Very fast simulated re-annealing

Abstract

An algorithm is developed to statistically find the best global fit of a nonlinear nonconvex cost-function over a D-dimensional space. It is argued that this algorithm permits an annealing schedule for "temperature" T decreasing exponentially in annealing-time k, T = T0 exp(-ck1/D). The introduction of re-annealing also permits adaptation to changing insensitivities in the multi-dimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T = T0/k, and much faster than Boltzmann annealing, where T = T0/1n k. Applications are being made to fit empirical data to Lagrangians representing nonlinear Gaussian-Markovian systems. © 1989.