Speaker: Dr.Yangyang Xu（Rensselaer Polytechnic Institute）

Title: first-order methods for convex programs with functional constraints

Time: Thus, May.9, 2019, PM:17:00-18:00

Location: Room 4318, Building No.4, Wushan Campus

Abstract:

        first-order methods use gradient and/or function value information. They are generally much cheaper than second or higher-order methods. Recently, first-order methods have been popularly used in applications such as machine learning and image processing. These applications are often unconstrained or only have affinely constraints. In this talk, I will present first-order methods on solving problems with nonlinear functional constraints. In the first part, I will give a deterministic first-order method that is based on linearizing the classic augmented Lagrangian function, and in the second part, I will give its two stochastic versions. Theoretical convergence rate and also numerical results will be presented.

Biography:

        Yangyang Xu is now a tenure-track assistant professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. He received his B.S. in Computational Mathematics from Nanjing University in 2007, M.S. in Operations Research from Chinese Academy of Sciences in 2010, and Ph.D from the Department of Computational and Applied Mathematics at Rice University in 2014. His research interests are optimization theory and methods and their applications such as in machine learning, statistics, and signal processing. He developed optimization algorithms for compressed sensing, matrix completion, and tensor factorization and learning. Recently, his research focuses on first-order methods, operator splitting, stochastic optimization methods, and high performance parallel computing. He has published over 30 papers in prestigious journals and conference proceedings. His work on block coordinate descent method for multi convex optimization has won the gold medal award in 2017 International Consortium of Chinese Mathematicians.