Continuous-state simulated annealing algorithms: theory and application

Saul B. Gelfand; Peter C. Doerschuk; Mohamed Nahhas-Mohandes

doi:10.1117/12.130832

16 December 1992 Continuous-state simulated annealing algorithms: theory and application

Saul B. Gelfand, Peter C. Doerschuk, Mohamed Nahhas-Mohandes

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130832
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Saul B. Gelfand, Peter C. Doerschuk, and Mohamed Nahhas-Mohandes "Continuous-state simulated annealing algorithms: theory and application", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130832

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