Mean field annealing: a formalism for constructing GNC-like algorithms

Author

Bilbro, Griff L. ; Snyder, Wesley E. ; Garnier, Stephen J. ; Gault, James W.

Author_Institution

Centre for Commun. & Signal Process., North Carolina State Univ., Raleigh, NC, USA

Volume

3

Issue

1

fYear

1992

fDate

1/1/1992 12:00:00 AM

Firstpage

131

Lastpage

138

Abstract

Optimization problems are approached using mean field annealing (MFA), which is a deterministic approximation, using mean field theory and based on Peierls´s inequality, to simulated annealing. The MFA mathematics are applied to three different objective function examples. In each case, MFA produces a minimization algorithm that is a type of graduated nonconvexity. When applied to the `weak-membrane´ objective, MFA results in an algorithm qualitatively identical to the published GNC algorithm. One of the examples, MFA applied to a piecewise-constant objective function, is then compared experimentally with the corresponding GNC weak-membrane algorithm. The mathematics of MFA are shown to provide a powerful and general tool for deriving optimization algorithms

Keywords

minimisation; simulated annealing; GNC-like algorithms; MFA; Peierls´s inequality; deterministic approximation; graduated nonconvexity; mean field annealing; mean field theory; minimization algorithm; objective function; piecewise-constant objective function; simulated annealing; Analytical models; Biomedical signal processing; Biomembranes; Image analysis; Image edge detection; Image restoration; Mathematics; Minimization methods; Signal processing algorithms; Simulated annealing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.105426

Filename

105426