Shuzhong Zhang

About

Shuzhong Zhang is a Professor and founding Department Head of the Department of Industrial and System Engineering at the University of Minnesota. He received a B.Sc. in Applied Mathematics from Fudan University in 1984 and a Ph.D. in Operations Research and Econometrics from the Tinbergen Institute, Erasmus University, in 1991. His professional background includes faculty positions at the Department of Econometrics at the University of Groningen, the Econometric Institute at Erasmus University, and the Department of Systems Engineering & Engineering Management at The Chinese University of Hong Kong, where he served as a Professor. Since 2011, he has been a faculty member at the University of Minnesota, serving as the founding Department Head from 2012 to 2018. His research interests encompass decision models within Operations Research, optimization, machine learning, and statistical learning, with activities spanning modeling, algorithm design, theory, and applications in these areas. Dr. Zhang has received numerous awards for his research and teaching, including the Erasmus University Research Prize, the SIAM Outstanding Paper Prize, and the IEEE Signal Processing Society Best Paper Award. He has served on editorial boards of prominent journals such as Operations Research and Management Science and has been actively involved in professional societies including INFORMS, SIAM, and MOS.

Research topics

• Biology
• Immunology
• Cell biology
• Cancer research
• Medicine
• Internal medicine

Selected publications

• Minimax Problems with Coupled Linear Constraints: Computational Complexity and Duality

  SIAM Journal on Optimization · 2023-10-13 · 8 citations

  articleSenior author

  .In this work we study a special minimax problem where there are linear constraints that couple both the minimization and maximization decision variables. The problem is a generalization of the traditional saddle point problem (which does not have the coupling constraint), and it finds applications in wireless communication, game theory, transportation, just to name a few. We show that the considered problem is challenging, in the sense that it violates the classical max-min inequality, and that…

  PublisherDOI

• Polar Decomposition-based Algorithms on the Product of Stiefel Manifolds with Applications in Tensor Approximation

  Journal of the Operations Research Society of China · 2023-03-25 · 8 citations

  articleSenior author
  PublisherDOI

• Primal-Dual First-Order Methods for Affinely Constrained Multi-block Saddle Point Problems

  SIAM Journal on Optimization · 2023-06-22 · 5 citations

  articleSenior author

  .We consider the convex-concave saddle point problem \(\min_{{\mathbf{x}}}\max_{{\mathbf{y}}}\Phi ({\mathbf{x}},{\mathbf{y}})\) , where the decision variables \(\mathbf{x}\) and/or \(\mathbf{y}\) are subject to certain multi-block structure and affine coupling constraints, and \(\Phi ({\mathbf{x}},{\mathbf{y}})\) possesses certain separable structure. Although the minimization counterpart of this problem has been widely studied under the topics of ADMM, this minimax problem is rarely investigate…

  PublisherDOI

• Spectrally Constrained Optimization

  Journal of Scientific Computing · 2024-08-06 · 3 citations

  articleSenior author
  PublisherDOI

• Beyond Monotone Variational Inequalities: Solution Methods and Iteration Complexities

  arXiv (Cornell University) · 2023-04-09 · 2 citations

  preprintOpen accessSenior author

  In this paper, we discuss variational inequality (VI) problems without monotonicity from the perspective of convergence of projection-type algorithms. In particular, we identify existing conditions as well as present new conditions that are sufficient to guarantee convergence. The first half of the paper focuses on the case where a Minty solution exists (also known as Minty condition), which is a common assumption in the recent developments for non-monotone VI. The second half explores alternati…

  PublisherOA PDFDOI

Recent grants

• Polynomial Optimization: Solution Methods and Applications

  NSF · $360k · 2012–2015

• Gradient Methods for Solving Big Data (Tensor) Optimization Problems

  NSF · $300k · 2015–2019

Frequent coauthors

• J.F. Sturm

  Tilburg University

  35 shared

• Bo Jiang

  35 shared

• Shiqian Ma

  Rice University

  34 shared

• Simai He

  Jilin Normal University

  26 shared

• Yongwei Huang

  Guangdong University of Technology

  26 shared

• Zhening Li

  University of Portsmouth

  24 shared

• Zhi‐Quan Luo

  23 shared

• Tianyi Lin

  Columbia University

  22 shared

Education

• Ph.D., Tinbergen Institute

  Erasmus Universiteit Rotterdam

  1991

• BS, Mathematics

  Fudan University

  1984

Awards & honors

• Erasmus University Research Prize (1999)
• CUHK Vice-Chancellor Exemplary Teaching Award (2001)
• SIAM Outstanding Paper Prize (2003)
• IEEE Signal Processing Society Best Paper Award (2010)
• 2015 SPS Signal Processing Magazine Best Paper Award

Similar researchers at University of Minnesota

• Mingyi Hongh-56
• Hui Zouh-50
• L. Jeff Hongh-49