42 citations · 182 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
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/…
On Markov Chain Gradient Descent
Tao Sun, Yuejiao Sun, Wotao Yin
Stochastic gradient methods are the workhorse (algorithms) of large-scale optimization problems in machine learning, signal processing, and other computational sciences and enginee…
An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines
Lei Guan, Linbo Qiao, Dongsheng Li +3
Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable sel…
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Tianyi Chen, Georgios B. Giannakis, Tao Sun +1
This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computat…
Non-ergodic Complexity of Convex Proximal Inertial Gradient Descents
Tao Sun, Linbo Qiao, Dongsheng Li
The proximal inertial gradient descent is efficient for the composite minimization and applicable for broad of machine learning problems. In this paper, we revisit the computationa…