Journal ArticleDOI
Stochastic quasigradient methods for optimization of discrete event systems
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TLDR
Stochastic programming techniques are adapted and further developed for applications to discrete event systems where the sample path of the system depends discontinuously on control parameters, which could make the computation of estimates of the gradient difficult.Abstract:
In this paper, stochastic programming techniques are adapted and further developed for applications to discrete event systems. We consider cases where the sample path of the system depends discontinuously on control parameters (e.g. modeling of failures, several competing processes), which could make the computation of estimates of the gradient difficult. Methods which use only samples of the performance criterion are developed, in particular finite differences with reduced variance and concurrent approximation and optimization algorithms. Optimization of the stationary behavior is also considered. Results of numerical experiments and convergence results are reported.read more
Citations
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Journal ArticleDOI
Optimization of computer simulation models with rare events
TL;DR: Particular emphasis will be placed on estimation of rare events and on integration of the associated performance function into stochastic optimization programs.
Evaluation of scenario-generation methods for stochastic programming
Michal Kaut,Stein W. Wallace +1 more
TL;DR: This paper formulate minimal requirements that should be imposed on a scenario generation method before it can be used for solving the stochastic programming model and shows how the requirements can be tested.
Journal ArticleDOI
Sample-path optimization of convex stochastic performance functions
TL;DR: This paper proposes a method for optimizing convex performance functions in stochastic systems, which can include expected performance in static systems and steady-state performance in discrete-event dynamic systems; they may be nonsmooth.
Journal ArticleDOI
Simulation-Based Optimization of Virtual Nesting Controls for Network Revenue Management
TL;DR: A continuous model of the virtual nesting problem is analyzed that retains most of the desirable features of the Bertsimas-de Boer method, yet avoids many of its pitfalls, and is able to prove that stochastic gradient methods are at least locally convergent.
Journal ArticleDOI
Convergence properties of backpropagation for neural nets via theory of stochastic gradient methods. Part 1
TL;DR: Convergence properties of serial and parallel backpropagation algorithm for training of neural nets, as well as its modification with momentum term are studied, showing that they can be put into the general framework of the stochastic gradient methods.
References
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Performance Analysis Using Stochastic Petri Nets
TL;DR: An isomorphism between the behavior of Petri nets with exponentially distributed transition rates and Markov processes is presented and this work solves for the steady state average message delay and throughput on a communication link when the alternating bit protocol is used for error recovery.