23 citations · 33 across the 5 of their papers we have counts for
8 papers · 1 filter
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Tao Sun, Yuejiao Sun, Dongsheng Li +1
The incremental aggregated gradient algorithm is popular in network optimization and machine learning research. However, the current convergence results require the objective funct…
Decentralized Markov Chain Gradient Descent
Tao Sun, Dongsheng Li
Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradie…
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…
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…
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…
Markov Chain Block Coordinate Descent
Tao Sun, Yuejiao Sun, Yangyang Xu +1
The method of block coordinate gradient descent (BCD) has been a powerful method for large-scale optimization. This paper considers the BCD method that successively updates a serie…