Stochastic approximation procedure with uniform initial conditions

Abstract Stochastic approximation with uniform initial condition distribution is a prevalent approach for optimizing functions lacking analytical solutions. This iterative method involves generating approximate minima from random samples within the solution domain. Despite its simplicity and broad applications, it demands numerous iterations for satisfactory outcomes, potentially leading to computational costs. This article focuses on discussing and applying stochastic approximation processes with uniform initial condition distribution. The article aims to clarify the theory behind stochastic approximation with uniform initial condition distribution and employ diffusion process modeling to Rosa-Chabanyuka models involving two system factor modifications.