XUDONG LI
XUDONG LI
Associate Professor, Fudan University
Verifierad e-postadress på fudan.edu.cn - Startsida
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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
952016
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
542018
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
47*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
42*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 (1-2), 395-418, 2019
302019
A TWO-PHASE AUGMENTED LAGRANGIAN METHOD FOR CONVEX COMPOSITE QUADRATIC PROGRAMMING
X Li
National University of Singapore, 2015
192015
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
162019
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
132018
On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope
X Li, D Sun, KC Toh
Mathematical Programming 179 (1-2), 419-446, 2020
92020
Estimation of Markov chain via rank-constrained likelihood
X Li, M Wang, A Zhang
arXiv preprint arXiv:1804.00795, 2018
92018
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
An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming
X Li, D Sun, KC Toh
arXiv preprint arXiv:1903.09546, 2019
12019
Fast projection onto the ordered weighted norm ball
Q Li, X Li
arXiv preprint arXiv:2002.05004, 2020
2020
H\" olderian error bounds and Kurdyka-{\L} ojasiewicz inequality for the trust region subproblem
R Jiang, X Li
arXiv preprint arXiv:1911.11955, 2019
2019
Learning Markov models via low-rank optimization
Z Zhu, X Li, M Wang, A Zhang
arXiv preprint arXiv:1907.00113, 2019
2019
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Artiklar 1–15