Research

Preprints:

    1. Yuetian Luo, Xudong Li, and Anru Zhang, On geometric connections of embedded and quotient geometries in Riemannian fixed-rank matrix optimizationarXiv: 2110.12121, 2021
    2. Yuetian Luo, Xudong Li, and Anru Zhang, Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization,  arXiv: 2108.01772, 2021
    3. 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, arXiv:2103.13108, 2021
    4. Yuetian Luo, Wen Huang, Xudong Li, and Anru Zhang, Recursive importance sketching for rank constrained least squares,        arXiv:2011.08630, 2020
    5. Zhensheng Yu, Xuyu Chen, and Xudong Li, A dynamic programming approach for generalized nearly isotonic optimization, arXiv:2011.03305, 2020
    6. 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. Rujun Jiang and Xudong Li, Hölderian error bounds and Kurdyka-Lojasiewicz inequality for the trust region subproblem, Mathematics of Operations Research, in print, arXiv:1911.11955, 2021
    2. Qinzhen Li and Xudong Li, Fast projection onto the ordered weighted $\ell_1$ norm ball, arXiv:2002.05004, SCIENCE CHINA Mathematics,  2021, https://doi.org/10.1007/s11425-020-1743-9
    3. 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
    4. 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
    5. 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
    6. Ziwei Zhu, Xudong Li, Mengdi Wang, and Anru Zhang, Learning Markov models via low-rank optimization, Operations Research, in print, arXiv:1907.00113, 2020
    7. 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
    8. Xudong Li, Efficient proximal point algorithm for convex composite optimization, Mathematica Numerica Sinica, 42 (2020), pp. 385-404 (in Chinese)
    9. 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
    10. Xudong Li and Ethan Xingyuan Fang, Invited discussion on the article “A Bayesian conjugate gradient method”, Bayesian Analysis, 14 (2019), pp. 977–979
    11. 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
    12. 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
    13. 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
    14. 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
    15. 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)
    16. 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
    17. 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