Stochastic approximation for Monte Carlo optimization

1 January 1986

conference paper
Published by Association for Computing Machinery (ACM)

p. 356-365
https://doi.org/10.1145/318242.318459

Abstract

In this paper, we introduce two convergent Monte Carlo algorithms for optimizing complex stochastic systems. The first algorithm, which is applicable to regenerative processes, operates by estimating finite differences. The second method is of Robbins-Monro type and is applicable to Markov chains. The algorithm is driven by derivative estimates obtained via a likelihood ratio argument.

Keywords

MONTE CARLO OPTIMIZATION
DERIVATIVE ESTIMATE
STOCHASTIC APPROXIMATION
CONVERGENT MONTE CARLO ALGORITHM
ROBBINS-MONRO TYPE
COMPLEX STOCHASTIC SYSTEM
FINITE DIFFERENCE
MARKOV CHAIN
LIKELIHOOD RATIO ARGUMENT
LIKELIHOOD RATIO
VARIANCE REDUCTION
MONTE CARLO
MONTE CARLO ALGORITHM
SELECTION

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