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math.OC2022
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…
math.OC2019★ 20 cited
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…
math.OC2018★ 2 cited
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…