教师主页移动版

基本信息 陈亮，男，理学博士学位，博士研究生导师，chl@hnu.edu.cn

教育背景

研究方向 数学/数值优化，线性与非线性规划，凸优化，矩阵优化，锥约束优化，运筹学 Mathematical/Computational Optimization, especially Linear and Nonlinear Programming, Convex Programming, Matrix Optimization, Statistical Optimization, Operations Research

工作履历

学习经历 2005-09-01—2009-06-18，湖南大学，数学与计量经济学院，大学毕业，理学学士 2009-09-01—2016-12-27，湖南大学，数学与计量经济学院，硕博连读，理学博士 2013-08-01—2015-07-31，新加坡国立大学，数学系，国家公派联合培养博士研究生

研究领域

工作经历 2017-02-01—2017-08-31，新加坡国立大学，Research Fellow 2017-09-01—2019-08-31，香港理工大学，博士后 2023-02-01—2023-08-31，香港理工大学，Research Fellow

学术成果

代表性论文 [1] Liang Chen, Defeng Sun, and Kim-Chuan Toh: An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming, Mathematical Programming, 2017, 161(1): 237—270 [2] Liang Chen, Defeng Sun, and Kim-Chuan Toh: A note on the convergence of ADMM for linearly constrained convex optimization problems, Computational Optimization and Applications, 2017, 66(2): 327—343 [3] Yunhai Xiao, Liang Chen, and Donghui Li: A generalized alternating direction method of multipliers with semi-proximal terms for convex composite conic programming, Mathematical Programming Computation, 2018, 10(4): 533—555 [4] Liang Chen, Defeng Sun, and Kim-Chuan Toh: Some problems on the Gauss-Seidel iteration method in degenerate cases, Journal on Numerical Methods and Computer Applications, 2019, 40: 98—110 (in Chinese) [5] Liang Chen, Defeng Sun, Kim-Chuan Toh, and Ning Zhang: A unified algorithmic framework of symmetric Gauss-Seidel decomposition based proximal ADMMs for convex composite programming, Journal of Computational Mathematics, 2019, 37: 739—757 [6] Xin-Yuan Zhao, Liang Chen: The linear and asymptotically superlinear convergence rates of the augmented Lagrangian method with a practical relative error criterion (Dedicated to Professor Minyi Yue on the occasion of his 100th birthday), Asia-Pacific Journal of Operational Research, 2020, 37(4): 2040001 [7] Liang Chen, Xiaokai Chang, and Sanyang Liu: A three-operator splitting perspective of a three-block ADMM for convex quadratic semidefinite programming and beyond (Dedicated to Professor Minyi Yue on the occasion of his 100th birthday), Asia-Pacific Journal of Operational Research, 2020, 37(4): 2040009 [8] Liang Chen, Anping Liao: On the convergence properties of a second-order augmented Lagrangian method for nonlinear programming problems with inequality constraints, Journal of Optimization Theory and Applications, 2020, 187: 248—265 [9] Liang Chen, Xudong Li, Defeng Sun, and Kim-Chuan Toh: On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming, Mathematical Programming, 2021, 185: 111—161 [10] Hailin Sun, Dali Zhang, Soon-Yi Wu, and Liang Chen: A modified exchange algorithm for distributional robust optimization and applications in risk management, International Transactions in Operational Research, 2022, 29(1): 130—157 [11] Liang Chen, Junyuan Zhu, and Xinyuan Zhao: Unified convergence analysis of a second-order method of multipliers for nonlinear conic programming, Science China Mathematics, 2022, 65: 2397—2422 [12] Haoran Ji, Lei Wang, Cong Peng, Liang Chen, Shuhao Zhang, and Qian Zhou: High-resolution short angle weight algorithm in sonar systems, IEEE Journal of Oceanic Engineering, 2023, online, doi:10.1109/JOE.2023.3318700 [13]  Liang Chen, Yaru Chen, Qiuqi Li, and Zhiwen Zhang: Stochastic domain decomposition based on variable-separation method, Computer Methods in Applied Mechanics and Engineering, 2024, 418(A): 116538