An optimization algorithm driven by probabilistic simulation

1 December 1986

proceedings article
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 1703-1705
https://doi.org/10.1109/cdc.1986.267226

Abstract

In this short paper we present an algorithm for optimization problems in which the evaluation of the objective function and of its gradient requires Monte Carlo-type probabilistic simulation. The algorithm is based on the gradient method and the paper also presents its convergence analysis.

Keywords

ANALYTICAL MODELS
STOCHASTIC PROCESSES
OPTIMIZATION METHODS
CONVERGENCE
APPROXIMATION METHODS
NETWORK SERVERS
GRADIENT METHODS
ALGORITHM DESIGN AND ANALYSIS
SPACE STATIONS
COST FUNCTION

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