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Ling Liang, Defeng Sun, and Kim-Chuan Toh. An inexact augmented Lagrangian method for second-order cone programming with applications. SIAM Journal on Optimization 31, no. 3 (2021): 1748-1773. arXiv, SIOPT

Ling Liang, Xudong Li, Defeng Sun, and Kim-Chuan Toh. QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems. ACM Transactions on Mathematical Software 48, no. 3 (2022): 1-27. arXiv, TOMS, CODE
Ying Cui, Ling Liang, Defeng Sun, and Kim-Chuan Toh. On degenerate doubly nonnegative projection problems. Mathematics of Operations Research 47, no. 3 (2022): 2219-2239. arXiv, MOOR
Quoc Tran-Dinh, Ling Liang, and Kim-Chuan Toh. A new homotopy proximal variable-metric framework for composite convex minimization. Mathematics of Operations Research 47, no. 1 (2022): 508-539. arXiv, MOOR

Hong T.M. Chu, Ling Liang, Kim-Chuan Toh, and Lei Yang. An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems. Computational Optimization and Applications 85, no. 1 (2023): 107–146. arXiv, COAP, CODE
Heng Yang, Ling Liang, Luca Carlone, and Kim-Chuan Toh. An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization. Mathematical Programming 201, no. 1–2 (2023): 409–472. arXiv, MP, CODE

Ling Liang, Defeng Sun, and Kim-Chuan Toh. A squared smoothing Newton method for semidefinite programming. Mathematics of Operations Research, 2024. arXiv MOOR
Shucheng Kang, Xiaoyang Xu, Jay Sarva, Ling Liang, and Heng Yang. Fast and certifiable trajectory optimization, WAFR 2024. arXiv, CODE
Ling Liang, Haizhao Yang. On the stochastic (variance-reduced) proximal gradient method for regularized expected reward optimization, TMLR 2024. arXiv, TMLR
Di Hou, Ling Liang, and Kim-Chuan Toh. A sparse smoothing Newton method for solving discrete optimal transport problems, ACM Transactions on Mathematical Software 50, no. 3 (2024): 1–26. arXiv, TOMS
Lei Yang, Ling Liang, Hong T.M. Chu, and Kim-Chuan Toh. A corrected inexact proximal augmented Lagrangian method with a relative error criterion for a class of group-quadratic regularized optimal transport problems. Journal of Scientific Computing 99, no. 79 (2024). arXiv J. Sci. Comput.

Jiayi Zhu, Ling Liang, Lei Yang, and Kim-Chuan Toh. ripALM: A relative-type inexact proximal augmented Lagrangian method with applications to quadratically regularized optimal transport, 2024. arXiv
Ling Liang, Haizhao Yang. PNOD: An efficient projected Newton framework for exact optimal experimental designs, 2024. arXiv
Ling Liang, Cameron Austin, and Haizhao Yang. Accelerating multi-block constrained optimization through learning to optimize, 2024. arXiv
Ling Liang, Kim-Chuan Toh, and Haizhao Yang. Vertex exchange method for a class of convex quadratic programming problems, 2024. arXiv
Ling Liang, Qiyuan Pang, Kim-Chuan Toh, and Haizhao Yang. Nesterov’s accelerated Jacobi-type methods for large-scale symmetric positive semidefinite linear systems, 2024. arXiv
Ling Liang, Kim-Chuan Toh, and Jia-Jie Zhu. An inexact Halpern iteration with application to distributionally robust optimization, 2024. arXiv
Ching-pei Lee, Ling Liang, Tianyun Tang, and Kim-Chuan Toh. Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition, 2023. arXiv