Preprints:

 Yuetian Luo, Wen Huang, Xudong Li, and Anru Zhang, Recursive importance sketching for rank constrained least squares, arXiv:2011.08630, 2020
 Zhensheng Yu, Xuyu Chen, and Xudong Li, A dynamic programming approach for generalized nearly isotonic optimization, arXiv:2011.03305, 2020
 Wenying Liao, Xudong Li, Mengdi Wang, Lars O. Hedin, and Simon A. Levin, Coordinated market approach for nitrogen pollution reduction and food security, 2020
 Rujun Jiang and Xudong Li, Hölderian error bounds and KurdykaLojasiewicz inequality for the trust region subproblem, arXiv:1911.11955, 2019
Selected Publications:

 Ling Liang, Xudong Li, Defeng Sun, and KimChuan Toh, QPPAL: A twophase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems, ACM Transactions on Mathematical Software, in print, arXiv:2103.13108, 2021
 Jiali Wang, He Chen, Rujun Jiang, Xudong Li, and Zihao Li, Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss, accepted, International Conference on Machine Learning (ICML), arXiv:2105.05531, 2021
 Ying Cui, Chao Ding, Xudong Li, and Xinyuan Zhao, Augmented Lagrangian methods for convex matrix optimization problems, Journal of the Operations Research Society of China, in print, 2021
 Liang Chen, Xudong Li, Defeng Sun, and KimChuan Toh, On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming, Mathematical Programming, 185 (2021), pp. 111–161, arXiv:1803.10803
 Ziwei Zhu, Xudong Li, Mengdi Wang, and Anru Zhang, Learning Markov models via lowrank optimization, Operations Research, in print, arXiv:1907.00113, 2020
 Xudong Li, Defeng Sun, and KimChuan Toh, An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming, SIAM Journal on Optimization, 30 (2020), pp. 2410–2440
 Xudong Li, Efficient proximal point algorithm for convex composite optimization, Mathematica Numerica Sinica, 42 (2020), pp. 385404 (in Chinese)
 Qinzhen Li and Xudong Li, Fast projection onto the ordered weighted $\ell_1$ norm ball, arXiv:2002.05004, SCIENCE CHINA Mathematics, in print, 2020
 Xudong Li, Defeng Sun, and KimChuan Toh, On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope, Mathematical Programming, 179 (2020), pp. 419–446, arXiv:1702.05934
 Xudong Li and Ethan Xingyuan Fang, Invited discussion on the article “A Bayesian conjugate gradient method”, Bayesian Analysis, 14 (2019), pp. 977–979
 Xudong Li, Defeng Sun, and KimChuan Toh, A block symmetric GaussSeidel decomposition theorem for convex composite quadratic programming and its applications, Mathematical Programming, 175 (2019), pp. 396–418, Springer Nature SharedIT
 Xudong Li, Defeng Sun, and KimChuan Toh, QSDPNAL: A twophase augmented Lagrangian method for convex quadratic semidefinite programming, Mathematical Programming Computation, 10 (2018), pp. 703–743, arXiv:1512.08872, Springer Nature SharedIT
 Xudong Li, Mengdi Wang, and Anru Zhang, Estimation of Markov chain via rankconstrained likelihood, Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80:30393048, 2018, Supplementary PDF
 Xudong Li, Defeng Sun, and KimChuan Toh, On efficiently solving the subproblems of a levelset method for fused lasso problems, SIAM Journal on Optimization, 28 (2018), pp. 1842–1866
 Xudong Li, Defeng Sun, and KimChuan Toh, A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems, SIAM Journal on Optimization, 28 (2018), pp. 433–458 Best Paper Prize for Young Researchers in Continuous Optimization, ICCOPT 2019 (1 in 3 years)
 Ying Cui, Xudong Li, Defeng Sun, and KimChuan Toh, On the convergence of a majorized ADMM for the linearly constrained convex optimization problems of coupled objective functions, Journal of Optimization Theory and Applications, 169 (2016), pp. 1013–1041
 Xudong Li, Defeng Sun, and KimChuan Toh, A Schur complement based proximal ADMM for convex quadratic conic programming and extensions, Mathematical Programming, 155 (2016), pp. 333–373