General Convergence Results for Stochastic Approximations Via Weak Convergence Theory,

Abstract

Using results in the theory of weak convergence of measures and in stability theory for ordinary differential equations, we prove some general convergence theorems for the sequences of random variables which are generated by algorithms of the stochastic approximation type. Such algorithms are used when one wishes to locate, via a recursive Monte-Carlo method, a minimum of a function, under handicap of noisy data. Algorithms for both constrained and unconstrained optimization problems will be considered, and for rather general noise processes. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1976
Accession Number
ADA021159

Entities

People

  • Harold J. Kushner

Organizations

  • Brown University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Convergence
  • Differential Equations
  • Equations
  • Heuristic Methods
  • Mathematics
  • Monte Carlo Method
  • Optimization
  • Random Variables
  • Sequences
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Operations Research