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math.OC2019
Linear Convergence of Frank-Wolfe for Rank-One Matrix Recovery Without Strong Convexity
Dan Garber
We consider convex optimization problems which are widely used as convex relaxations for low-rank matrix recovery problems. In particular, in several important problems, such as ph…
math.OC2019★ 5 cited
On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems
Dan Garber
Smooth convex minimization over the unit trace-norm ball is an important optimization problem in machine learning, signal processing, statistics and other fields, that underlies ma…