Time: 11:00am to 12:30pm
Venue: Room 7-208, 7/F, Lau Ming Wai Academic Building
Minimization with respect to a matrix X subject to orthogonality constraints X'X = I has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, the total energy minimization in electronic structure calculation, sparse principal component analysis, community detection and matrix rank minimization, etc. These problems are generally difficult because the constraints are not only non-convex but also numerically expensive to preserve during iterations. This talk will present a few recent advance for solving these problems.
Zaiwen Wen. Peking University. He holds a Ph.D in Operations Research from Columbia University (2009). His research interests include large-scale computational optimization and their applications in data sciences. He has published papers on journals including SIAM J. on Optimization, SIAM J. on Scientific Computing, SIAM J. on Imaging Sciences, SIAM Journal on Numerical Analysis, SIAM Journal on Matrix Analysis and Applications, Mathematical Programming, etc. He was awarded the INFORMS Computing Society Student Paper Award in 2009, the National Science Fund for Excellent Young Scholars in 2013, the Young top-notch talent in 2015 and the Science and Technology Award for Chinese Youth in 2016. More detailed information is available at: http://bicmr.pku.edu.cn/~wenzw