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.

• Speaker: Joel Tropp (Caltech)
• Monday 18 June 2018, 16:00–17:00
• Venue: MR12.
• Series: Statistics; organiser: Quentin Berthet.