62 citations · 315 across the 65 of their papers we have counts for
5 papers · 1 filter
A simple preprocessing algorithm for semidefinite programming
Preston Faulk, Gabor Pataki, Quoc Tran-Dinh
We propose a very simple preprocessing algorithm for semidefinite programming. Our algorithm inspects the constraints of the problem, deletes redundant rows and columns in the cons…
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization
Quoc Tran-Dinh
In this paper, we develop a variant of the well-known Gauss-Newton (GN) method to solve a class of nonconvex optimization problems involving low-rank matrix variables. As opposed t…
Convex block-sparse linear regression with expanders -- provably
Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad +3
Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization…
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…
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…