title: Mean field annealing: a formalism for constructing GNC-like algorithms.
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.
authors.affiliation: Centre for Commun. and Signal Process., North Carolina State Univ., Raleigh, NC.
authors.forename: G L
authors.initials: GL
authors.lastname: Bilbro
authors.forename: W E
authors.initials: WE
authors.lastname: Snyder
authors.forename: S J
authors.initials: SJ
authors.lastname: Garnier
authors.forename: J W
authors.initials: JW
authors.lastname: Gault