Seminar: Second Order Sparsity and Big Data Optimization


                   Department of Systems Engineering and Engineering Management

                                The Chinese University of Hong Kong


Date: Friday, 4:30pm - 5:30pm, October 6, 2017

Title: Second Order Sparsity and Big Data Optimization

Speaker: Sun Defeng, Department of Applied Mathematics, Hong Kong Polytechnic University

Abstract: Concerned with inherent huge computational burdens of the interior point methods for solving optimization problems of large scales, many researchers and practitioners tend to the first order methods such as the accelerated proximal gradient methods and the alternating direction methods of multipliers for the rescue. While these first order methods have indeed enjoyed very successful stories for some interesting applications, they also encounter enormous numerical difficulties in dealing with many real data optimization problems of big scales even only with a low or moderate solution quality. New ideas for solving these problems are highly sought both in practice and academic research. In this talk, I will explain how the second order sparsity (SOS) property exhibited in big composite optimization models can be intelligently exploited to design efficient and scalable algorithms to overcome the mentioned numerical difficulties either in IPMs or in the first order methods.  A highly efficient software called LassoNAL (freely available for academic research), based on a semismooth Newton augmented Lagrangian method with sub-problems solved at costs comparable or even lower than those from many first order methods, for solving the well-known Lasso problem will be used to demonstrate how the key SOS property makes the solving of big data optimization models realistic even in high precision.


Professor Sun Defeng is currently Chair Professor of Applied Optimization and Operations Research at the Hong Kong Polytechnic University.   Before moving to Hong Kong in August 2017, Professor Sun served as  Professor at Department of Mathematics, National University of Singapore, Deputy Director (Research) at the NUS Risk Management Institute and  Program Director for its Master of Financial Engineering program.

Professor Sun mainly publishes in the field of optimization.  He has written a number of software for solving large-scale complex optimization problems, including the codes for correlation matrix calibrations that are widely used in the financial industry. His most notable recent achievement is his building up of the new field of matrix optimization. Currently Professor Sun focuses on establishing the foundation for the next generation methodologies for big data optimization and applications.

Professor Sun has actively involved in many professional activities. He served as editor-in-chief of  Asia-Pacific Journal of Operational Research from 2011 to 2013 and he now serves as associate editor of Mathematical Programming (both Series A and Series B), SIAM Journal on Optimization,  Journal of the Operations Research Society of China, and Journal of Computational Mathematics.

Everyone is welcome to attend the talk!

Venue: Room 513,William M.W. Mong Engineering Building

(ERB),(Engineering Building Complex Phase 2) The Chinese University of

Hong Kong.

The talk will be hosted by:

Prof. Chen Nan,

Department of Systems Engineering and Engineering Management,

The Chinese University of Hong Kong,



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Friday, October 6, 2017 - 16:30 to 17:30