| An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization H Yang, L Liang, L Carlone, KC Toh Mathematical Programming 201 (1), 409-472, 2023 | 35 | 2023 |
| QPPAL: A two-phase proximal augmented Lagrangian method for high-dimensional convex quadratic programming problems L Liang, X Li, D Sun, KC Toh ACM Transactions on Mathematical Software (TOMS) 48 (3), 1-27, 2022 | 12 | 2022 |
| An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems HTM Chu, L Liang, KC Toh, L Yang Computational Optimization and Applications 85 (1), 107-146, 2023 | 10 | 2023 |
| An inexact augmented Lagrangian method for second-order cone programming with applications L Liang, D Sun, KC Toh SIAM Journal on Optimization 31 (3), 1748-1773, 2021 | 10 | 2021 |
| Fast and certifiable trajectory optimization S Kang, X Xu, J Sarva, L Liang, H Yang arXiv preprint arXiv:2406.05846, 2024 | 8 | 2024 |
| Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition C Lee, L Liang, T Tang, KC Toh Journal of Machine Learning Research 25 (379), 1-52, 2024 | 8 | 2024 |
| A new homotopy proximal variable-metric framework for composite convex minimization Q Tran-Dinh, L Liang, KC Toh Mathematics of Operations Research 47 (1), 508-539, 2022 | 6 | 2022 |
| A corrected inexact proximal augmented Lagrangian method with a relative error criterion for a class of group-quadratic regularized optimal transport problems L Yang, L Liang, HTM Chu, KC Toh Journal of Scientific Computing 99 (3), 79, 2024 | 4 | 2024 |
| A squared smoothing Newton method for semidefinite programming L Liang, D Sun, KC Toh Mathematics of Operations Research, 2024 | 3 | 2024 |
| A sparse smoothing Newton method for solving discrete optimal transport problems D Hou, L Liang, KC Toh ACM Transactions on Mathematical Software 50 (3), 1-26, 2024 | 3 | 2024 |
| On Degenerate Doubly Nonnegative Projection Problems Y Cui, L Liang, D Sun, KC Toh Mathematics of Operations Research 47 (3), 2219-2239, 2022 | 3* | 2022 |
| An Inexact Halpern Iteration with Application to Distributionally Robust Optimization L Liang, KC Toh, JJ Zhu arXiv preprint arXiv:2402.06033, 2024 | 2 | 2024 |
| Accelerating Multi-Block Constrained Optimization Through Learning to Optimize L Liang, C Austin, H Yang arXiv preprint arXiv:2409.17320, 2024 | 1 | 2024 |
| Vertex exchange method for a class of quadratic programming problems L Liang, KC Toh, H Yang arXiv preprint arXiv:2407.03294, 2024 | 1 | 2024 |
| From Equations to Insights: Unraveling Symbolic Structures in PDEs with LLMs R Bhatnagar, L Liang, K Patel, H Yang arXiv preprint arXiv:2503.09986, 2025 | | 2025 |
| PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal Transport D Wu, L Liang, H Yang arXiv preprint arXiv:2502.03749, 2025 | | 2025 |
| ripALM: A Relative-Type Inexact Proximal Augmented Lagrangian Method with Applications to Quadratically Regularized Optimal Transport J Zhu, L Liang, L Yang, KC Toh arXiv preprint arXiv:2411.13267, 2024 | | 2024 |
| PNOD: An Efficient Projected Newton Framework for Exact Optimal Experimental Designs L Liang, H Yang https://arxiv.org/abs/2409.18392, 2024 | | 2024 |
| Nesterov's Accelerated Jacobi-Type Methods for Large-scale Symmetric Positive Semidefinite Linear Systems L Liang, Q Pang, KC Toh, H Yang arXiv preprint arXiv:2407.03272, 2024 | | 2024 |
| On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization L Liang, H Yang arXiv preprint arXiv:2401.12508, 2024 | | 2024 |
Kim-Chuan TohProfessor of Mathematics, National University of SingaporeVerified email at nus.edu.sg
