Monte Carlo Methodology

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

Diogo Gomes, Associate Dean for Students, Computer, Electrical and Mathematical Sciences and Engineering

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.

Fangyuan Yu

Ph.D. Student, Statistics

computational probability Monte Carlo Methodology

Fangyuan Yu is a Ph.D. student in the Statistics program at KAUST, his supervisor is Professor Ajay Jasra. Education and Early Career Fangyuan holds a master's degree in statistics from the National University of Singapore (NUS). He also holds a master's degree and a bachelor's degree in mathematics and applied mathematics at Shandong University, China. Before joining KAUST, Fangyuan worked as a research assistant in the Department of Statistics and Applied Probability, National University of Singapore from August 2018 to July 2019. His principal investigator was Professor Ajay Jasra. Research