Accession Number : AD0755382
Title : Versatile Methods for the Sequential Monte Carlo Optimization of Unconstrained Stochastic Systems.
Descriptive Note : Technical rept.,
Corporate Author : BROWN UNIV PROVIDENCE R I CENTER FOR DYNAMICAL SYSTEMS
Personal Author(s) : Kushner,Harold J. ; Gavin,T.
Report Date : NOV 1972
Pagination or Media Count : 59
Abstract : The report contains 2 papers. The first paper discusses a versatile family of Monte Carlo Methods for the sequential optimization of stochastic systems. The method selects a sequence of successive one-dimensional search directions, defines a (stochastic) search in each of the directions, where the data used for both the one-dimensional search and the direction determination are merely noise-corrupted observations on the system; In the second paper, Kesten had proposed a method for adjusting the coefficients of a scalar stochastic approximation process, and proved w.p.1. convergence. A family of multidimensional processes for function minimization are treated here. Each method consists of a sequence truncated one-dimensional procedures of the Kesten type. The methods seem to offer a number of advantages over the usual Kiefer-Wolfowitz procedures, and are more natural analogs of the schemes in common use in deterministic optimization theory. (Author)
Descriptors : (*ADAPTIVE CONTROL SYSTEMS, MATHEMATICAL MODELS), STOCHASTIC PROCESSES, MONTE CARLO METHOD, RANDOM VARIABLES, SEARCH THEORY, OPTIMIZATION, THEOREMS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE