Mingyi Hong

Professor Mingyi Hong

Mingyi Hong

Associate Professor, Department of Electrical and Computer Engineering


Kenneth H. Keller Hall
Room 6-109
200 Union Street Se
Minneapolis, MN 55455

Ph.D. 2011, University of Virginia, Charlottesville, United States
B.Sc. 2005, Zhejiang University, Hangzhou, China

Professional Background

Optimization; signal processing and communication; machine learning

Research Interests

Hong Research Group Site

My research focus on contemporary issues in optimization, information processing and wireless networking.

Teaching Subjects
EE 5239 Nonlinear Optimization
EE 3015 Signals and Systems
Honors and Awards

2020 IBM University Award


Mingyi Hong and Zhi-Quan Luo, “Signal Processing and Optimal Resource Allocation for the Interference Channel”, Academic Press Library in Signal Processing, Elsevier, 2013, available at [arXiv.org]

Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun and Zhi-Quan Luo “Optimization Algorithms for Big Data with Application in Wireless Networks”, Big Data Over Networks, Cambridge University Press, 2014

Selected Publications

Journal Papers

G. Zhang, X. Fu, J. Wang, X. L. Zhao, M. Hong, “Spectrum Cartography via Coupled Block-Term Tensor Decomposition”, IEEE Transactions on Signal Processing, 2020

M. Razaviyayn, T. Huang, S. Lu, M. Nouiehed, M. Sanjabi, M. Hong, “Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances”, IEEE Signal Processing Magazine, 2020; available at [arXiv.org]

S. A. H. Hosseini, B. Yaman, S. Moeller, M. Hong and M. Akcakaya, “Dense recurrent neural networks for accelerated MRI: History-cognizant unrolling of optimization algorithms” IEEE Journal of Selected Topics in Signal Processing, 2020; available at [arXiv.org]

Qingjiang Shi and Mingyi Hong, “Penalty Dual Decomposition Method For Nonsmooth Nonconvex Optimization—Part I: Algorithms and Convergence Analysis”, accepted IEEE TSP; available at [arXiv.org]

Qingjiang Shi, Mingyi Hong, Xiao Fu and Tsung-Hui Chang, “Penalty Dual Decomposition Method For Nonsmooth Nonconvex Optimization—Part II: Applications”, accepted IEEE TSP; available at [arXiv.org]

K. Tang, N. Kan, J. Zou, C. Li, X. Fu, M. Hong, H. Xiong, “Multi-user Adaptive Video Delivery over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach”, IEEE Transactions on Circuits and Systems for Video Technology, 2020

Tsung-Hui Chang, Mingyi Hong and Jong-Shi Pang, “Local Minimizers and Second-Order Conditions in Composite Piecewise Programming via Directional Derivatives", accepted Journal of Optimization Theory and Applications, 2020

Tsung-Hui Chang *, Mingyi Hong *, Hoi-To Wai *, Xinwei Zhang and Songtao Lu, “Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond”, accepted, IEEE Signal Processing Magazine, Jan, 2020. (* equal contribution),

Songtao Lu, Ioannis Tsaknakis, Mingyi Hong and Yongxin Chen, “Block Alternating Optimization for Non-Convex Min-Max Problems: Algorithms and Applications in Signal Processing and Communications”, accepted, IEEE Transactions on Signal Processing, Dec. 2019; available at [arXiv]

Haoran Sun and Mingyi Hong, “Distributed Non-Convex First-Order Optimization and Information Processing: Lower Complexity Bounds and Rate Optimal Algorithms”, accepted, IEEE Transactions on Signal Processing, July 2019; available at [arXiv.org];

S. Shen, X. Chen, M. Sadoughi, M. Hong and C. Hu, “A Deep Learning Method for Online Capacity Estimation of Lithium-Ion Batteries”, Journal of Energy Storage, accepted, 2019

M. Razaviyayn *, M. Hong *, N. Reyhanian, and Z.-Q. Luo,“A Doubly Stochastic Gauss-Seidel Algorithm for Solving Linear Equations and Certain Convex Minimization Problems”, accepted, Mathematical Programming Series B, May. 2019 (* equal contribution), available at [arXiv.org]

Mingyi Hong, Tsung-Hui Chang, Xiangfeng Wang, Meisam Razaviyayn , Shiqian Ma and Zhi-Quan Luo, “A Block Successive Upper Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization”, accepted, Mathematics of Operations Research, available at [Optimization-Online]

Davood Hajenizhad and Mingyi Hong, “Perturbed Proximal Primal Dual Algorithm for Nonconvex Nonsmooth Optimization”, Mathematical Programming Series B, Vol 176, No. 1-2, pages 207-245. 2019; see [here] for the full version with proof.

Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos, and Mingyi Hong, “Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data”, IEEE Transactions on Signal Processing, Vol. 76, No. 2, Jan. pages 306-319, 2019

Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos, Qingjiang Shi, and Mingyi Hong, “Anchor-Free Correlated Topic Modeling”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 5, pages 1056-1071, May, 2019

Haoran Sun, Xiangyi Chen, Qingjiang Shi, Mingyi Hong, Xiao Fu, and Nicholas D.Sidiropoulos, “Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management”, IEEE Transactions on Signal Processing, Vol 66, No. 20, pages 5438 – 5453, Oct. 2018; available at [arXiv.org][code].

Qingjiang Shi, Mingyi Hong, “Spectral Efficiency Optimization For Millimeter Wave Multi-User MIMO Systems”, IEEE Journal on Selected Topics in Signal Processing, Vol. 12, No. 3 pp. 455 - 468, 2018

Davood Hajinezhad, Mingyi Hong, Alfredo Garcia, “Zeroth Order Nonconvex Multi-Agent Optimization over Networks”, accepted by IEEE Transactions on Automatic Control, Jan. 2018

Xingguo Li, Tuo Zhao, Raman Aurora, Han Liu and Mingyi Hong, “On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization”, Journal of Machine Learning Research, Vol. 184, No. 18, pages 1-24, 2018

Wei-Cheng Liao, Mingyi Hong, Hamid Farmanbar, and Zhi-Quan Luo, “A Distributed Semi-Asynchronous Algorithm for Network Traffic Engineering” IEEE Transactions on Signal and Information Processing over Networks, Vol. 4, No. 3, pages 436 – 450, 2018.

Yijian Zhang, Mingyi Hong, Emiliano Dall’Anese, Sairaj Dhople, and Zi Xu, ‘‘Distributed Controllers Seeking AC Optimal Power Flow Solutions Using ADMM", IEEE Transactions on Smart Grid, Vol. 9, No. 5, Sept, 2018.

Nan Zhang, Ya-Feng Liu, Hamid Farmanbar, Tsung-Hui Chang, Mingyi Hong, and Zhi-Quan Luo, “Network Slicing for Service-Oriented Networks Under Resource Constraints”, IEEE Journal on Selected Areas in Communication, Special issue on Emerging Technologies in Software-Driven Communication, Vol. 35, No. 11, pp. 2512-2521., 2017; available at [arXiv.org]

Qingjiang Shi, Haoran Sun, Songtao Lu, Mingyi Hong and Meisam Razaviyayn, “Inexact Block Coordinate Descent Methods For Symmetric Nonnegative Matrix Factorization”, IEEE Transactions on Signal Processing, Vol. 65, No. 22, pp. 5995-6008, Nov.,2017; available at [arXiv.org] [code].

Xiao Fu, Kejun Huang, Mingyi Hong, Nicholas D. Sidiropoulos, and Anthony Man-Cho So. “Scalable and Optimal Generalized Canonical Correlation Analysis via Alternating Optimization.”, IEEE Transactions on Signal Processing, Vol. 65, No. 16, pp. 4150-4165, Aug. 2017; available at [arXiv.org]