Preprints:

 Shuoguang Yang, Xudong Li, Guanghui Lan, Datadriven minimax optimization with expectation constraints, arXiv: 2202.07868, 2022
 Yuetian Luo, Xudong Li, and Anru Zhang, On geometric connections of embedded and quotient geometries in Riemannian fixedrank matrix optimization, arXiv: 2110.12121, 2021
 Yuetian Luo, Xudong Li, and Anru Zhang, Nonconvex factorization and manifold formulations are almost equivalent in lowrank matrix optimization, arXiv: 2108.01772, 2021
 Wenying Liao, Xudong Li, Mengdi Wang, Lars O. Hedin, and Simon A. Levin, Coordinated market approach for nitrogen pollution reduction and food security, 2020
Selected Publications:

 Yuetian Luo, Wen Huang, Xudong Li, and Anru Zhang, Recursive importance sketching for rank constrained least squares, Operations Research, accepted, arXiv:2011.08630, 2022
 Zhensheng Yu, Xuyu Chen, and Xudong Li, A dynamic programming approach for generalized nearly isotonic optimization, Mathematical Programming Computation, in print, arXiv:2011.03305, 2022
 Jiali Wang, Wen Huang, Rujun Jiang, Xudong Li, and Alex L. Wang, Solving Stackelberg prediction game with least squares loss via spherically constrained least squares reformulation, accepted, International Conference on Machine Learning (ICML), 2022, arXiv:2206.02991 ICML 2022 Outstanding Paper Award
 Ling Liang, Xudong Li, Defeng Sun, and KimChuan Toh, QPPAL: A twophase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems, in print, ACM Transactions on Mathematical Software, 2022, arXiv:2103.13108
 Rujun Jiang and Xudong Li, Hölderian error bounds and KurdykaLojasiewicz inequality for the trust region subproblem, Mathematics of Operations Research, 2022, https://pubsonline.informs.org/doi/abs/10.1287/moor.2021.1243, arXiv:1911.11955
 Qinzhen Li and Xudong Li, Fast projection onto the ordered weighted $\ell_1$ norm ball, arXiv:2002.05004, SCIENCE CHINA Mathematics, 65 (2022), pp. 869886, https://doi.org/10.1007/s1142502017439
 Jiali Wang, He Chen, Rujun Jiang, Xudong Li, and Zihao Li, Fast algorithms for Stackelberg prediction game with least squares loss, Proceedings of the 38th International Conference on Machine Learning, 2021, PMLR 139:1070810716
 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, 2021, https://doi.org/10.1007/s40305021003469
 Ziwei Zhu, Xudong Li, Mengdi Wang, and Anru Zhang, Learning Markov models via lowrank optimization, Operations Research, 70 (2022), 19532596, arXiv:1907.00113
 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
 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)
 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