DE_ex.m Optimization of the Rosenbrock function via differential evolution.
demand.m Demand subroutine for the (s,S) inventory problem.
demandsize.m Generates the size of the demand for the (s,S) inventory problem.
f.m Cost function for the inventory system. Uses demand.m, demandsize.m, interarrival.m, leadtime.m, and order.m.
GA_ex_fig.m An application of a binary-encoded genetic algorithm to the satisfiability (SAT) problem. Uses SATdata.mat and sC.c.
interarrival.m Generates a demand interarrival time for the (s,S) inventory problem.
leadtime.m Generates a lead time for the order in the (s,S) inventory problem.
opt_policy.m Noisy optimization for an (s,S) inventory problem, using cross-entropy. Uses f.m.
order.m Order subroutine for the (s,S) inventory problem.
SA.m An example of continuous optimization using simulated annealing.
SAnnealing_Multi_SA.m Comparison of exact and approximate sampling from the Boltzmann distribution in the simulated annealing example.
SATdata.mat Contains the SAT instance used in the book. Load into the workspace via "load SATdata.mat".
sC.c Evaluates the SAT score function efficiently, given a *sparse* matrix. Must first be compiled within Matlab, using "mex sC.c".
SCM.m CE optimization via the stochastic counterpart method.
SCMb.m Similar to SCM.m. Estimates for mu and sigma are plotted.
StochApprox.m Noisy optimization example using stochastic approximation.