Sample-path optimization of convex stochastic performance functions
Mathematical Programming: Series A and B
Retrospective simulation response optimization
WSC '91 Proceedings of the 23rd conference on Winter simulation
A projected stochastic approximation algorithm
WSC '91 Proceedings of the 23rd conference on Winter simulation
On the Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs
SIAM Journal on Optimization
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Line search methods with variable sample size for unconstrained optimization
Journal of Computational and Applied Mathematics
| Hi-index | 0.00 |
The Simulation-Optimization (SO) problem is a constrained optimization problem where the objective function is observed with error, usually through an oracle such as a simulation. Retrospective Approximation (RA) is a general technique that can be used to solve SO problems. In RA, the solution to the SO problem is approached using solutions to a sequence of approximate problems, each of which is generated using a specified sample size and solved to a specified error tolerance. In this paper, our focus is parameter choice in RA algorithms, where the term parameter is broadly interpreted. Specifically, we present (i) conditions that guarantee convergence of estimated solutions to the true solution; (ii) convergence properties of the sample-size and error-tolerance sequences that ensure that the sequence of estimated solutions converge to the true solution in an optimal fashion; and (iii) a numerical procedure that efficiently solves the generated approximate problems for one-dimensional SO.