XUDONG LI
XUDONG LI
Associate Professor, Fudan University
Verified email at fudan.edu.cn - Homepage
Title
Cited by
Cited by
Year
A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions
X Li, D Sun, KC Toh
Mathematical Programming 155 (1-2), 333-373, 2016
1162016
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
872018
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 (3), 1013-1041, 2016
55*2016
QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming
X Li, D Sun, KC Toh
Mathematical Programming Computation 10 (4), 703-743, 2018
54*2018
A block symmetric Gauss–Seidel decomposition theorem for convex composite quadratic programming and its applications
X Li, D Sun, KC Toh
Mathematical Programming 175 (1), 395-418, 2019
362019
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
272018
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, 1-51, 2019
252019
A TWO-PHASE AUGMENTED LAGRANGIAN METHOD FOR CONVEX COMPOSITE QUADRATIC PROGRAMMING
X Li
National University of Singapore, 2015
182015
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
152020
Estimation of Markov chain via rank-constrained likelihood
X Li, M Wang, A Zhang
International Conference on Machine Learning, 3033-3042, 2018
112018
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
92020
Recursive importance sketching for rank constrained least squares: Algorithms and high-order convergence
Y Luo, W Huang, X Li, AR Zhang
arXiv preprint arXiv:2011.08360, 2020
52020
Learning Markov models via low-rank optimization
Z Zhu, X Li, M Wang, A Zhang
arXiv preprint arXiv:1907.00113, 2019
32019
An efficient linearly convergent semismooth Netwon-CG augmented Lagrangian method for Lasso problems
X Li, D Sun, KC Toh
arXiv preprint arXiv:1607.05428, 2016
32016
H\" olderian error bounds and Kurdyka-{\L} ojasiewicz inequality for the trust region subproblem
R Jiang, X Li
arXiv preprint arXiv:1911.11955, 2019
22019
Fast projection onto the ordered weighted ℓ1 norm ball
Q Li, X Li
Science China Mathematics, 1-18, 2021
12021
Augmented Lagrangian Methods for Convex Matrix Optimization Problems
Y Cui, C Ding, XD Li, XY Zhao
Journal of the Operations Research Society of China, 1-38, 2021
2021
Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
J Wang, H Chen, R Jiang, X Li, Z Li
arXiv preprint arXiv:2105.05531, 2021
2021
QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems
L Liang, X Li, D Sun, KC Toh
arXiv preprint arXiv:2103.13108, 2021
2021
A dynamic programming approach for generalized nearly isotonic optimization
Z Yu, X Chen, X Li
arXiv preprint arXiv:2011.03305, 2020
2020
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Articles 1–20