Faculty

Current position： Home Faculty List By job title All of it Professor Content
Hu Yaohua

  • Educational level:

  • Professional titles: Professor

  • Telephone:0755-26538922

  • Email:

  • Address:Room 1409, Huiwen Building

Educational level Professional titles Professor
Professional titles 0755-26538922 Email
Address Room 1409, Huiwen Building Personal Profile
Educational experience Work experience
Research Field Mathematical optimization theory, algorithms and applications; Sparse optimization, generalized convex optimization, composite optimization; Machine learning, big data analytics, systems biology Honors obtained
Academic Programs Scientific research [1]Y. Hu, G. Li, C. K. W. Yu and T. L. Yip, Quasi-convex feasibility problems: Subgradient methods and convergence rates, European Journal of Operational Research, 298(1): 45-58, 2022.
[2]J. Qin, Y. Hu, J.-C. Yao, R. W. T. Leung, Y. Zhou, Y. Qin and J. Wang, Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data, Briefings in Bioinformatics, 22(6): bbab311, 2021.
[3]Y. Hu, J. Li and C. K. W. Yu, Convergenece rates of subgradient methods for quasi-convex optimization problems, Computational Optimization and Applications, 77(1): 183-212, 2020.
[4]J. Wang, Y. Hu, C. K. W. Yu, C. Li and X. Yang, Extended Newton methods for multiobjective optimization: Majorizing function technique and convergence analysis, SIAM Journal on Optimization, 29(3): 2388-2421, 2019.
[5]C. Li, L. Meng, L. Peng, Y. Hu and J.-C. Yao, Weak sharp minima for convex infinite optimization problems in normed linear spaces, SIAM Journal on Optimization, 28(3): 1999-2021, 2018.
[6]J. Wang, Y. Hu, C. Li and J.-C. Yao, Linear convergence of CQ algorithms and applications in gene regulatory network inference, Inverse Problems, 33(5): 055017, 2017.
[7]Y. Hu, C. Li, K. Meng, J. Qin and X. Yang, Group sparse optimization via L(p,q) regularization, Journal of Machine Learning Research, 18(30): 1-52, 2017.
[8]Y. Hu, C. Li and X. Yang, On convergence rates of linearized proximal algorithms for convex composite optimization with applications, SIAM Journal on Optimization, 26(2):1207-1235, 2016.
[9]Y. Hu, X. Yang and C.-K. Sim, Inexact subgradient methods for quasi-convex optimization problems, European Journal of Operational Research, 240(2): 315-327, 2015.
[10]C. Li, X. P. Zhao and Y. Hu, Quasi-Slater and Farkas–Minkowski qualifications for semi-infinite programming with applications, SIAM Journal on Optimization, 23(4): 2208-2230, 2013.

Personal Profile

Shenzhen University Professor, Associate Dean, Professor, part-time doctoral advisor, Hong Kong Polytechnic University. He was awarded the National Outstanding Youth Science Foundation and the Shenzhen Outstanding Youth Science Foundation, and was selected as one of Lai Chi Kok Amusement Park's top overseas talents and Shenzhen University. He is mainly engaged in the research of mathematical optimization theory, algorithm and application, and has made a series of academic achievements in non-convex sparse optimization method and cross-disciplinary application. Optim. ♪ Inverse probl. Come on, J. Mach. Learn. Res. , brief. Bioinform. He has published more than 40 academic papers in international academic journals, granted three national invention patents, developed several bioinformatics kits and web servers, and presided over four projects funded by the National Natural Science Foundation of China, more than 10 provincial and municipal scientific research projects. Personal homepage: The https://mayhhu.gitlab.io  invites the master doctorate graduate student who the Mathematics Foundation is solid, welcome to consult and applies for an examination. Long-term recruitment of researchers/postdocs, Welcome to contact the application.

Educational experience

Work experience

Research Field

  • Mathematical optimization theory, algorithms and applications; Sparse optimization, generalized convex optimization, composite optimization; Machine learning, big data analytics, systems biology

Honors obtained

Academic Programs

Scientific research

  • [1]Y. Hu, G. Li, C. K. W. Yu and T. L. Yip, Quasi-convex feasibility problems: Subgradient methods and convergence rates, European Journal of Operational Research, 298(1): 45-58, 2022. [2]J. Qin, Y. Hu, J.-C. Yao, R. W. T. Leung, Y. Zhou, Y. Qin and J. Wang, Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data, Briefings in Bioinformatics, 22(6): bbab311, 2021. [3]Y. Hu, J. Li and C. K. W. Yu, Convergenece rates of subgradient methods for quasi-convex optimization problems, Computational Optimization and Applications, 77(1): 183-212, 2020. [4]J. Wang, Y. Hu, C. K. W. Yu, C. Li and X. Yang, Extended Newton methods for multiobjective optimization: Majorizing function technique and convergence analysis, SIAM Journal on Optimization, 29(3): 2388-2421, 2019. [5]C. Li, L. Meng, L. Peng, Y. Hu and J.-C. Yao, Weak sharp minima for convex infinite optimization problems in normed linear spaces, SIAM Journal on Optimization, 28(3): 1999-2021, 2018. [6]J. Wang, Y. Hu, C. Li and J.-C. Yao, Linear convergence of CQ algorithms and applications in gene regulatory network inference, Inverse Problems, 33(5): 055017, 2017. [7]Y. Hu, C. Li, K. Meng, J. Qin and X. Yang, Group sparse optimization via L(p,q) regularization, Journal of Machine Learning Research, 18(30): 1-52, 2017. [8]Y. Hu, C. Li and X. Yang, On convergence rates of linearized proximal algorithms for convex composite optimization with applications, SIAM Journal on Optimization, 26(2):1207-1235, 2016. [9]Y. Hu, X. Yang and C.-K. Sim, Inexact subgradient methods for quasi-convex optimization problems, European Journal of Operational Research, 240(2): 315-327, 2015. [10]C. Li, X. P. Zhao and Y. Hu, Quasi-Slater and Farkas–Minkowski qualifications for semi-infinite programming with applications, SIAM Journal on Optimization, 23(4): 2208-2230, 2013.

Copyright School of Mathematical Sciences，Shenzhen University

You Are No  Visitors