19 August 1993 Fast combinatorial optimization using generalized deterministic annealing
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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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Scott Thomas Acton, Scott Thomas Acton, Joydeep Ghosh, Joydeep Ghosh, Alan Conrad Bovik, Alan Conrad Bovik, } "Fast combinatorial optimization using generalized deterministic annealing", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152639; https://doi.org/10.1117/12.152639

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