2 citations · 4 across the 5 of their papers we have counts for
6 papers
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning
Yongfeng Li, Mingming Zhao, Weijie Chen +1
In this paper, we consider the linear programming (LP) formulation for deep reinforcement learning. The number of the constraints depends on the size of state and action spaces, wh…
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods
Minghan Yang, Dong Xu, Hongyu Chen +2
In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to i…
A Stochastic Trust-Region Framework for Policy Optimization
Mingming Zhao, Yongfeng Li, Zaiwen Wen
In this paper, we study a few challenging theoretical and numerical issues on the well known trust region policy optimization for deep reinforcement learning. The goal is to find a…
Low-rank Matrix Optimization Using Polynomial-filtered Subspace Extraction
Yongfeng Li, Haoyang Liu, Zaiwen Wen +1
In this paper, we study first-order methods on a large variety of low-rank matrix optimization problems, whose solutions only live in a low dimensional eigenspace. Traditional firs…
A Semi-smooth Newton Method for Solving Semidefinite Programs in Electronic Structure Calculations
Yongfeng Li, Zaiwen Wen, Chao Yang +1
The ground state energy of a many-electron system can be approximated by an variational approach in which the total energy of the system is minimized with respect to one and two-bo…
A Regularized Semi-Smooth Newton Method With Projection Steps for Composite Convex Programs
Xiantao Xiao, Yongfeng Li, Zaiwen Wen +1
The goal of this paper is to study approaches to bridge the gap between first-order and second-order type methods for composite convex programs. Our key observations are: i) Many w…