most citedHeavy-ball Algorithms Always Escape Saddle Points

2 citations · 5 across the 3 of their papers we have counts for

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math.OC2019

Inertial nonconvex alternating minimizations for the image deblurring

Tao Sun, Roberto Barrio, Marcos Rodriguez +1

In image processing, Total Variation (TV) regularization models are commonly used to recover blurred images. One of the most efficient and popular methods to solve the convex TV pr…

math.OC20192 cited

Heavy-ball Algorithms Always Escape Saddle Points

Tao Sun, Dongsheng Li, Zhe Quan +3

Nonconvex optimization algorithms with random initialization have attracted increasing attention recently. It has been showed that many first-order methods always avoid saddle poin…

math.OC20192 cited

Bregman Proximal Gradient Algorithm with Extrapolation for a class of Nonconvex Nonsmooth Minimization Problems

Xiaoya Zhang, Roberto Barrio, M. Angeles Martinez +2

In this paper, we consider an accelerated method for solving nonconvex and nonsmooth minimization problems. We propose a Bregman Proximal Gradient algorithm with extrapolation(BPGe…

math.OC20191 cited

Iteratively reweighted penalty alternating minimization methods with continuation for image deblurring

Tao Sun, Dongsheng Li, Hao Jiang +1

In this paper, we consider a class of nonconvex problems with linear constraints appearing frequently in the area of image processing. We solve this problem by the penalty method a…

math.OC2018

Non-ergodic Convergence Analysis of Heavy-Ball Algorithms

Tao Sun, Penghang Yin, Dongsheng Li +3

In this paper, we revisit the convergence of the Heavy-ball method, and present improved convergence complexity results in the convex setting. We provide the first non-ergodic O(1/…