Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithm

Article Abstract:

Several heuristic methods for large discrete problems were considered to solve the uncertainty involved in allocating a specific number of random draws among a set of possibilities. The problem was undertaken with the objective of minimizing costs from the statistical estimate of the true argmin and reducing the projected size of the confidence sets. Results show that the resolution of a convex optimization problem can lead to the production of an asymptotically optimal sampling method.

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An investigation of the behavior of simulation response surfaces

Article Abstract:

Caution has to be observed in selecting appropriate simulation optimization search technique. Such conclusion was derived after assessment of the behavior of simulation response surfaces revealed that point-estimate measures are highly sensitive to experimental design conditions. It was also noted that gridpoints' inter-gridpoint spacing plays a significant role with regards to the amenability of overall search area to first-order Response Surface Methodology (RSM).

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Stochastic gradient algorithm with random truncations

Article Abstract:

A stochastic gradient algorithm with random truncations was applied to an unconstrained stochastic minimization problem with non-stationary and correlated observations. Results show the noise assumptions to be more complicated and more flexible than those considered by Chen and Zhu, Kushner, and Yin. It is also shown that such an algorithm with truncations on a fixed domain can be used when a priori positions of stable zeros and attractivity domains is known.

Stochastic approximation

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