Mingyi Hong

About

Mingyi Hong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota Twin Cities. His research focuses on contemporary issues in optimization, information processing, and training, with particular emphasis on foundation models such as language and diffusion models. His work involves addressing challenges in signal processing, wireless communication, and machine learning, contributing to the development of algorithms and methods that enhance the efficiency and effectiveness of these technologies. He holds a Ph.D. from the University of Virginia, obtained in 2011, and a B.Sc. from Zhejiang University in 2005. Mingyi Hong has received numerous honors and awards, including being named an IEEE Fellow in 2025 for his contributions to optimization in signal processing, wireless communication, and machine learning. His scholarly work includes authoring books and publishing extensively in reputable journals, where he has made significant contributions to the fields of optimization algorithms, resource allocation, and signal processing.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Mathematical optimization
• Algorithm
• Combinatorics
• Distributed computing
• Theoretical computer science
• Applied mathematics
• Pure mathematics

Selected publications

• Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization—Part I: Algorithms and Convergence Analysis

  IEEE Transactions on Signal Processing · 2020 · 258 citations

  Senior authorCorresponding

  Many contemporary signal processing, machine learning and wireless communication applications can be formulated as nonconvex nonsmooth optimization problems. Often there is a lack of efficient algorithms for these problems, especially when the optimization variables are nonlinearly coupled in some nonconvex constraints. In this work, we propose an algorithm named penalty dual decomposition (PDD) for these difficult problems and discuss its various applications. The PDD is a double-loop iterative…

  DOI

• FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data

  IEEE Transactions on Signal Processing · 2021 · 170 citations

  Federated Learning (FL) is popular for communication-efficient learning from distributed data. To utilize data at different clients without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a computation then aggregation model, in which multiple local updates are performed using local data before aggregation. These algorithms fail to work when faced with practical challenges, e.g., the local data being non-identically independently distributed. In this pa…

  DOI

• Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications

  IEEE Transactions on Signal Processing · 2020 · 132 citations

  The min-max problem, also known as the saddle point problem, is a class of optimization problems which minimizes and maximizes two subsets of variables simultaneously. This class of problems can be used to formulate a wide range of signal processing and communication (SPCOM) problems. Despite its popularity, most existing theory for this class has been mainly developed for problems with certain special convex-concave structure. Therefore, it cannot be used to guide the algorithm design for many…

  DOI

• Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances

  IEEE Signal Processing Magazine · 2020 · 109 citations

  Senior authorCorresponding

  The min-max optimization problem, also known as the <;i>saddle point problem<;/i>, is a classical optimization problem that is also studied in the context of zero-sum games. Given a class of objective functions, the goal is to find a value for the argument that leads to a small objective value even for the worst-case function in the given class. Min-max optimization problems have recently become very popular in a wide range of signal and data processing applications, such as fair beamforming, tr…

  DOI

• A Block Successive Upper-Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization

  Mathematics of Operations Research · 2020 · 33 citations

  1st authorCorresponding

  Consider the problem of minimizing the sum of a smooth convex function and a separable nonsmooth convex function subject to linear coupling constraints. Problems of this form arise in many contemporary applications, including signal processing, wireless networking, and smart grid provisioning. Motivated by the huge size of these applications, we propose a new class of first-order primal–dual algorithms called the block successive upper-bound minimization method of multipliers (BSUM-M) to solve t…

  DOI

Recent grants

• CIF: Small: A Simple and Unifying Optimization Framework for Signal and Information Processing Problems with Min-Max Structures

  NSF · $410k · 2019–2025

• CIF: Small: Collaborative Research: Optimal Provision of Backhaul and Radio Access Networks: A Cross-Network Approach

  NSF · $90k · 2017–2020

• Collaborative Research: MLWiNS: ANN for Interference Limited Wireless Networks

  NSF · $192k · 2020–2025

Frequent coauthors

• Zhi‐Quan Luo

  59 shared

• Meisam Razaviyayn

  43 shared

• Songtao Lu

  42 shared

• Tsung‐Hui Chang

  Shenzhen Research Institute of Big Data

  39 shared

• Xiangyi Chen

  37 shared

• Qingjiang Shi

  32 shared

• Xiao Fu

  Oregon State University

  29 shared

• Alfredo García

  28 shared

Awards & honors

• IEEE Fellow for “contributions to optimization in signal pro…
• IBM Pat Goldberg Memorial Award, honorable mention (2024)
• Pierre-Simon Laplace Early Career Technical Achievement Awar…
• Best Paper Award, IEEE Signal Processing Society (2021, 2022…
• Best Student Paper Award (as advisor), NeurIPS Workshop on S…

Similar researchers at University of Minnesota

• Hui Zouh-50
• L. Jeff Hongh-49
• Shuzhong Zhangh-49