Accession Number:

ADA459610

Title:

Metropolis-type Annealing Algorithms for Global Optimization in IRd

Descriptive Note:

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS

Personal Author(s):

• Gelfand, Saul B.
• Mitter, Sanjoy K.

Report Date:

1990-05-01

Pagination or Media Count:

31.0

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.

Descriptors:

• *ALGORITHMS
• *ANNEALING
• *GLOBAL
• *OPTIMIZATION
• STOCHASTIC PROCESSES
• MARKOV PROCESSES
• GRADIENTS
• THEOREMS

Subject Categories:

• Theoretical Mathematics
• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE