Research

Papers and preprints:

    1. Ziwei Zhu, Xudong Li, Mengdi Wang, and Anru Zhang, Learning Markov models via low-rank optimization, arXiv:1907.00113, 2019
    2. Xudong Li, Defeng Sun, and Kim-Chuan Toh, An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming, arXiv:1903.09546, 2019
    3. 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, in print, arXiv:1803.10803, 2018
    4. Xudong Li, Defeng Sun, and Kim-Chuan Toh, On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope, arXiv:1702.05934, Mathematical Programming, DOI: 10.1007/s10107-018-1342-9, 2018
    5. 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
    6. Xudong Li, Defeng Sun, and Kim-Chuan Toh, QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming (with Defeng Sun and Kim-Chuan Toh), Mathematical Programming Computation,  10 (2018), pp. 703–743, arXiv:1512.08872, Springer Nature SharedIT
    7. 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
    8. Xudong Li, Defeng Sun, and Kim-Chuan Toh, On efficiently solving the subproblems of a level-set method for fused lasso problems (with Defeng Sun and Kim-Chuan Toh), SIAM Journal on Optimization, 28 (2018), pp. 1842–1866
    9. Xudong Li, Defeng Sun, and Kim-Chuan Toh, A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems (with Defeng Sun and Kim-Chuan Toh), SIAM Journal on Optimization, 28 (2018), pp. 433–458                                                      Best Paper Prize for Young Researchers in Continuous Optimization, ICCOPT 2019 (1 in 3 years)
    10. 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
    11. 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