Global random optimization by simultaneous perturbation stochastic approximation
Proceedings of the 33nd conference on Winter simulation
A New Algorithm for Stochastic Discrete Resource AllocationOptimization
Discrete Event Dynamic Systems
Some current issues in quasi-Monte Carlo methods
Journal of Complexity
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Searching for extensible Korobov rules
Journal of Complexity
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The stochastic root-finding problem: Overview, solutions, and open questions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
On the performance of the cross-entropy method
Winter Simulation Conference
Random search in high dimensional stochastic optimization
Proceedings of the Winter Simulation Conference
Root finding via darts: dynamic adaptive random target shooting
Proceedings of the Winter Simulation Conference
Fitting Statistical Models of Random Search in Simulation Studies
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Limit Theorems for Simulation-Based Optimization via Random Search
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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This study concerns a generic model-free stochastic optimization problem requiring the minimization of a risk function defined on a given bounded domain in a Euclidean space. Smoothness assumptions regarding the risk function are hypothesized, and members of the underlying space of probabilities are presumed subject to a large deviation principle; however, the risk function may well be nonconvex and multimodal. A general approach to finding the risk minimizer on the basis of decision/observation pairs is proposed. It consists of repeatedly observing pairs over a collection of design points. Principles are derived for choosing the number of these design points on the basis of an observation budget, and for allocating the observations between these points in both prescheduled and adaptive settings. On the basis of these principles, large-deviation type bounds of the minimizer in terms of sample size are established.