中文论著:
[1] 《凸分析讲义》，李庆娜，李萌萌，于盼盼，科学出版社，2019.1.
[2] 《多维标度分析》, 李庆娜 科学出版社 2019.4, ISBN 978-7-03-060963-2
[3] 《凸分析讲义-共轭函数及其相关函数》, 李庆娜，科学出版社，2020.12. ISBN 978-7-03-066877-6
[4] 《最优化方法》，李学文，闫桂峰，李庆娜，新葡的京集团8814登录入口出版社，2018
英文论著:
[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
