郦旭东 - 复旦大学 - 大数据学院

个人简介

青年研究员，博士生导师。2015年博士毕业于新加坡国立大学，此后在新加坡国立大学和普林斯顿大学从事博士后研究。研究成果发表在权威国际期刊与会议（包括 MP, SIOPT, OR, MOR, MPC, ACM TOMS, ICML, DAC 等），获国际数学优化协会青年学者奖（2019，3年1人次），ICML 2022杰出论文奖（2022），中国运筹学会青年科技奖（2022）。入选第五届中国科协青年人才托举工程，获上海市扬帆计划及晨光计划支持。现为Mathematical Programming Computation副主编。 Education 2010–2015 Ph.D. in Mathematics (major in Optimization), National University of Singapore, Singapore. Dissertation: A two-phase augmented Lagrangian method for convex composite quadratic programming Advisors: Defeng Sun, Kim-Chuan Toh 2006–2010 B.S. in Mathematics, University of Science and Technology of China, Hefei, China. Positions Sep 2018 – Associate Professor, School of Data Science, Fudan University Aug 2017 – Aug 2018 Postdoctoral Research Associate, Department of Operations Research and Financial Engineering, Princeton University Sep 2015 – Jul 2017 Research Fellow, Department of Mathematics, National University of Singapore, Singapore Feb – Aug 2015 Research Assistant, Department of Mathematics, National University of Singapore, Singapore Honors and Awards Aug 2019 Best Paper Prize for Young Researchers in Continuous Optimization, by the Mathematical Optimization Society, 2019 (Awarded once every three years) Oct 2013 Part-Time Teaching Assistant Award for AY 2012/2013, National University of Singapore

研究领域

Matrix Optimization Problems, in particular, large scale convex quadratic semidefinite programming Efficient algorithms for large scale optimization problems in data science Optimization and decision making under uncertainty

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Shuoguang Yang, Xudong Li, and Guanghui Lan, Data-driven minimax optimization with expectation constraints, Operations Research, accepted, arXiv: 2202.07868, 2023 Tianchen Gu, Wangzhen Li, Aidong Zhao, Zhaori Bi, Xudong Li, Fan Yang, Changhao Yan, Wenchuang Hu, Dian Zhou, Tao Cui, Xin Liu, Zaikun Zhang, and Xuan Zeng, BBGP-sDFO: Batch Bayesian and Gaussian process enhanced subspace derivative free optimization for high-dimensional analog circuit synthesis, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, accepted, 2023. Xuyu Chen, Xudong Li, and Yangfeng Su, An active-set based recursive approach for solving convex isotonic regression with generalized order restrictions, Asia-Pacific Journal of Operational Research, accepted, arXiv:2304.00244, 2023 Yuetian Luo, Xudong Li, and Anru Zhang, On geometric connections of embedded and quotient geometries in Riemannian fixed-rank matrix optimization, Mathematics of Operations Research, https://doi.org/10.1287/moor.2023.1377, arXiv: 2110.12121, 2023 Yuetian Luo, Wen Huang, Xudong Li, and Anru Zhang, Recursive importance sketching for rank constrained least squares, Operations Research, https://doi.org/10.1287/opre.2023.2445, arXiv:2011.08630, 2023 Zhensheng Yu, Xuyu Chen, and Xudong Li, A dynamic programming approach for generalized nearly isotonic optimization, Mathematical Programming Computation, 15 (2023), pp.195–225. 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, ACM Transactions on Mathematical Software, 48 (2022), pp. 1-27, arXiv:2103.13108 Rujun Jiang and Xudong Li, H?lderian error bounds and Kurdyka-Lojasiewicz inequality for the trust region subproblem, Mathematics of Operations Research, 47 (2022), pp. 3025-3050, arXiv:1911.11955 Qinzhen Li and Xudong Li, Fast projection onto the ordered weighted $\ell_1$ norm ball, SCIENCE CHINA Mathematics, 65 (2022), pp. 869-886 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, 10 (2022), pp. 305–342 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 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 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 Xudong Li, Efficient proximal point algorithm for convex composite optimization, Mathematica Numerica Sinica, 42 (2020), pp. 385-404 (in Chinese) 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 and Ethan Xingyuan Fang, Invited discussion on the article “A Bayesian conjugate gradient method”, Bayesian Analysis, 14 (2019), pp. 977–979 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, 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