Preprints:

1. 1. Shuoguang Yang, Xudong Li, Guanghui Lan, Data-driven minimax optimization with expectation constraints,  arXiv: 2202.07868, 2022
   2. Yuetian Luo, Xudong Li, and Anru Zhang, On geometric connections of embedded and quotient geometries in Riemannian fixed-rank matrix optimization,  arXiv: 2110.12121, 2021
   3. Yuetian Luo, Xudong Li, and Anru Zhang, Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization,  arXiv: 2108.01772, 2021
   4. 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:

1. 1. Yuetian Luo, Wen Huang, Xudong Li, and Anru Zhang, Recursive importance sketching for rank constrained least squares, Operations Research, accepted, arXiv:2011.08630, 2022
   2. 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
   3. 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
   4. Ling Liang, Xudong Li, Defeng Sun, and Kim-Chuan Toh, QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems, in print, ACM Transactions on Mathematical Software, 2022, arXiv:2103.13108
   5. Rujun Jiang and Xudong Li, Hölderian error bounds and Kurdyka-Lojasiewicz 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
   6. Qinzhen Li and Xudong Li, Fast projection onto the ordered weighted $\ell_1$ norm ball, arXiv:2002.05004, SCIENCE CHINA Mathematics,  65 (2022), pp. 869-886, https://doi.org/10.1007/s11425-020-1743-9
   7. 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 139:10708-10716
   8. 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/s40305-021-00346-9
   9. Ziwei Zhu, Xudong Li, Mengdi Wang, and Anru Zhang, Learning Markov models via low-rank optimization, Operations Research, 70 (2022), 1953-2596,  arXiv:1907.00113
   10. Liang Chen, Xudong Li, Defeng Sun, and Kim-Chuan 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
   11. Xudong Li, Defeng Sun, and Kim-Chuan Toh, An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming, SIAM Journal on Optimization, 30 (2020), pp. 2410–2440
   12. Xudong Li, Efficient proximal point algorithm for convex composite optimization, Mathematica Numerica Sinica, 42 (2020), pp. 385-404 (in Chinese)
   13. Xudong Li, Defeng Sun, and Kim-Chuan 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
   14. Xudong Li and Ethan Xingyuan Fang, Invited discussion on the article “A Bayesian conjugate gradient method”, Bayesian Analysis, 14 (2019), pp. 977–979
   15. Xudong Li, Defeng Sun, and Kim-Chuan Toh, A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications, Mathematical Programming, 175 (2019),  pp. 396–418, Springer Nature SharedIT
   16. Xudong Li, Defeng Sun, and Kim-Chuan Toh, QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming, Mathematical Programming Computation,  10 (2018), pp. 703–743, arXiv:1512.08872, Springer Nature SharedIT
   17. Xudong Li, Mengdi Wang, and Anru Zhang, Estimation of Markov chain via rank-constrained likelihood, Proceedings of the 35-th International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80:3039-3048, 2018, Supplementary PDF
   18. Xudong Li, Defeng Sun, and Kim-Chuan Toh, On efficiently solving the subproblems of a level-set method for fused lasso problems, SIAM Journal on Optimization, 28 (2018), pp. 1842–1866
   19. Xudong Li, Defeng Sun, and Kim-Chuan 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)
   20. Ying Cui, Xudong Li, Defeng Sun, and Kim-Chuan 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
   21. Xudong Li, Defeng Sun, and Kim-Chuan Toh, A Schur complement based proximal ADMM for convex quadratic conic programming and extensions, Mathematical Programming, 155 (2016), pp. 333–373