| A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems X Li, D Sun, KC Toh SIAM Journal on Optimization 28 (1), 433-458, 2018 | 216 | 2018 |
| A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions X Li, D Sun, KC Toh Mathematical Programming 155 (1), 333-373, 2016 | 163 | 2016 |
| QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming X Li, D Sun, KC Toh Mathematical Programming Computation 10, 703-743, 2018 | 97 | 2018 |
| On the convergence properties of a majorized alternating direction method of multipliers for linearly constrained convex optimization problems with coupled objective functions Y Cui, X Li, D Sun, KC Toh Journal of Optimization Theory and Applications 169, 1013-1041, 2016 | 85* | 2016 |
| On efficiently solving the subproblems of a level-set method for fused lasso problems X Li, D Sun, KC Toh SIAM Journal on Optimization 28 (2), 1842-1866, 2018 | 72 | 2018 |
| An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for linear programming X Li, D Sun, KC Toh SIAM Journal on Optimization 30 (3), 2410-2440, 2020 | 63 | 2020 |
| On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming L Chen, X Li, D Sun, KC Toh Mathematical Programming 185 (1), 111-161, 2021 | 61 | 2021 |
| A block symmetric Gauss–Seidel decomposition theorem for convex composite quadratic programming and its applications X Li, D Sun, KC Toh Mathematical Programming 175, 395-418, 2019 | 60 | 2019 |
| On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope X Li, D Sun, KC Toh Mathematical Programming 179 (1), 419-446, 2020 | 47 | 2020 |
| Learning Markov models via low-rank optimization Z Zhu, X Li, M Wang, A Zhang Operations Research 70 (4), 2384-2398, 2022 | 37 | 2022 |
| Estimation of Markov chain via rank-constrained likelihood X Li, M Wang, A Zhang International Conference on Machine Learning, 3033-3042, 2018 | 22 | 2018 |
| A TWO-PHASE AUGMENTED LAGRANGIAN METHOD FOR CONVEX COMPOSITE QUADRATIC PROGRAMMING X Li National University of Singapore, 2015 | 22 | 2015 |
| Fast algorithms for stackelberg prediction game with least squares loss J Wang, H Chen, R Jiang, X Li, Z Li International Conference on Machine Learning, 10708-10716, 2021 | 21 | 2021 |
| Recursive importance sketching for rank constrained least squares: Algorithms and high-order convergence Y Luo, W Huang, X Li, A Zhang Operations Research 72 (1), 237-256, 2024 | 17 | 2024 |
| Hölderian error bounds and kurdyka-łojasiewicz inequality for the trust region subproblem R Jiang, X Li Mathematics of Operations Research 47 (4), 3025-3050, 2022 | 17 | 2022 |
| Solving stackelberg prediction game with least squares loss via spherically constrained least squares reformulation J Wang, W Huang, R Jiang, X Li, AL Wang International conference on machine learning, 22665-22679, 2022 | 14 | 2022 |
| 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 |
| Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization Y Luo, X Li, AR Zhang INFORMS Journal on Optimization 7 (1), 38-60, 2025 | 9 | 2025 |
| Data-driven minimax optimization with expectation constraints S Yang, X Li, G Lan Operations Research, 2024 | 9 | 2024 |
| Augmented Lagrangian methods for convex matrix optimization problems Y Cui, C Ding, XD Li, XY Zhao Journal of the Operations Research Society of China 10 (2), 305-342, 2022 | 9 | 2022 |
Defeng Sun (孙德锋)Chair Professor of Applied Optimization and OR, The Hong Kong Polytechnic UniversityVerified email at polyu.edu.hk
