[Ml-stat-talks] IDeAS Seminar today: Sketchy decisions: Low-rank matrix optimization with optimal storage

Barbara Engelhardt bee at princeton.edu
Wed Apr 26 08:44:20 EDT 2017


IDeAS Seminar: Sketchy decisions: Low-rank matrix optimization with optimal
storage <http://www.pacm.princeton.edu/node/728>
Speaker:
Joel Tropp, Caltech




Date:
Apr 26 2017 - 2:30pm

Room:
224 Fine Hall

Abstract:

Convex matrix optimization problems with low-rank solutions play a
fundamental role in signal processing, statistics, and related disciplines.
These problems are difficult to solve because of the cost of maintaining
the matrix decision variable, even though the low-rank solution has few
degrees of freedom. This talk presents the first algorithm that provably
solves these problems using optimal storage. The algorithm produces
high-quality solutions to large problem instances that, previously, were
intractable.

Joint with Volkan Cevher, Roarke Horstmeyer, Quoc Tran-Dinh, Madeleine
Udell, and Alp Yurtsever.

*Joel A. Tropp is Professor of Applied & Computational Mathematics at the
California Institute of Technology. He earned the Ph.D. degree in
Computational Applied Mathematics from the University of Texas at Austin in
2004. His research centers on signal processing, numerical analysis, and
random matrix theory. Prof. Tropp won the 2008 Presidential Early Career
Award for Scientists and Engineers. He received society best paper awards
from SIAM in 2010, EUSIPCO in 2011, and IMA in 2015. He was also recognized
as a Thomson Reuters Highly Cited Researcher in Computer Science in 2014,
2015, and 2016.*
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