Extension of the direct optimization algorithm for noisy functions
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms
Computational Optimization and Applications
Line search methods with variable sample size for unconstrained optimization
Journal of Computational and Applied Mathematics
ACM Transactions on Modeling and Computer Simulation (TOMACS)
On sample size control in sample average approximations for solving smooth stochastic programs
Computational Optimization and Applications
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The sample-path method is one of the most important tools in simulation-based optimization. The basic idea of the method is to approximate the expected simulation output by the average of sample observations with a common random number sequence. In this paper, we describe a new variant of Powell’s unconstrained optimization by quadratic approximation (UOBYQA) method, which integrates a Bayesian variable-number sample-path (VNSP) scheme to choose appropriate number of samples at each iteration. The statistically accurate scheme determines the number of simulation runs, and guarantees the global convergence of the algorithm. The VNSP scheme saves a significant amount of simulation operations compared to general purpose ‘fixed-number’ sample-path methods. We present numerical results based on the new algorithm.