Penalty Function Methods for Constrained Stochastic Approximation,

Abstract

The paper is concerned with sequential Monte Carlo methods for optimizing a system under constraints. The authors wish to minimize f(x) where (q sub i) (x) 2 or = O, i = 1,...,m, must hold. The (q sub i) (x) can be calculated, but f(x) can only be observed in the presence of noise. A general approach, based on an adaptation of a version of stochastic approximation to the penalty function method, is discussed, and a convergence theorem proved. (Author Modified Abstract)

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1973
Accession Number
AD0759295

Entities

People

  • Emilio G. Sanvicente
  • Harold J. Kushner

Organizations

  • Brown University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Convergence
  • Data Science
  • Information Science
  • Mathematics
  • Monte Carlo Method
  • Sequential Monte Carlo Methods
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Computational Modeling and Simulation