Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Retrospective approximation algorithms for stochastic root finding
WSC '94 Proceedings of the 26th conference on Winter simulation
Convergence analysis of gradient descent stochastic algorithms
Journal of Optimization Theory and Applications
IEEE Transactions on Computers
A review of simulation optimization techniques
Proceedings of the 30th conference on Winter simulation
Retrospective simulation response optimization
WSC '91 Proceedings of the 23rd conference on Winter simulation
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 35th conference on Winter simulation: driving innovation
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Globally convergent limited memory bundle method for large-scale nonsmooth optimization
Mathematical Programming: Series A and B
Discrete Optimization via Simulation Using COMPASS
Operations Research
Kriging metamodeling in constrained simulation optimization: an explorative study
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Simulation optimization using metamodels
Winter Simulation Conference
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An introspective on the retrospective-approximation paradigm
Proceedings of the Winter Simulation Conference
Use of retrospective optimization for placement of oil wells under uncertainty
Proceedings of the Winter Simulation Conference
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We consider discrete stochastic optimization problems where the objective function can only be estimated by a simulation oracle; the oracle is defined only at the discrete points. We propose a method using continuous search with simplex interpolation to solve a wide class of problems. A retrospective framework provides a sequence of deterministic approximating problems that can be solved using continuous optimization techniques that guarantee desirable convergence properties. Numerical experiments show that our method finds the optimal solutions for discrete stochastic optimization problems orders of magnitude faster than existing random search algorithms.