Stochastic discrete optimization
SIAM Journal on Control and Optimization
A new search algorithm for discrete stochastic optimization
WSC '95 Proceedings of the 27th conference on Winter simulation
A method for discrete stochastic optimization
Management Science
Methods for selecting the best system
WSC '91 Proceedings of the 23rd conference on Winter simulation
Accelerating the convergence of the stochastic ruler method for discrete stochastic optimization
Proceedings of the 29th conference on Winter simulation
Accelerating the convergence of random search methods for discrete stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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In this paper, we present a modification of the stochastic ruler method for solving discrete stochastic optimization problems. Our method generates a stationary Markov chain sequence taking values in the feasible set of the underlying discrete optimization problem. The number of visits to every state by this Markov chain is used to estimate the optimal solution. Unlike the original stochastic ruler method, our method is guaranteed to converge almost surely to a global optimal solution. We present empirical results that illustrate the performance of our method, and we show that these results compare favorably with empirical results obtained using the original stochastic ruler method.