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

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  • UserJoel Tropp (Caltech)
  • ClockMonday 18 June 2018, 16:00-17:00
  • HouseMR12.

If you have a question about this talk, please contact Quentin Berthet.

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.

This talk is part of the Statistics series.

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