Metropolis-type Annealing Algorithms for Global Optimization in IRd

Abstract

We establish the convergence of a class of Metropolis-type Markov chain annealing algorithms for global optimization of a smooth function U(.) on IRd. No prior information is assumed as to what bounded region contains a global minimum. Our analysis is based on writing the Metropolis-type algorithm in the form of a recursive stochastic algorithm, where [some entities] are independent standard Gaussian random variables, [and others] are (unbounded, correlated) random variables, and then applying results about [our findings].

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 1990
Accession Number
ADA459610

Entities

People

  • Sanjoy K. Mitter
  • Saul B. Gelfand

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Annealing
  • Convergence
  • Diffusion
  • Digital Computers
  • Electrical Engineering
  • Engineering
  • Gaussian Noise
  • Image Processing
  • Markov Chains
  • Numbers
  • Optimization
  • Probability
  • Random Variables
  • Sampling
  • Sequences
  • Standards

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Traumatic Brain Injury (TBI) and Cognitive Aging in the Guam and Border Populations Affected by Alzheimer's Disease and Tau-Associated Dementias.