酈旭東，男，博士，復旦大學大數據學院青年研究員。
基本介紹
- 中文名：酈旭東
- 畢業院校：新加坡國立大學
- 學位/學歷：博士
- 職業：教師
- 專業方向：數據驅動的大規模最佳化問題理論與套用
- 職務：associate professor
- 任職院校：復旦大學大數據學院
個人經歷,研究方向,學術成果,
個人經歷
Xudong Li is a tenure-track associate professor at theSchool of Data Science,Fudan Universityand theShanghai Center for Mathematical Sciences. He received his Ph.D. degree in Optimization fromNational University of Singaporein 2015 under the supervision of ProfessorsDefeng SunandKim-Chuan Toh. From 2015 to 2017, he was a research fellow in the Department of Mathematics at National University of Singapore. Prior to joining Fudan University in autumn 2018, he was a postdoctoral research associate in the Department of Operations Research and Financial Engineering atPrinceton Universitysupervised by ProfessorMengdi Wang. He obtained his Bachelor’s degree in Mathematics fromUniversity of Science and Technology of China.
He received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2019 (awarded once every three years). Heis currently serving as an associate editor for Mathematical Programming Computation.
研究方向
- Matrix Optimization Problems, in particular, large scale convex quadratic semidefinite programming
- Efficient algorithms for large scaleoptimization problems in data science
- Optimization and decision making under uncertainty
為數據驅動的大規模最佳化問題理論與套用，以及其求解算法的設計、開發與分析。
學術成果
- Qinzhen Li and Xudong Li, Fast projection onto the ordered weightedℓ1norm ball,arXiv:2002.05004, 2020
- Rujun Jiang and Xudong Li, Hölderian error bounds and Kurdyka-Lojasiewicz inequality for the trust region subproblem,arXiv:1911.11955, 2019
- Xudong Li and Ethan Xingyuan Fang,Invited discussion on the article “A Bayesian conjugate gradient method”, Bayesian Analysis, 14 (2019), pp. 977–979
- Ziwei Zhu, Xudong Li, Mengdi Wang, and Anru Zhang, Learning Markov models via low-rank optimization,arXiv:1907.00113, 2019
- Xudong Li, Defeng Sun, and Kim-Chuan Toh, An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming,arXiv:1903.09546, 2019
- 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
- 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
- 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
- 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
- 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
- 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
- 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)
- 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
- 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
