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Yaohua Hu - Publications

Preprints

  1. X. Wang, Y. Hu*, C. Li and S.-M. Guu, Linear convergence of subgradient algorithm for convex feasibility problems with applications, submitted to Journal of Optimization Theory and Applications.

  2. X. Hu, K. Zhang, Y. Hu* and X. Yang, Structured sparse optimization via hard thresholding pursuit, submitted to IEEE Transactions on Signal Processing.

  3. Y. Hu, J. Lu, X. Yang and K. Zhang, Mix sparse optimization: Theory and application, submitted to Journal of Machine Learning Research.

Journal Papers

  1. Y. Hu, X. Hu and X. Yang, On convergence of iterative thresholding algorithms to approximate sparse solution for composite nonconvex optimization, Accepted in Mathematical Programming,2024. [link]

  2. Y. Hu, J. Li, Y. Liu and C. K. W. Yu, Quasi-subgradient methods with Bregman distance for quasi-convex feasibility problems, Accepted in Journal of Nonlinear and Variational Analysis, 2024. [link]

  3. Y. Zhou, Y. Zhang, M. Peng, Y. Zhang, C. Li, L. Shu, Y. Hu, J. Su and J. Xu, scDMV: a zero–one inflated beta mixture model for DNA methylation variability with scBS-seq data, Bioinformatics, 40(1): btad772, 2024. [link]

  4. Y. Hu, C. Li, J. Wang, X. Yang and L. Zhu, Linearized proximal algorithms with adaptive stepsizes for convex composite optimization with applications, Applied Mathematics & Optimization, 87: 52, 2023. [link]

  5. X. Li, Y. Hu*, C. Li, X. Yang and T. Jiang, Sparse estimation via lower-order penalty optimization methods in high-dimensional linear regression, Journal of Global Optimization, 85: 315-349, 2023. [link]

  6. R. W. T. Leung#, X. Jiang#, X. Zong#, Y. Zhang, X. Hu, Y. Hu* and J. Qin*, CORN - Condition Orientated Regulatory Networks: bridging conditions to gene networks, Briefings in Bioinformatics, 23(6): bbac402, 1–9, 2022. [link]

  7. C. Wang#, X. Zong#, F. Wu, R. W. T. Leung, Y. Hu* and J. Qin*, DNA and RNA binding proteins linked transcriptional control and alternative splicing together in a two-layer regulatory network system, Frontiers in Molecular Biosciences, 9: 920492, 2022. [link]

  8. R. S. Burachik, Y. Hu and X. Yang*, Interior quasi-subgradient method with non-Euclidean distances for constrained quasi-convex optimization problems in Hilbert spaces, Journal of Global Optimization, 83(2): 249-271, 2022. [link]

  9. Y. Hu, G. Li*, M. Li and C. K. W. Yu, Multiple-sets split quasi-convex feasibility problems: Adaptive subgradient methods with convergence guarantee, Journal of Nonlinear and Variational Analysis, 6(2): 15-33, 2022. [link]

  10. Y.-J. Wang, Y.-H. Kuo*, G. Q. Huang, W. Gu and Y. Hu, Dynamic demand-driven bike station clustering, Transportation Research Part E: Logistics and Transportation Review, 160: 102656, 2022. [link]

  11. G. Li, M. Li and Y. Hu*, Stochastic quasi-subgradient method for stochastic quasi-convex feasibility problems, Discrete & Continuous Dynamical Systems - Series S, 15(4): 713-725, 2022. [link]

  12. 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. [link] [supplementary material]

  13. H. Wang*, F. Zhang, Y. Shi and Y. Hu, Nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods, Journal of Global Optimization, 81(3): 717–748, 2021. [link]

  14. 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, 1-16, 2021. [link]

  15. Y. Zhou, J. Liang, Y. Hu and H. Lian*, Random projections for quantile ridge regression, Stat, 10(1): e386, 2021. [link]

  16. Y. Hu, C. Li, K. Meng* and X. Yang, Linear convergence of inexact descent methods and inexact proximal gradient algorithms for lower-order regularization problems, Journal of Global Optimization, 79(4): 853-883, 2021. [link]

  17. L. Zhang, W. Gu*, L. Fu, Y. Mei and Y. Hu, A two-stage heuristic approach for fleet management optimization under time-varying demand, Transportation Research Part E: Logistics and Transportation Review, 147: 102268, 2021. [link]

  18. X. Li, L. Cai, J. Li, C. K. W. Yu, and Y. Hu*, A survey of clustering methods via optimization methodology, Journal of Applied and Numerical Optimization, 3(1): 151-174, 2021. [link]

  19. X. Hu, Y. Hu, F. Wu, R. W. T. Leung and J. Qin*, Integration of single-cell multi-omics for gene regulatory network inference, Computational and Structural Biotechnology Journal, 18: 1925-1938, 2020. [link]

  20. Y. Hu, C. Li, Y. Liu and M. Li*, The effect of deterministic noise on quasi-subgradient method for quasi-convex feasibility problems, Journal of Applied and Numerical Optimization, 2(2): 235-247, 2020. [link]

  21. 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. [link]

  22. K. Zhang*, X. Yang and Y. Hu, Power penalty method for solving HJB equations arising from finance, Automatica, 111: 108668, 2020. [link]

  23. Y. Hu*, C. K. W. Yu and X. Yang, Incremental quasi-subgradient methods for minimizing the sum of quasi-convex functions, Journal of Global Optimization, 75(4): 1003-1028, 2019. [link]

  24. 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. [link]

  25. J. Bao, C. K. W. Yu, J. Wang*, Y. Hu and J.-C. Yao, Modified inexact Levenberg-Marquardt methods for solving nonlinear least squares problems, Computational Optimization and Applications, 74: 547-582, 2019. [link]

  26. J. Wang, Y. Hu*, C. K. W. Yu and X. Zhuang, A family of projection gradient methods for solving the multiple-sets split feasibility problem, Journal of Optimization Theory and Applications, 183(2): 520-534, 2019. [link]

  27. C. K. W. Yu, Y. Hu, X. Yang* and S. K. Choy, Abstract convergence theorem for quasi-convex optimization problems with applications, Optimization, 68(7): 1289-1304, 2019. [link]

  28. W. Shen, Y. Hu*, C. Li and J.-C. Yao, Convergence of the Newton-type methods for the square inverse singular value problems with multiple and zero singular values, Applied Numerical Mathematics, 143: 172-187, 2019. [link]

  29. J. Bao, S.-M. Guu*, J. Wang, Y. Hu and C. Li, On convergence of a truncated Gauss-Newton method for solving underdetermined nonlinear least squares problems, Journal of Nonlinear and Convex Analysis, 19(12): 2235-2246, 2018. [link]

  30. S. Liu, H. Wang, Y. Hu*, Sparse optimization and regularization methods, Mathematical Modeling and its Applications, 7(4): 1-15, 2018. [link]

  31. L. Zhang, Y. Hu*, C. K. W. Yu and J. Wang, Iterative positive thresholding algorithm for non-negative sparse optimization, Optimization, 67(9): 1345-1363, 2018. [link]

  32. W. Shen*, Y. Hu, C. Li and J.-C. Yao, Convergence of a Ulm-like method for square inverse singular value problems with multiple and zero singular values, Numerical Algorithms, 79(2): 375-398, 2018. [link]

  33. 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. [link]

  34. Y. Zhang, Y. Hu*, C. K. W. Yu and J. Wang, Cubic convergence of Newton-Steffensen's method for operators with Lipschitz continuous derivative, Journal of Nonlinear and Convex Analysis, 19(3): 433-460, 2018. [link]

  35. J. Qin*, Y. Hu, K. Y. Ma, X. Jiang, C. H. Ho, L. M. Tsang, L. Yi, R. W. T. Leung and K. H. Chu*, CrusTF: A comprehensive resource for evolutionary and functional studies of crustacean transcription factors, BMC Genomics, 18(1): 908, 2017. [link]

  36. L. Zhang, L. Fu, W. Gu*, Y. Ouyang and Y. Hu, A general iterative approach for the system-level joint optimization of pavement maintenance, rehabilitation, and reconstruction planning, Transportation Research Part B: Methodological, 105: 378-400, 2017. [link]

  37. L. Zhang, Y. Hu*, C. Li and J.-C. Yao, A new linear convergence result for iterative soft thresholding algorithm, Optimization, 66(7): 1177-1189, 2017. [link]

  38. 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. [link]

  39. 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. [link]

  40. Y. Hu, X. Yang* and C. K. W. Yu, Subgradient methods for saddle point problems of quasiconvex optimization, Pure and Applied Functional Analysis, 2(1): 83-97, 2017. [link]

  41. J. Qin, B. Yan, Y. Hu, P. Wang, J. Wang*, Applications of integrative OMICs approaches to gene regulation studies, Quantitative Biology, 4(4): 283-301, 2016. [link]

  42. Y. Hu*, C. K. W. Yu, C. Li and X. Yang, Conditional subgradient methods for constrained quasi-convex optimization problems, Journal of Nonlinear and Convex Analysis, 17(10): 2143-2158, 2016. [link]

  43. 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. [link]

  44. Y. Hu*, C. K. W. Yu and C. Li, Stochastic subgradient method for quasi-convex optimization problems, Journal of Nonlinear and Convex Analysis, 17(4): 711-724, 2016. [link]

  45. Y. Hu, C.-K. Sim and X. Yang*, A subgradient method based on gradient sampling for solving convex optimization problems, Numerical Functional Analysis and Optimization, 36(12):1559-1584, 2015. [link]

  46. B. Tian, Y. Hu and X. Yang*, A box-constrained differentiable penalty method for nonlinear complementarity problems, Journal of Global Optimization, 62(4):729-747, 2015. [link]

  47. 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. [link]

  48. J. Qin#, Y. Hu#, F. Xu, H. K. Yalamanchili and J. Wang*, Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods, Methods, 67: 294-303, 2014. [link]

  49. 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. [link]