Call the program from MATLAB, with the following syntax: toyexample2  ssmall.m
This program illustrates the evolution of the CE method on a very simple problem, showing that it is quite possible to have the standard deviation parameter reduced below an extremely small number.Usage:
Call the program from MATLAB, with the following syntax: sigma = ssmall( N , rho )Example: sig = ssmall( 200 , 0.1 )
Inputs: N  number of samples each iteration rho  fraction of best performing samples to take Outputs: sigma  A vector of all of the std. deviations
Bibliography

D. P. Kroese and R. Y. Rubinstein.
The CrossEntropy Method: A Unified Approach to Combinatorial Optimization, MonteCarlo Simulation and Machine Learning.
Springer, 2004.