Poster
Global Optimality of Local Search for Low Rank Matrix Recovery
Srinadh Bhojanapalli · Behnam Neyshabur · Nati Srebro

Tue Dec 6th 06:00 -- 09:30 PM @ Area 5+6+7+8 #20 #None

We show that there are no spurious local minima in the non-convex factorized parametrization of low-rank matrix recovery from incoherent linear measurements. With noisy measurements we show all local minima are very close to a global optimum. Together with a curvature bound at saddle points, this yields a polynomial time global convergence guarantee for stochastic gradient descent {\em from random initialization}.