Global optimization and stochastic differential equations
Let \({\mathbb{R}}^ n\) be the n-dimensional real Euclidean space, \(x=(x_ 1,x_ 2,...,x_ n)^{T}\in {\mathbb{R}}^ n\), and let f: \({\mathbb{R}}^ n\to {\mathbb{R}}\) be a real-valued function. We consider the problem of finding the global minimizers of f. A new method to compute numerically the global minimizers by following the paths of a system of stochastic differential equations is proposed. This method is motivated by quantum mechanics. Some numerical experience on a set of test problems is presented. The method compares favorably with other existing methods for global optimization.
system of stochastic differential equations
scientific article; zbMATH DE number 3876493