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Xudong Li
Xudong Li
Professor, Fudan University
Подтвержден адрес электронной почты в домене fudan.edu.cn - Главная страница
Название
Процитировано
Процитировано
Год
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
2082018
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
1582016
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
942018
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
83*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
702018
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
642020
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
592021
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
582019
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
442020
Learning Markov models via low-rank optimization
Z Zhu, X Li, M Wang, A Zhang
Operations Research 70 (4), 2384-2398, 2022
382022
Estimation of Markov chain via rank-constrained likelihood
X Li, M Wang, A Zhang
International Conference on Machine Learning, 3033-3042, 2018
222018
A TWO-PHASE AUGMENTED LAGRANGIAN METHOD FOR CONVEX COMPOSITE QUADRATIC PROGRAMMING
X Li
National University of Singapore, 2015
222015
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
202021
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
172024
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
142022
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
112022
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
112022
Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization
Y Luo, X Li, AR Zhang
INFORMS Journal on Optimization, 2024
92024
Data-driven minimax optimization with expectation constraints
S Yang, X Li, G Lan
Operations Research, 2024
92024
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
92022
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