2 citations · 6 across the 18 of their papers we have counts for
20 papers
An Inexact Halpern-accelerated Preconditioned Generalized Proximal Point Algorithm for the Maximal Monotone Inclusion Problem
Lei Yang, Haihang Lan, Di Hou +2
This paper studies an inexact Halpern-accelerated generalized proximal point algorithm with an admissible positive semidefinite preconditioner for solving maximal mon…
D-ripALM: A Tuning-friendly Decentralized Relative-Type Inexact Proximal Augmented Lagrangian Method
Jiayi Zhu, Hong Wang, Ling Liang +1
This paper proposes D-ripALM, a Decentralized relative-type inexact proximal Augmented Lagrangian Method for consensus convex optimization over multi-agent networks. D-ripALM adopt…
Convergence Analysis of a Relative-type Inexact Preconditioned Proximal ALM for Convex Nonlinear Programming
Lei Yang, Jiayi Zhu, Ling Liang +1
This article investigates the convergence properties of a relative-type inexact preconditioned proximal augmented Lagrangian method (ripALM) for convex nonlinear programming, a…
Group zero-norm regularized robust loss minimization: proximal MM method and statistical error bound
Ling Liang, Shujun Bi
This study focuses on solving group zero-norm regularized robust loss minimization problems. We propose a proximal Majorization-Minimization (PMM) algorithm to address a class of e…
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…
A Sparse Smoothing Newton Method for Solving Discrete Optimal Transport Problems
Di Hou, Ling Liang, Kim-Chuan Toh
The discrete optimal transport (OT) problem, which offers an effective computational tool for comparing two discrete probability distributions, has recently attracted much attentio…
A Corrected Inexact Proximal Augmented Lagrangian Method with a Relative Error Criterion for a Class of Group-quadratic Regularized Optimal Transport Problems
Lei Yang, Ling Liang, Hong T. M. Chu +1
The optimal transport (OT) problem and its related problems have attracted significant attention and have been extensively studied in various applications. In this paper, we focus…
A squared smoothing Newton method for semidefinite programming
Ling Liang, Defeng Sun, Kim-Chuan Toh
This paper proposes a squared smoothing Newton method via the Huber smoothing function for solving semidefinite programming problems (SDPs). We first study the fundamental properti…
Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition
Ching-pei Lee, Ling Liang, Tianyun Tang +1
This work proposes a rapid algorithm, BM-Global, for nuclear-norm-regularized convex and low-rank matrix optimization problems. BM-Global efficiently decreases the objective value…
An Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimization
Heng Yang, Ling Liang, Luca Carlone +1
We consider solving high-order semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solution…
QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems
Ling Liang, Xudong Li, Defeng Sun +1
In this paper, we aim to solve high dimensional convex quadratic programming (QP) problems with a large number of quadratic terms, linear equality and inequality constraints. In or…
An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems
Hong T. M. Chu, Ling Liang, Kim-Chuan Toh +1
We introduce a class of specially structured linear programming (LP) problems, which has favorable modeling capability for important application problems in different areas such as…
An Inexact Augmented Lagrangian Method for Second-order Cone Programming with Applications
Ling Liang, Defeng Sun, Kim-Chuan Toh
In this paper, we adopt the augmented Lagrangian method (ALM) to solve convex quadratic second-order cone programming problems (SOCPs). Fruitful results on the efficiency of the AL…
On Degenerate Doubly Nonnegative Projection Problems
Ying Cui, Ling Liang, Defeng Sun +1
The doubly nonnegative (DNN) cone, being the set of all positive semidefinite matrices whose elements are nonnegative, is a popular approximation of the computationally intractable…
A New Homotopy Proximal Variable-Metric Framework for Composite Convex Minimization
Quoc Tran-Dinh, Liang Ling, Kim-Chuan Toh
This paper suggests two novel ideas to develop new proximal variable-metric methods for solving a class of composite convex optimization problems. The first idea is a new parameter…