128 citations · 327 across the 14 of their papers we have counts for
15 papers
Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization
Shuang Li, Zhihui Zhu, Qiuwei Li
We analyze Bregman ADMM for nonconvex linearly constrained problems under two-sided relative smoothness, a condition that replaces the standard Lipschitz gradient assumption with a…
Local and Global Convergence of General Burer-Monteiro Tensor Optimizations
Shuang Li, Qiuwei Li
Tensor optimization is crucial to massive machine learning and signal processing tasks. In this paper, we consider tensor optimization with a convex and well-conditioned objective…
The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery without Regularization
Shuang Li, Qiuwei Li, Zhihui Zhu +2
Low-rank matrix recovery is a fundamental problem in signal processing and machine learning. A recent very popular approach to recovering a low-rank matrix X is to factorize it as…
Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems
Qiuwei Li, Zhihui Zhu, Gongguo Tang +1
The (global) Lipschitz smoothness condition is crucial in establishing the convergence theory for most optimization methods. Unfortunately, most machine learning and signal process…
Spherical Principal Component Analysis
Kai Liu, Qiuwei Li, Hua Wang +1
Principal Component Analysis (PCA) is one of the most important methods to handle high dimensional data. However, most of the studies on PCA aim to minimize the loss after projecti…
Global Optimality in Distributed Low-rank Matrix Factorization
Zhihui Zhu, Qiuwei Li, Xinshuo Yang +2
We study the convergence of a variant of distributed gradient descent (DGD) on a distributed low-rank matrix approximation problem wherein some optimization variables are used for…