873 citations · 5.4k across the 120 of their papers we have counts for
17 papers · 1 filter
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity
Yibo Zeng, Fei Feng, Wotao Yin
In this paper, we propose AsyncQVI, an asynchronous-parallel Q-value iteration for discounted Markov decision processes whose transition and reward can only be sampled through a ge…
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…
Acceleration of Primal-Dual Methods by Preconditioning and Simple Subproblem Procedures
Yanli Liu, Yunbei Xu, Wotao Yin
Primal-Dual Hybrid Gradient (PDHG) and Alternating Direction Method of Multipliers (ADMM) are two widely-used first-order optimization methods. They reduce a difficult problem to s…
Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters
Huan Li, Cong Fang, Wotao Yin +1
In this paper, we study the communication and (sub)gradient computation costs in distributed optimization and give a sharp complexity analysis for the proposed distributed accelera…
Multilevel Optimal Transport: a Fast Approximation of Wasserstein-1 distances
Jialin Liu, Wotao Yin, Wuchen Li +1
We propose a fast algorithm for the calculation of the Wasserstein-1 distance, which is a particular type of optimal transport distance with homogeneous of degree one transport cos…
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…