您好，欢迎访问北京理工大学机构知识库！
所在位置： 首页 - 学者导航
- 李庆娜

关键词

相关导航
按重要数据库分组
  • 任何
  • 7 SCIE
  • 13 EI工程索引(美)
  • 1 国内核心期刊
  • 8 Scopus文摘索引数据库(荷)
  • 更多
按类型分组
  • 任何
  • 3 会议论文
  • 16 期刊
  • 1 学位论文
  • 更多
按语种分组
  • 任何
  • 2 中文
  • 18 外文
优秀人才
  • 任何
年代
  • 任何
  • 1 2024
  • 8 2023
  • 8 2022
  • 1 2021
  • 1 2017
按来源期刊分组
  • 任何
  • 7 arXiv
  • 2 PACIFIC JOU..
  • 2 5th Interna..
  • 1 硕士
  • 1 Mathematica..
  • 更多
基金
  • 任何
    • 1 国家自然科学基金项目
      • 1 国家自然科学基金
  • 更多

李庆娜

  • 职称：副高级
  • 研究方向：最优化理论与算法
  • 所属院系：数学与统计学院  
  • 成果数量：20条，属于本单位的个人成果20条

共条数据
导出
[外文期刊] A fast smoothing newton method for bilevel hyperparameter optimization for SVC with logistic loss SCIE

作者： Wang, Yixin1;Li, Qingna1,2 （1Beijing Inst Technol, Sch Math & Stat, Beijing, Peoples R China.;2Beijing Inst Technol, Beijing Key Lab MCAACI, Key Lab Math Theory & Computationin Informat Secur, Beijing, Peoples R China.）

出处： OPTIMIZATION 2024

关键词： MODEL SELECTION; REGRESSION; PARAMETER

摘要： Support vector classification (SVC) with logistic loss has excellent theoretical properties in classification problems where the label values are not ...

[外文会议论文] An Adaptive Sieving Strategy for the Complex Lasso Problem EI

作者： Bai, Xiaoning1; Ye, Yuge1; Li, Qingna2

出处： 6th International Conference on Data Science and Information Technology, DSIT 2023 Shanghai, China 2023

会议录： 67-72

[外文期刊] PREFACE: SPECIAL ISSUE ON THEORY AND ALGORITHMS FOR DATA-DRIVEN OPTIMIZATION SCIE

作者： Cui, Chunfeng1;Li, Dong-Hui2;Li, Qing-Na3;Ling, Chen4 （1Beihang Univ, Sch Math Sci, LMIB Minist Educ, Beijing 100191, Peoples R China.;2South China Normal Univ, Sch Math Sci, Guangzhou 510631, Peoples R China.;3Beijing Inst Technol, Sch Math & Stat, Beijing 100081, Peoples R China.;4Hangzhou Dianzi Univ, Sch Sci, Hangzhou 310018, Peoples R China.）

出处： PACIFIC JOURNAL OF OPTIMIZATION 2023 Vol.19 No.1 PI-III

摘要： The optimization method is one of the core techniques to solve practical problems arising from science and engineering. With the rapid development of ...

[外文期刊] Global relaxation-based LP–Newton method for multiple hyperparameter selection in support vector classification with feature selection EI

作者： Li, Qingna1; Qian, Yaru1; Zemkoho, Alain2 （1School of Mathematics and Statistics, Beijing Key Laboratory on MCAACI, Key Laboratory of Mathematical Theory and Computation in Information Security, Beijing Institute of Technology, Beijing; 100081, China;2School of Mathematical Sciences, University of Southampton, Southampton; SO17 1BJ, United Kingdom）

出处： arXiv 2023

[外文期刊] The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach SCIE EI

作者： Hamza, Sakar Hasan1; Li, Qingna1 （1School of Mathematics and Statistics, Beijing Institute of Technology, Beijing; 100081, China）

出处： Energies 2023 Vol.16 No.12

[外文期刊] An Efficient Algorithm for Computational Protein Design Problem EI

作者： Zheng, Yu-Kai1; Chen, Wei-Kun2; Li, Qing-Na2 （1School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China;2School of Mathematics and Statistics, Beijing Key Laboratory on Mcaaci, Beijing Institute of Technology, Beijing, China）

出处： arXiv 2023

[外文期刊] A Fast Smoothing Newton Method for Bilevel Hyperparameter Optimization for SVC with Logistic Loss EI

作者： Wang, Yixin1; Li, Qingna1, 2 （1School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China;2Beijing Key Laboratory on MCAACI, Key Laboratory of Mathematical Theory and Computation in Information Security, Beijing Institute of Technology, Beijing, China）

出处： arXiv 2023

[外文期刊] A majorization penalty method for SVM with sparse constraint SCIE EI

作者： Lu, Sitong1;Li, Qingna2,3; （1 Beijing Inst Technol, Sch Math & Stat, Beijing, Peoples R China. ;2 Beijing Inst Technol, Sch Math & Stat, Beijing Key Lab MCAACI, Key Lab Math Theory & Computat Informat Secur, Beijing, Peoples R China. ;3 Beijing Inst Technol, Sch Math & Stat, Beijing Key Lab MCAACI, Key Lab Math Theory & Computat Informat Secur, Beijing 100081, Peoples R China.）

出处： OPTIMIZATION METHODS & SOFTWARE 2023

关键词： SUPPORT VECTOR MACHINE; CLASSIFICATION

摘要： Support vector machine (SVM) is an important and fundamental technique in machine learning. Soft-margin SVM models have stronger generalization perfor ...

[中文期刊] 中国乳腺癌自然史参数选择的优化方法 核

作者： 尹娟1;，王乐2,3;，白晓宁4;，李燕婕2;，王鑫2;，张在坤5;，李炳照4;，李扬6;，石菊芳2;，李庆娜4; （1北京理工大学管理与经济学院；2国家癌症中心国家肿瘤临床医学研究中心和中国医学科学院北京协和医学院肿瘤医院癌症早诊早治办公室；3浙江省肿瘤医院；4北京理工大学数学与统计学院MCAACI北京市重点实验室和信息安全的数学理论与计算工信部重点实验室；5香港理工大学应用数学系；6中国医学科学院北京协和医学院医学信息研究所）

出处： 中国科学(数学) 2023 第53卷 第6期 P895-913

关键词： 乳腺癌；自然史；参数选择；黄金分割法；无导数法；坐标下降法

摘要： 乳腺癌是女性最常见的恶性肿瘤之一.为了提出有效的筛查策略并评估其效果,一个基本且重要的步骤是在中国乳腺癌自然史模型中选择合适的参数,即转移概率.选择合理的转移概率有两个挑战.首先,由于乳腺癌的流行病学特性,其他国家使用的转移概率不一定适用于中国.其次,可用的筛查样本数据很少,这使得传统的基于统计的方 ...

[外文会议论文] An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines EI

作者： Su, Wen1; Li, Qingna2

出处： 5th International Conference on Data Science and Information Technology, DSIT 2022 Shanghai, China 2022

共20条记录 1/2 第一页 [1] [2] 下一页 最后一页 到第 页
李庆娜副高级简介

Educational Background

PhD (Sep . 2005-June 2010)
Computational Mathematics, College of Mathematics and Econometrics, Hunan University, China

BSc (Sep. 2001- July 2005)
Information and Computational Science, College of Mathematics and Econometrics, Hunan University, China

Working Experience

2013.7- Associate Professor, school of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China

2012.6-2013.6: Assistant Professor, school of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China

2010.8-2012.6: PostDoc in Institute of Computational Mathematics and Scientific/ Engineering Computing, Chinese Academy of Sciences, Beijing 100190, China

Teachings

Optimization methods and theories, function of complex variables, Mathematical Analysis for engineering

Publications

Visiting Positions

2013.3-2013.5 Chinese University of Hong Kong, HongKong

2010.12 National University of Singapore, Singapore

2008.1-2010.1 University of Southampton, UK

Research Projects

National Science Foundation of China (01/2012-12/2014) : Numerical Algorithm for Rank Constraint Semidefinite Programming (Grant No. 11101410)

China Postdoctoral Science Foundation (11/2011-6/2012): Numerical Methods for Schatten-p Relaxation of Matrix Rank Minimization Problems(Grant No.: 2011M500416).

Publication

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
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.
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.
Li Q.N. and Li D.H., A class of derivative-free methods for large-scale nonlinear monotone equations, IMA Journal on Numerical Analysis, 2011, DOI: 10.1093/imanum/drp015.
Li D.H. and Li Q.N., A projected semismooth Newton method for problems of calibrating least squares covariance matrix, Operations Research Letter， 39（2011），103-108
Li Q.N., Alternating direction method for a class of constrained matrix approximation problems,Pacific Journal of Optimization, 8(2012),765-778

Li Q.N., Yan H., Wu L.Q. and Wang R., Robust PCA for Ground Moving Target Indication in Wide-Area Surveillance Radar System, accepted by Journal of Operations Research Society of China, 2013

Research Interests

Optimization methods and theories, especially in matrix optimization, sparse optimization. Applications in finance, statistics, signal processing and others

Conference Talks and Invited Presentations

Conference Organization

Grants & Scholarships Assessor

International Refereed Journals

International Refereed Proceedings