Research

Papers and preprints:

    1. Rujun Jiang and Xudong Li, Hölderian error bounds and Kurdyka-Lojasiewicz inequality for the trust region subproblem, arXiv:1911.11955, 2019
    2. Xudong Li and Ethan Xingyuan Fang, Invited discussion on the article “A Bayesian conjugate gradient method”, Bayesian Analysis, 14 (2019), pp. 977–979.
    3. Ziwei Zhu, Xudong Li, Mengdi Wang, and Anru Zhang, Learning Markov models via low-rank optimization, arXiv:1907.00113, 2019
    4. Xudong Li, Defeng Sun, and Kim-Chuan Toh, An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming, arXiv:1903.09546, 2019
    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, in print, arXiv:1803.10803, 2018
    6. 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
    7. 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
    8. 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
    9. 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
    10. 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
    11. 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)
    12. 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
    13. 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