Qingna Li

Li Qingna Job Title: Professor E-mail: qnl@bit.edu.cn
I mainly study optimization theory and method and its application in artificial intelligence, medicine, communication and other fields. Interested students are welcome to contact me: ResearchGate: https://www.researchgate.net/profile/Qing-Na-Li (can be downloaded from the link above my thesis and code) personal homepage: https://math.bit.edu.cn/szdw/jgml/sxgcx/lqn/index.htm
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Optimization methods and theories, especially matrix optimization, sparse optimization, in finance, statistics, signal processing and other applications

September 2007 - June 2010, Doctor of Computational Mathematics, Hunan University, Tutors: Professor Li Donghui, Professor Qi Houduo.
September 2005 - July 2007, Master of Operations Research, Hunan University
September 2001 - July 2005, Bachelor Degree in Information and Computing Science, Hunan University

From July 2021 to now, Professor, School of Mathematics and Statistics, Beijing Institute of Technology
From March 2019 to now, PhD Supervisor, School of Mathematics and Statistics, Beijing Institute of Technology
July 2013 - June 2021, Associate Professor, School of Mathematics and Statistics, Beijing Institute of Technology
June 2012 - June 2013 Assistant Professor, School of Mathematics and Statistics, Beijing Institute of Technology
September 2010 - June 2012, Postdoctoral Fellow, Institute of Computational Mathematics and Science/Engineering Computing, Chinese Academy of Sciences, Supervisor: Professor Yuan Yaxiang

Chinese Literature :
[1] Lecture Notes on Convex Analysis, Li Qing-Na, LI Meng-Meng, Yu Panpan, Science Press, 2019.1.
[2] Multidimensional Scaling Analysis, Li Qingna Science Press 2019.4, ISBN 978-7-03-060963-2
[3] Lecture Notes on Convex Analysis: Conjugate Functions and Their Correlation Functions, Li Qingna, Science Press, 2020.12. ISBN 978-7-03-066877-6
[4] Optimization Method, Li Xuewen, Yan Guifeng, Li Qingna, Beijing Institute of Technology Press, 2018
English Papers :
[1] Li Q.N. and Qi H.D., A sequential semismooth Newton method for the nearest low-rank correlation matrix problem, SIAM Journal on Optimization, 21(2011). 1641-1666
[2] Li Q.N., Li D.H. and Qi H.D., Newton's method for computing the nearest correlation matrix with a simple upper bound, Journal on Optimization Theory and Applications, 147(2010), 546-568
[3] Li Q.N., Qi H.D.* and Xiu N.H., Block relaxation and majorization methods for the nearest correlation matrix with factor structure, Computational Optimization and Applications, 50 (2011), 327-349.
[4] Li Q.N. and Li D.H., A class of derivative-free methods for large-scale nonlinear monotone equations, IMA Journal on Numerical Analysis, 35(2011), 1625-1635.
[5] Cui C.F., Li Q.N.*, Qi L.Q. and Yan H., A quadratic penalty method for hypergraph matching, Journal of Global Optimization, 2018, 70 (1), 237-259.
[6] Yin J. and Li Q.N.*, A Semismooth Newton Method for Support Vector Classification and Regression, Computational Optimization and Applications., 2019, 73(2), 477-508
[7] Tongyao Pang, Li Q.N., and Zaiwen Wen, Zuowei Shen, Phase retrieval: a data-driven wavelet frame-based approach, Applied and Computational Harmonic Analysis, 49(2020), 971-1000, access number :20192407032578
[8] Yan Y. Q. and Li Q.N.*, An efficient augmented Lagrangian method for support vector machine, Optimization Methods and Software, 35, 2020, 855-883
[9] L. Wang, J.F. Shi, Q.N. Li, et al, Epidemiological and economic evaluation of breast cancer screening in urban population in China: a multitarget calibrated modelling study, Lancet, 2019, 394:S58.
[10] Zhao P.-F., Li Q.-N.*, Chen W.-K. and Liu Y.-F., An efficient quadratic programming relaxation-based algorithm for large-scale MIMO detection, SIAM Journal on Optimization, 2021, 31 (2), 1519-1545
[11] Shi H. and Li Q. N.*, A Facial Reduction Approach to the Single Source Localization Problem, Journal of Global Optimization, 2022, https://doi.org/10.1007/s10898-022-01188-2 < br >