Characterization of the convergence of stationary Fokker-Planck learning

TL;DR

This paper investigates the convergence behavior of the stationary Fokker-Planck algorithm used for estimating the long-term density in stochastic search processes, providing theoretical and empirical insights.

Contribution

It offers a novel analysis of convergence properties for stationary Fokker-Planck learning in nonlinear optimization and neural network parameter inference.

Findings

Convergence characterization for separable and nonseparable problems

Theoretical and empirical validation of convergence properties

Implications for neural network parameter inference

Abstract

The convergence properties of the stationary Fokker-Planck algorithm for the estimation of the asymptotic density of stochastic search processes is studied. Theoretical and empirical arguments for the characterization of convergence of the estimation in the case of separable and nonseparable nonlinear optimization problems are given. Some implications of the convergence of stationary Fokker-Planck learning for the inference of parameters in artificial neural network models are outlined.