Gradient flows

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

Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

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.

Simone Portaro

Ph.D. Student, Applied Mathematics and Computational Sciences

Nonlinear Partial Differential Equation biological network and complex systems Analysis of PDE's Mathematical modeling Gradient flows Measure theory

Simone Portaro is a Ph.D. Student in the Computer, Electrical, Mathematical Sciences and Engineering (CEMSE) department at the Applied Mathematics and Computational Sciences (AMCS) program under the supervision of Professor Peter Markowich at King Abdullah University of Science and Technology. Simone received his B.S. degree in Industrial Engineering from the University of Catania, Italy in 2017. In 2020, he received his M.S. degree in Mathematical Engineering from Politecnico di Torino, Italy. Research interests Nonlinear Partial Differential Equations, Analysis-Modeling of biological network