When: January 25, 2017, 12:30 PM
Location: 3rd Floor Orchard View Room , Discovery Building
Contact: 608-316-4401, email@example.com
Gradient Descent For Matrix Completion
From recommender systems to healthcare analytics low-rank recovery from partial observations is prevalent in modern data analysis. There has been significant progress over the last decade in providing rigorous guarantees for low-rank recovery problems based on convex relaxation techniques. However, the computational complexity of these algorithms render them impractical for large-scale applications. Recent advances in nonconvex optimization explain the surprising effectiveness of simple first-order algorithms for many low rank matrix recovery problems, especially for positive semidefinite matrices. The common theme of these algorithms is to work directly with the low rank factors of the semidefinite variable. In this talk, I will discuss how similar ideas can be applied to rectangular matrix completion. We provide rigorous convergence guarantee to show such simple algorithms are effective and can overcome the scalability limits faced by popular convex relaxation approach.
SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group and sponsored by generous support of the Advance Technology Group of the 3M Company and the Analytics Group of Northwestern Mutual.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.