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Proceedings Paper

Continuous-state simulated annealing algorithms: theory and application
Author(s): Saul B. Gelfand; Peter C. Doerschuk; Mohamed Nahhas-Mohandes
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Paper Abstract

Simulated annealing algorithms for optimization over continuous spaces come in two varieties: Markov chain algorithms and modified gradient algorithms. Unfortunately, there is a gap between the theory and the application of these algorithms: the convergence conditions cannot be practically implemented. In this paper we suggest a practical methodology for implementing the modified gradient annealing algorithms based on their relationship to the Markov chain algorithms.

Paper Details

Date Published: 16 December 1992
PDF: 11 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130832
Show Author Affiliations
Saul B. Gelfand, Purdue Univ. (United States)
Peter C. Doerschuk, Purdue Univ. (United States)
Mohamed Nahhas-Mohandes, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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