CN EN

KONG Lingchen

Professional title:Professor

Office No.:86-10-51683333

Email:lchkong@bjtu.edu.cn

Education

September 2007-August 2009, Postdoctoral, University of Waterloo

September 2004-July 2007, Ph.D. Student, Operations Research, Beijing Jiaotong University

September 2001-June 2004, M.Sc. Course Student, Management Science and Engineering, Shandong University of Science and Technology

September 1994-June 1996, Second Bachelor's Degree, School of Economics and Management, China University of Mining and Technology

September 1990-June 1994, B.Sc. Student, Mathematics, Qufu Normal University

Professional Experience

Lingchen Kong is now a professor in Department of Mathematics, School of Science, Beijing Jiaotong University. His research interests include optimization theory, methods and applications, Statistical analysis of operations research, statistical learning. Visited Chinese University of Hong Kong as research assistant from January 2014 to March 2014; visiting scholar at University of Minnesota from September 2014 to September 2015; visiting scholar at University of Minnesota from July 2016 to August 2016; visiting scholar at National University of Singapore from February 2017 to March 2017; visiting scholar at Southampton University from February 2018 to March 2018. Served as Deputy Secretary General of The Mathematical Programming Branch of OR Society of China. Editorial board for Journal on Numerical Methods and Computer Applications from 2017, Mathematical foundation of computing from 2017, Asia-Pacific Journal of Operational Research from 2020.

Research Field

Optimization Theory, Methods and Applications, Statistical Analysis of Operations Research, Statistical Learning

Projects

2021.01-2024.12: The National Natural Science Foundation of China, “Research on theory and algorithm of structural matrix optimization for high-dimensional clustering”.

2020.08-2020.12: The Tianyuan Mathematical Communication Projection on Statistical Optimization and Artificial Intelligence.

2019.01-2019.12: The Tianyuan Mathematical Communication Projection on Optimization and Machine Learning.

2017.01-2020.12: The National Natural Science Foundation of China, “Optimization Theory and Algorithm of High Dimensional Constrained Matrix Regression”.

2015.01-2019.12: The National Natural Science Foundation of China, “Theory and Algorithms for Large-Scale Sparse Optimization”.

2013.01-2014.12: The Basic Research Funds: “Fast Optimization Method and Application Research of Sparse Magnetic Resonance Tensor Imaging”.

2012.01-2014.12: Ministry of science and technology "973", “A priori Statistical Modeling and Nonlinear Optimization Theory (II)”.

2012.01-2015.12: The National Natural Science Foundation of China, “Relaxation Theory and Algorithm of Matrix Rank Minimization”.

2011.07-2013.12: The Talent Fund: “Study on Optimization Method for Sparse Magnetic Resonance Tensor Imaging”.

2011.03-2013.12: The Basic Research Funds: “Research on p-relaxation Optimization Theory and Algorithm for Matrix Rank Minimal Problem”.

2011.01-2012.12: The Fund for Returning Overseas Students: “Study of Newtonian Algorithms for Symmetric Cone Complementarity Problems”.

2010.05-2011.12: Ministry of science and technology "973", “A priori Statistical Modeling and Nonlinear Optimization Theory”.

2009.01-2012.12: The National Natural Science Foundation of China, “Optimization Theory and Applications - Symmetric Cone Optimization and Complementary Newtonian Algorithms”.

Teaching Courses

Probability Theory and Mathematical Statistics, Statistical Machine Learning, Optimization Methods I & III, Statistical Optimization, Statistical Methods and Calculations

Paper

[1] Pan Shang and Lingchen Kong, L_1 norm quantile regression screening rule via the dual circumscribed sphere, IEEE Transaction on Pattern Analysis and Machine Intelligence, accept, 2021.

[2] Hongxin Zhao, Lingchen Kong and Hou-Duo Qi, Optimal portfolio selections via L_1,2-norm regularization. Computational Optimization and Applications, accept, 2021.

[3] Jiyuan Tao, Guoqiang Wang and Lingchen Kong, The Araki-Lieb-Thirring inequality and the Golden-Thompson inequality in Euclidean Jordan algebras, Linear and Multilinear Algebra, accept, 2021.

[4] Bingzhen Chen, Wenjuan Zhai and Lingchen Kong, Variable selection and collinearity processing for multivariate data via row-elastic-net regularization, AStA Advances in Statistical Analysis, accept, 2021.

[5] Jun Sun, Lingchen Kong and Mei Li, Fast Algorithms for LS and LAD-collaborative regression, Asia-Pacific Journal of Operational Research, accept, 2021.

[6] Xianchao Xiu, Ying Yang, Lingchen Kong and Wangquan Liu, Data-driven process monitoring using structured joint sparse canonical correlation analysis, IEEE Transactions on Circuits and Systems II: Express Briefs, 68(1), 361-365, 2021.

[7] Pan Shang and Lingchen Kong, Regularized parameter selection for the low rank matrix recovery, Journal of Optimization Theory and Applications, 189, 772-792, 2021.

[8] Mei Li, Lingchen Kong and Zhihua Su, Double fused Lasso regularized regression with both matrix and vector valued predictors,Electronical of Journal Statistics, 15(1), 1909-1950, 2021.

[9] Huaiyuan Zhai, Mengjie Li, Shengyue Hao, Mingli Chen and Lingchen Kong, How does metro maintenance staff’s risk perception influence safety citizenship behavior-the mediating role of safety attitude, International Journal of Environmental Research and Public Health, 18(10), 5466, 2021.

[10] Pan Shang and Lingchen Kong, Singular value screening rules for the nuclear norm regularized multivariate linear regression, Pacific Journal of Optimization, 17(1), 1-22, 2021.

[11] Xianchao Xiu, Ying Yang, Lingchen Kong and Wangquan Liu, tSSNALM: A fast two-stage semi-smooth Newton augmented Lagrangian method for sparse CCA, Applied Mathematics and Computation, 383(15), 125272, 2020.

[12] Xianchao Xiu, YingYang, Lingchen Kong, Wangquan Liu and Meijuan Shang, An improved total variation regularized RPCA for moving object detection with dynamic background, Journal of Industrial and Management Optimization, 16(4), 1685-1698, 2020.

[13] Huangyue Chen, Lingchen Kong, Pan Shang and Shanshan Pan, Safe feature screening rules for the regularized Huber regression, Applied Mathematics and Computation, 357, 119-138, 2020.

[14] Xianchao Xiu, Ying Yang, Lingchen Kong and Wanquan Liu, Laplacian regularized robust principal component analysis for process monitoring, Journal of Process Control, 92, 212-219, 2020.

[15] Bingzhen Chen, Lingchen Kong and Nana Xu, Smoothing quantile regression with elastic net penalty, Pacific Journal of Optimization, 16(3), 369-393, 2020.

[16] Huangyue Chen, Lingchen Kong and Yan Li, A novel convex clustering method for high-dimensional data using semiproximal ADMM, Mathematical Problems in Engineering, 9216351, 2020.

[17] Xianchao Xiu, Ying Yang, Lingchen Kong and Wanquan Liu, A highly efficient joint sparsity constrained robust principal component analysis for fault diagnosis, Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS, 36-41, 2020.

[18] Lingchen Kong, Chuanqi Qi and Hou-Duo Qi, Classical multidimensional scaling: a subspace perspective, over-denoising, and outlier detection, IEEE Transactions on Signal Processing, 67(14), 3842-3857, 2019.

[19] Xianchao Xiu, Wanquan Liu, Ling Li and Lingchen Kong, Alternating direction method of multipliers for nonconvex fused regression problems, Computational Statistics and Data Analysis, 136, 59–71, 2019.

[20] Huifang Wang, Lingchen Kong and Jiyuan Tao, The linearized alternating direction method of multipliers for sparse group LAD model, Optimization Letters, 13, 505–525, 2019.

[21] 张璐, 孔令臣, 陈黄岳, 基于距离相关系数的分层聚类法, 计算数学, 41 (3), 320-334, 2019.

[22] Jun Fan, Lingchen Kong, Liqun Wang and Naihua Xiu, Variable selection in sparse regression with quadratic measurements, Statistica Sinica. 28(3), 1157-1178, 2018.

[23] Yuwen Gu, Jun Fan, Lingchen Kong, Shiqian Ma and Hui Zou, ADMM for high-dimensional sparse penalized quantile regression, Technometrics, 60 (3), 319-331, 2018.

[24] Xianchao Xiu, Lingchen Kong, Yan Li and Houduo Qi, Iterative reweighted methods for L1-Lp minimization, Computational Optimization and Applications, 70, 201-219, 2018.

[25] 陈丙振, 孔令臣, 尚盼, 稳健矩阵回归模型和方法研究, 计算数学, 40 (4), 402-417, 2018.

[26] Bingzhen Chen and Lingchen Kong, High-dimensional least square matrix regression via Elastic Net penalty, Pacific Journal of Optimization, 13(2), 185-196, 2017.  

[27] Huaiyuan Zhai, Yan Li, Qingmin Meng, Lingchen Kong and Jiyuan Tao, A power penalty method for linear complementary problem over symmetric cones, Pacific Journal of Optimization, 13(2), 365-374, 2017.  

[28] Pan Shang and Lingchen Kong, On the degrees of freedom of mixed matrix regression, Mathematical Problems in Engineering, 1-8, 2017.

[29] 孔令臣, 陈丙振, 修乃华, 戚厚铎, 高维约束矩阵回归问题, 运筹学学报, 21(2) , 31—38, 2017.

[30] Lianjun Zhang, Lingchen Kong, Yan Li and Shenglong Zhou, A smoothing iterative method for quantile regression with nonconvex lp penalty, Journal of Industrial and Management Optimization, 3(1), 93-112, 2017. 

[31] Guoqiang Wang, Jiyuan Tao and Lingchen Kong, A note on an inequality involving Jordan productin Euclidean Jordan algebras, Optimization Letters, 10(4), 1-6, 2016.

[32] Shenglong Zhou,Naihua Xiu,Yingnan Wang,Lingchen Kong and Huoduo Qi, A null-space-based weighted L1 minimization approach to compressed sensing, Information and Inference: A Journal of the IMA, 5(1), 76-102, 2016.

[33] Lingchen Kong, Jie Sun and Jiyuan Tao, Sparse recovery on Euclidean Jordan algebras, Linear Algebra and Its Applications, 465, 65-87, 2015

[34] Guoqiang Wang, Lingchen Kong and Jiyuan Tao, Improved complexity analysis of Full Nesterov-Todd step feasible interior-point method for symmetric optimization, Journal of Optimization Theory and Applications, 166, 588-604, 2015.

[35] Sarah Y Gao, Lingchen Kong and Jie Sun, Robust two-stage stochastic linear programs with moment constraints, Optimization, 63, 829-837, 2014

[36] Ziyan Luo, Linxia Qin, Lingchen Kong and NaihuaXiu, The nonnegative L0 norm minimization under generalized Z-matrix, Journal of Optimization Theory and Applications, 160, 854-864, 2014.

[37] Jiyuan Tao,Lingchen Kong and Ziyan Luo, Some majorization inequalities in Euclidean Jordan algebras, Linear Algebra and Its Applications, 461, 92-122, 2014.

[38] Bo Zhong, Huanhe Dong, Lingchen Kong and Jiyuan Tao, Generalized nonlinear complementarity problems with order P0 and R0 properties, Positivity 18, 413-423, 2014.

[39] Lingchen Kong and Naihua Xiu, Exact low-rank matrix recovery via noncovex schatten p-minimization, Aisa-Pacific Journal of Oprational Reseach, 30(3), 1340010(13pages), 2013.

[40] Lingchen Kong, Levent Tuncel and Naihua Xiu, Lowner operator and its applications to symmetric cone complementarity problems, Mathematical Programming, 133, 327-336, 2012.

[41] Lingchen Kong, Levent Tuncel and Naihua Xiu, Existence and uniqueness of solutions for homogeneous cone complementarity problems, Journal of Optimization Theory and Applications, 153(2), 357-376, 2012.

[42] Lingchen Kong and Qingmin Meng, A semismooth Newton method for nonlinear symmetric cone programming, Mathematical Methods of Operations Research, 76, 129-145, 2012.

[43] Lingchen Kong, Quadratic convergence of a smoothing Newton method for symmetric cone programming without strict complementarity, Positivity, 16, 297-319, 2012.

[44] Ziyan Luo, Naihua Xiu and Lingchen Kong, Lyapunov-type least-squares problems over symmetric cones, Linear Algebra and its Applications,437, 2498-2515, 2012.

[45] Linxia Qin, Naihua Xiu , Lingchen Kong and Yu Li, Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays, Computational and Mathematical Methods in Medicine, ID 646045, 2012.

[46] Lingchen Kong, Linxia Qin and Naihua Xiu, A penalized NR function for symmetric cone complementarity problems, Advance in Mathematics, 2, 173-178, 2011.

[47] Lingchen Kong, Levent Tuncel and Naihua Xiu, Equivalent conditions for Jacobian nonsingularity in linear symmetric cone programming,Journal of Optimization Theory and Applications, 148, 364-389, 2011.

[48] Lingchen Kong, Levent Tuncel and Naihua Xiu, Fischer-Burmeister complementarity function on Euclidean Jordan algebras, Pacific Journal of Optimization, 6, 423-440, 2010.

[49] Lingchen Kong, Jie Sun and Naihua Xiu, A regularized smoothing Newton method for symmetric cone complementarity problems, SIAM Journal on Optimization, 19, 1028-1047, 2008.

Monograph

Lingchen Kong, Lichun Wang,Probability and Statistics,Beijing Jiaotong University Press,2014