Randomized Difference Two-Timescale Simultaneous Perturbation Stochastic Approximation Algorithms for Simulation Optimization of Hidden Markov Models | Zendy

Shalabh Bhatnagar | Zendy; Michael C. Fu | Zendy; Steven I. Marcus | Zendy; Shashank Bhatnagar | Zendy

Open Access

Randomized Difference Two-Timescale Simultaneous Perturbation Stochastic Approximation Algorithms for Simulation Optimization of Hidden Markov Models

Author(s) -

Shalabh Bhatnagar,

Michael C. Fu,

Steven I. Marcus,

Shashank Bhatnagar

Publication year - 2000

Publication title -

digital repository at the university of maryland (university of maryland college park)

Language(s) - English

Resource type - Reports

DOI - 10.21236/ada637176

Subject(s) - simultaneous perturbation stochastic approximation , convergence (economics) , perturbation (astronomy) , markov chain , queueing theory , finite difference , algorithm , mathematics , stochastic approximation , hidden markov model , markov process , computer science , finite difference method , mathematical optimization , stochastic process , physics , mathematical analysis , key (lock) , artificial intelligence , statistics , computer security , economics , economic growth , quantum mechanics

: We propose two finite difference two-timescale simultaneous perturbation stochastic approximation (SPSA) algorithms for simulation optimization of hidden Markov models. Stability and convergence of both the algorithms is proved. Numerical experiments on a queueing model with high dimensional parameter vectors demonstrate orders of magnitude faster convergence using these algorithms over related (N + 1)-Simulation finite difference analogues and another Two-Simulation finite difference algorithm that updates in cycles.

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