Hong Zhu 朱 红

• 2016 Ph. D.  in Mathematics, Hong Kong Baptist University

• 2012 M. Sc.  in Mathematics, Henan University

• 2009 B. Sc.   in Mathematics, Henan University

• 2017 -     School of Mathematical Sciences, Jiangsu University

• 2018-2019 Faculty of Science, Simon Fraser University, Postdoctor

• Newton-type methods

• Dual quaternion: Basic theory and applications

• Matrix optimization: Basic theory and applications

• Hong Zhu, Randomized Subspace Newton-CG Method under Generalized Smoothness, submitted.

• Hong Zhu, OFF (Proximal) Newton-type Methods with Inexact Derivatives for Unconstrained Optimization, submitted. arXiv

• Hong Zhu,  On the complexity of proximal gradient and proximal gradient-Newton-CG methods for l1-regularized Optimization, submitted. arXiv

• Hong Zhu and Xun Qian, A Stochastic Block-coordinate Proximal Newton Method for Nonconvex Composite Minimization, submitted. arXiv

Newton-type Methods

• Hong Zhu, An Inexact Proximal Newton Method for Nonconvex Composite Minimization, Journal of Scientific Computing, 2025, 102 (79).

• Hong Zhu and Yunhai Xiao, A hybrid inexact regularized Newton and negative curvature method, Computational Optimization and Applications, 2024-05-12, 88(3), pp. 849--870.

Dual Quaternion

• Hong Zhu, Yuqing Shen, and Michael K. Ng, Multi-camera robot-world hand-eye calibration by solving multi-unit dual quaternion equations, CSIAM Transactions on Applied Mathematics, 7(6), pp. 1047-1079. doi: 10.4208/csiam-am.SO-2025-0052. 

• Hong Zhu and Michael K. Ng, The subspace constrained least squares solution of unit dual quaternion vector equations and its application to hand-eye calibration, Journal of Scientific Computing, 2025, 103 (49).  

Matrix Optimization and Applications

• Zhihui, Tu, Jian Lu, Hong Zhu, Wenyu Hu, Qiangtang Jiang, and Michael K. Ng, Fully-connected tensor network decomposition and group sparsity for multitemporal images cloud removal, Inverse Problems and Imaging, 2025, 19(1), 59-86.

• Hong Zhu, Chenchen Niu, and Yongjin Liang, Efficient Method for Symmetric Nonnegative Matrix Factorization with an approximate augmented Lagrangian scheme, Journal of Computational and Applied Mathematics, 2025, 454, 116218. 

• Hong Zhu and Michael K. Ng, An Inexact Majorized Proximal Alternating Direction Method of Multipliers for Diffusion Tensors, SIAM Journal on Imaging Sciences, 2024, 17(3), pp. 1795--1819.

• Hong Zhu, Xiaoxia Liu, Lin Huang, Zhaosong Lu, Jian Lu, and Michael K. Ng, Augmented Lagrangian method for tensor low-rank and sparsity models in multi-dimensional image recovery, Advances in Computational Mathematics, 2024, 50(4).

• Zhihui Tu, Jian Lu, Hong Zhu, Huan Pan, Wenyu Hu, Qiangtang Jiang and Zhaosong Lu, A new nonconvex low-rank tensor approximation method with applications to hyperspectral images denoising, Inverse Problems, 2023, 39(6).

• Hong Zhu, Michael K. Ng and Guang-jing Song, An Approximate Augmented Lagrangian Method for Nonnegative Low-Rank Matrix Approximation, Journal of Scientific Computing, 88, 2021. Correction

• Hanrui Wu, Hong Zhu, Yuguang Yan, Jiaju Wua, Yifan Zhang and Michael Kwok-Po Ng, Heterogenous Domain Adaptation by Information Capturing and Distribution Matching, IEEE Transactions on Image Processing, 30, 6364 - 6376, 2021.

• Ye Liu, Michael Kwok-Po Ng and Hong Zhu. Multiple graph semi-supervised clustering with automatic calculation of graph associations, Neurocomputing, 429, 33-46, 2021.

• Hong Zhu and Michael Kwok-Po Ng. Structured Dictionary Learning for Image Denoising under Mixed Gaussian and Impulse Noise, IEEE Transactions on Image Processing, 29, 6680-6693, 2020.

• Chuan Chen, Hui Qian, Wuhui Chen, Zibin Zheng, and Hong Zhu. Auto-weighted multi-view constrained spectral clustering, Neurocomputing, 366, 1-11, 2019. 

• Hong Zhu, Li-Zhi Liao, and Michael Kwok-Po Ng. Multi-instance dimensionality reduction via sparsity and orthogonality, Neural Computation, 30(12): 3281-3308, 2018.

• Hong Zhu, Chuan Chen, Li-Zhi Liao, and Michael Kwok-Po Ng. Multiple Graphs Clustering by Gradient Flow Method,  Journal of the Franklin Institute, 355(4): 1819-1845, 2017.

• Hong Zhu, Xiaowei Zhang, Li-Zhi Liao, and Delin Chu. Nonconvex and Nonsmooth Optimization with Generalized Orthogonality Constraints: An Approximate Augmented Lagrangian Method,  Journal of Scientific Computing, 72(1): 331-372, 2017.

Inner Point Method

• Xun Qian, Li-Zhi Liao, Jie Sun, and Hong Zhu, The convergent generalized central paths for linearly constrained convex programming, SIAM Journal on Optimization, 28(2): 1183-1204, 2018.

Sparse Optimization

• Shuhan Cao, Yunhai Xiao, and Hong Zhu, Linearized Alternating Directions Method for l1-norm Inequality Constrained l1-norm minimization, Applied Numerical Mathematics, 85: 142-153, 2014. 

• Hong Zhu, Yunhai Xiao, and Soon-Yi Wu. Large sparse signal recovery by conjugate gradient algorithm based on smoothing technique. Computers & Mathematics with Applications, 66(1): 24-32, 2013.

• Yunhai Xiao, Hong Zhu, and Soon-Yi Wu. Primal and dual alternating direction algorithms for l1-l1-norm minimization problems in compressive sensing. Computational Optimization and Applications, 54(2): 441-459, 2013.

• Yunhai Xiao, and Hong Zhu, A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing. Journal of Mathematical Analysis and Applications, 405(1): 310-319, 2013. 

• NSFC Grant, 2023.01-2026.12

• NSFC Grant, 2018.01-2020.12

• CSC, 2018.12-2019.12

• SFC Grant of Jiangsu Province, 2017.07-2020.06