This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …

This paper presents a methodology for using varying sample sizes in batch-type
optimization methods for large-scale machine learning problems. The first part of the paper …

This paper proposes a simulation-based optimization (SO) method that enables the efficient
use of complex stochastic urban traffic simulators to address various transportation …

This review discusses some issues related to the use of simulation in transportation
analysis. Potential pitfalls are identified and discussed. An overview of some methods …

The numerical solution of optimization problems governed by partial differential equations
(PDEs) with random coefficients is computationally challenging because of the large number …

Response surface methodology (RSM) is a widely used method for simulation optimization.
Its strategy is to explore small subregions of the decision space in succession instead of …

We consider unconstrained optimization problems where only “stochastic” estimates of the
objective function are observable as replicates from a Monte Carlo oracle. The Monte Carlo …

Choosing the appropriate sample size in Sample Average Approximation (SAA) method is
very challenging. Inappropriate sample size can lead to the generation of low quality …

This paper presents a trust region algorithm to minimize a function f when one has access
only to noise-corrupted function values ̄ ff¯. The model-based algorithm dynamically …

Monte Carlo methods have extensively been used and studied in the area of stochastic
programming. Their convergence properties typically consider global minimizers or first …