convex optimization

Additive Schwarz Methods for Convex Optimization - Theory and Applications

Jongho Park, Research Scientist, Applied Mathematics and Computational Sciences

Sep 19, 16:00 - 17:00

B5 L5 R5209

Additive Schwarz methods convex optimization

This talk is devoted to additive Schwarz methods for convex optimization. First, we propose an abstract framework for additive Schwarz methods for convex optimization. The framework's flexibility allows it to handle composite optimization problems and inexact local solvers. Moreover, it establishes a sharp convergence theory that agrees with the classical theory when addressing linear problems.

Grigorii Malinovskii

Ph.D. Student, Computer Science

machine learning convex optimization Federated learning

I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.

Aritra Dutta

Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

machine learning Deep learning convex optimization

Aritra Dutta is a Postdoctoral Research Fellow at the Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Panagiotis Kalnis in his research group. Prior to that, he was a Postdoctoral Research Fellow in the Optimization and Machine Learning group of Professor Peter Richtárik. Education and Early Career Aritra Dutta received his bachelor degree in Mathematics (Hons.), Minor in Physics and Statistics from Presidency College in Calcutta University. He obtained a master degree in Mathematics and Computing from Indian