Stochastic Approximation Type Algorithms for the Optimization of Constrained and Multinode Stochastic Problems.

Abstract

The aim of the paper is the development of a structure for stochastic optimization algorithms (of the Monte-Carlo or stochastic approximation type) which is analogous to that used in non-linear programming. The developed structure is quite versatile, and seems to consider the elements of the problem in a very natural manner from both the theoretical and practical viewpoints. A second paper is also included in the report, titled: Stochastic Approximation Algorithms for the Local Optimization of Functions With Non-Unique Stationary Points.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1972
Accession Number
AD0736958

Entities

People

  • Harold J. Kushner

Organizations

  • Brown University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Evolutionary Algorithms
  • Heuristic Methods
  • Linear Programming
  • Mathematical Programming
  • Mathematics
  • Optimization
  • Simplex Method
  • Stationary

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.