873 citations · 5.4k across the 120 of their papers we have counts for
10 papers · 2 filters
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…
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…
Breaking the Span Assumption Yields Fast Finite-Sum Minimization
Robert Hannah, Yanli Liu, Daniel O'Connor +1
In this paper, we show that SVRG and SARAH can be modified to be fundamentally faster than all of the other standard algorithms that minimize the sum of smooth functions, such…