ISE seminar 2-18

INDUSTRIAL AND SYSTEMS ENGINEERING DEPARTMENT
SPRING 2011 SEMINAR SERIES
 
TITLE:                       Some Practical Methods for Computing Sparse or Low-Rank Solutions
 
SPEAKER:                 Dr. Zhaosong Lu, Assistant Professor of Mathematics, Simon Fraser University
                                  And associate faculty member in Statistics and Actuarial Science.
 
DATE / TIME:           Friday, February 18, 2011
                                  2:30 – 3:30 p.m.
 
LOCATION:             Room 453 Mohler Lab, 200 W. Packer Avenue
 
ABSTRACT:  Nowadays there are numerous emerging applications in science and engineering concerning about sparse or low-rank solution such as compressed sensing, image recovery and dimension reduction.  In this talk, we propose some practical methods for computing sparse or low-rank solutions.  In particular, we study a novel first-order augmented Lagrangian (AL) method for solving a class of nonsmooth constrained optimization problems which include l₁-norm and nuclear-norm regularized problems as special cases.  The global convergence of this method is established.  We also develop two first-order methods for solving the associated AL subproblems and establish their global and local convergence.  In addition, we propose penalty decomposition methods for solving l₀-norm and rank minimization problems.  Under some suitable assumptions, we show that each accumulation point is a stationary point of an equivalent smooth optimization problem.  Finally, we demonstrate the computational performance of these methods by applying them to sparse principal component analysis and sparse logistic regression.
 
BIOGRAPHY:  Zhaosong Lu is an Assistant Professor of Mathematics at Simon Fraser University and an associate faculty member in Statistics and Actuarial Science.  He received his PhD in Operations Research from Georgia Institute of Technology in 2005.  He was a Zeev Nehari Visiting Assistant Professor of Mathematical Sciences at Carnegie Mellon University during 2005-2006.  Dr. Lu's research interests include theory and algorithms for continuous optimization, and applications in data mining, finance, statistics, machine learning, image processing, engineering design, and decision-making under uncertainty.  He was a finalist of 2005 INFORMS George Nicholson Best Student Paper Competition for the work on first-order methods for solving large-scale well-structured semi definite programming.  He has published papers in major journals of his research areas such as Mathematical Programming, SIAM Journal on Optimization, INFORMS Journal on Computing, SIAM Journal on Matrix Analysis and Applications, Optimization Methods and Software, Journal of the Royal Statistical Society, and ASME Journal of Mechanical Design.
 
 
ALL FULL-TIME ISE PHD STUDENTS ARE REQUIRED TO ATTEND AND
 
ALL ISE GRADUATE STUDENTS ARE ENCOURAGED TO ATTEND
REFRESHMENTS WILL BE SERVED FOLLOWING THE SEMINAR