2 citations · 6 across the 18 of their papers we have counts for
6 papers · 1 filter
ripALM: A Relative-Type Inexact Proximal Augmented Lagrangian Method for Linearly Constrained Convex Optimization
Jiayi Zhu, Ling Liang, Lei Yang +1
Inexact proximal augmented Lagrangian methods (ipALMs) have been widely used for solving linearly constrained convex optimization problems, owing to their strong theoretical guaran…
NewVEM: A Newton Vertex Exchange Method for a Class of Constrained Self-Concordant Minimization Problems
Ling Liang, Kim-Chuan Toh, Haizhao Yang
We propose \textbf{NewVEM}, a Newton vertex exchange method for efficiently solving self-concordant minimization problems under generalized simplex constraints. The algorithm featu…
Nesterov's Accelerated Jacobi-Type Methods for Large-scale Symmetric Positive Semidefinite Linear Systems
Ling Liang, Qiyuan Pang, Kim-Chuan Toh +1
Solving symmetric positive semidefinite linear systems is an essential task in many scientific computing problems. While Jacobi-type methods, including the classical Jacobi method…
Fast and Certifiable Trajectory Optimization
Shucheng Kang, Xiaoyang Xu, Jay Sarva +2
We propose semidefinite trajectory optimization (STROM), a framework that computes fast and certifiably optimal solutions for nonconvex trajectory optimization problems defined by…
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization
Ling Liang, Zusen Xu, Kim-Chuan Toh +1
The Halpern iteration for solving monotone inclusion problems has gained increasing interests in recent years due to its simple form and appealing convergence properties. In this p…
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
Ling Liang, Haizhao Yang
We consider a regularized expected reward optimization problem in the non-oblivious setting that covers many existing problems in reinforcement learning (RL). In order to solve suc…