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- W2157130268 abstract "In In this paper, we introduce two convergent Monte Carlo algorithms for optimizing complex stochastic systems. The first algorithm, which is applicable to to regenerative processes, operates by estimating finite differences. The second method is of Robbins-Monro type and is applicable to to Markov chains. The algorithm is driven by derivative estimates obtained via a likelihood ratio argument." @default.
- W2157130268 created "2016-06-24" @default.
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- W2157130268 date "2007-01-01" @default.
- W2157130268 modified "2023-09-24" @default.
- W2157130268 title "LANDMARK PAPER REPRISE - STOCHASTIC APPROXIMATION FOR MONTE CARLO OPTIMIZATION" @default.
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