On Random Search with a Learning Memory,

Abstract

A new class of random search algorithms for stochastic optimization is presented. The designer has the option to employ a learning memory in order to reduce the cost of the optimization process measured in terms of the number of observations. The asymptotical properties of the procedure are discussed, and new probability theoretical techniques are used in the proof of convergence. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1976
Accession Number
ADA038333

Entities

People

  • L. P. Devroye

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Automata Theory
  • Computations
  • Control Systems
  • Control Systems Engineering
  • Convergence
  • Cybernetics
  • Distribution Functions
  • Electrical Engineering
  • Engineering
  • Environment
  • Gaussian Distributions
  • Learning
  • Pattern Recognition
  • Probability
  • Random Variables

Readers

  • Artificial Intelligence
  • Operations Research
  • Regression Analysis.