Numerical techniques for stochastic optimization
Numerical techniques for stochastic optimization
Stochastic algorithms with Armijo stepsizes for minimization of functions
Journal of Optimization Theory and Applications
SIAM Journal on Control and Optimization
Sample-path optimization of convex stochastic performance functions
Mathematical Programming: Series A and B
Convergence analysis of stochastic algorithms
Mathematics of Operations Research
A simulation-based approach to two-stage stochastic programming with recourse
Mathematical Programming: Series A and B
Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A simulation-based optimization heuristic using self-organization for complex assembly lines
Proceedings of the Winter Simulation Conference
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In this talk we consider a problem of optimizing an expected value function by Monte Carlo simulation methods. We discuss, somewhat in details, the stochastic counterpart (sample path) method where a relatively large sample is generated and the expected value function is approximated by the corresponding average function. Consequently the obtained approximation problem is solved by deterministic methods of nonlinear programming. One of advantages of this approach, compared with the classical stochastic approximation method, is that a statistical inference can be incorporated into optimization algorithms. This allows to develop a validation analysis, stopping rules and variance reduction techniques which in some cases considerably enhance numerical performance of the stochastic counterpart method.