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Xudong Li
Xudong Li
Associate Professor, Fudan University
Verifierad e-postadress på fudan.edu.cn - Startsida
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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
1722018
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
1462016
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
832018
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
73*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
582018
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
522020
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
522019
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
492021
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
372020
Learning Markov models via low-rank optimization
Z Zhu, X Li, M Wang, A Zhang
Operations Research 70 (4), 2384-2398, 2022
262022
A TWO-PHASE AUGMENTED LAGRANGIAN METHOD FOR CONVEX COMPOSITE QUADRATIC PROGRAMMING
X Li
National University of Singapore, 2015
222015
Estimation of Markov chain via rank-constrained likelihood
X Li, M Wang, A Zhang
International Conference on Machine Learning, 3033-3042, 2018
212018
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
142024
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
132021
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
122022
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
72022
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
62022
Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization
Y Luo, X Li, AR Zhang
arXiv preprint arXiv:2108.01772, 2021
62021
Data-driven minimax optimization with expectation constraints
S Yang, X Li, G Lan
Operations Research, 2024
52024
A dynamic programming approach for generalized nearly isotonic optimization
Z Yu, X Chen, X Li
Mathematical Programming Computation 15 (1), 195-225, 2023
52023
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Artiklar 1–20