Very Fast Simulated Re-Annealing
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An algorithm is developed to statistically find the best global fit of a nonlinear non-convex 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 sensitivities in the multidimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T = T0/k, and much faster than Boltzmann annealing, where T = T0/ ln k. Applications are being made to fit empirical data to Lagrangians representing nonlinear Gaussian-Markovian systems.
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