Gradient flows

Derivative-Free Global Minimization: Relaxation, Monte Carlo and Sampling

Diogo Gomes, Professor, Applied Mathematics and Computational Science

Nov 27, 11:30 - 12:30

B9 L2 H2 H2

minimization Gradient flows Monte Carlo Monte Carlo Methodology

We develop a derivative-free global minimization algorithm that is based on a gradient flow of a relaxed functional. We combine relaxation ideas, Monte Carlo methods, and resampling techniques with advanced error estimates. Compared with well-established algorithms, the proposed algorithm has a high success rate in a broad class of functions, including convex, non-convex, and non-smooth functions, while keeping the number of evaluations of the objective function small.