Sketchy decisions

With Joel Tropp (Caltech)

Sketchy decisions: Low-rank convex matrix optimization with optimal storage

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 an algorithm that provably solves these problems using optimal storage. The algorithm produces high-quality solutions to large problem instances that, previously, were intractable.

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

Add to your calendar or Include in your list

How can mathematics help us to understand the behaviour of ants? Read more about the fanscinating work being carri… https://t.co/iCODvvxqE6 View on Twitter