SPEAKER: Prof. Guanghui Lan, University of Florida

TIME: 10:00-11:00am, 2014-1-2

VENUE:Room 09 at Quan Zhai, BICMR

ABSTRACT: This talk focuses on the design and analysis of efficient algorithms to tackle the big-data challenges in optimization. The last several years have seen an unprecedented growth in the amount of available data. While nonlinear, especially convex programming models are important to extract useful knowledge from raw data, high problem dimensionality, large data volumes, and inherent uncertainty present significant challenges to the design of optimization algorithms. Aiming to attack some of these challenges, we introduce: i) a new class of stochastic approximation algorithms that can yield the optimal rate of convergence for solving different stochastic optimization problems. Some of these optimal rates were obtained for the first time in the literature; and ii) a new class of deterministic first-order algorithms that can converge optimally, require no structural information and do not rely on line search, based on level methods. To the best of our knowledge, no such uniformly optimal first-order methods have been studied before in the literature. Applications of these stochastic/deterministic algorithms will be studied. We will also briefly discuss some other related work and possible future research directions.

BIO: Guanghui (George) Lan obtained his Ph.D. degree in Operations Research from Georgia Institute of Technology in 2009. He joined the Department of Industrial and Systems Engineering at the University of Florida as an assistant professor thereafter. His main research interests lie in the theory and algorithms for stochastic optimization, nonlinear programming, simulation-based optimization, and their applications in various fields, such as large-scale data analysis. His research has been supported by the National Science Foundation and Office of Naval Research. The academic honors that he received include the INFORMS Computing Society Student Paper Competition First Place (2008), INFORMS George Nicholson Paper Competition Second Place (2008), Mathematical Optimization Society Tucker Prize Finalist (2012), INFORMS Junior Faculty Interest Group (JFIG) Paper Competition First Place (2012) and the recent National Science Foundation CAREER Award Winner (2013).