Publications
- 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
- Ling Liang, Defeng Sun, and Kim-Chuan Toh. A squared smoothing Newton method for semidefinite programming. Mathematics of Operations Research, 2024 (Accepted). 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, 2024. 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.
- 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, 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
- 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
