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Proceedings Paper

Fast combinatorial optimization using generalized deterministic annealing
Author(s): Scott Thomas Acton; Joydeep Ghosh; Alan Conrad Bovik
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Paper Abstract

Generalized Deterministic Annealing (GDA) is a useful new tool for computing fast multi-state combinatorial optimization of difficult non-convex problems. By estimating the stationary distribution of simulated annealing (SA), GDA yields equivalent solutions to practical SA algorithms while providing a significant speed improvement. Using the standard GDA, the computational time of SA may be reduced by an order of magnitude, and, with a new implementation improvement, Windowed GDA, the time improvements reach two orders of magnitude with a trivial compromise in solution quality. The fast optimization of GDA has enabled expeditious computation of complex nonlinear image enhancement paradigms, such as the Piecewise Constant (PICO) regression examples used in this paper. To validate our analytical results, we apply GDA to the PICO regression problem and compare the results to other optimization methods. Several full image examples are provided that show successful PICO image enhancement using GDA in the presence of both Laplacian and Gaussian additive noise.

Paper Details

Date Published: 19 August 1993
PDF: 12 pages
Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152639
Show Author Affiliations
Scott Thomas Acton, Univ. of Texas/Austin (United States)
Joydeep Ghosh, Univ. of Texas/Austin (United States)
Alan Conrad Bovik, Univ. of Texas/Austin (United States)

Published in SPIE Proceedings Vol. 1966:
Science of Artificial Neural Networks II
Dennis W. Ruck, Editor(s)

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