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math.OC2016
A single-phase, proximal path-following framework
Quoc Tran-Dinh, Anastasios Kyrillidis, Volkan Cevher
We propose a new proximal, path-following framework for a class of constrained convex problems. We consider settings where the nonlinear---and possibly non-smooth---objective part…
math.OC2016
Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval
Gergely Odor, Yen-Huan Li, Alp Yurtsever +4
We study a phase retrieval problem in the Poisson noise model. Motivated by the PhaseLift approach, we approximate the maximum-likelihood estimator by solving a convex program with…