Linglingzhi Zhu

Postdoctoral Fellow @ Georgia Institute of Technology

Office: ISyE Main 324
Email: llzzhu at gatech.edu or lzzhuling at gmail.com

[Google Scholar]

About Me

I am a Postdoctoral Fellow at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE), Georgia Institute of Technology, working with Professor Yao Xie, and collaborating with Professor Xiuyuan Cheng at Duke University. I received my Ph.D. in Operations Research in 2024 from The Chinese University of Hong Kong (CUHK) under the supervision of Professor Anthony Man-Cho So. Before that, I received an M.S. in Computational Mathematics in 2020 and a B.S. in Mathematics in 2017 from Zhejiang University, where I was advised by Professor Chong Li. Additionally, I worked as a research assistant at The Hong Kong Polytechnic University from February to May 2019, under the mentorship of Professor Xiaoqi Yang.

Research Interests

Mathematical Optimization, Computational Data Analysis, Perturbation Theory, Nonsmooth Analysis, Inverse Problems, Statistical Inference

Publications

Preprints

  1. CoreFlow: Low-Rank Matrix Generative Models [arXiv]
    Dongze Wu, Linglingzhi Zhu, Yao Xie.
    preprint, 2026.

  2. Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints [arXiv]
    Linglingzhi Zhu, Wentao Ding, Shangyuan Liu, Anthony Man-Cho So.
    preprint, 2026.

  3. Dynamic Proximal Gradient Algorithms for Schatten-p Quasi-Norm Regularized Problems [arXiv]
    Weiping Shen^{dagger}, Linglingzhi Zhu^{dagger}, Yaohua Hu, Chong Li, Xiaoqi Yang.
    preprint, 2026.

  4. Worst-Case Generation via Minimax Optimization over Continuous Distributions [arXiv]
    (α-β) Xiuyuan Cheng, Yao Xie, Linglingzhi Zhu, Yunqin Zhu.
    preprint, 2025.

  5. Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models [arXiv]
    Linglingzhi Zhu, Jonghyeok Lee, Yao Xie.
    preprint, 2025.

  6. Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces [arXiv]
    Linglingzhi Zhu, Yao Xie.
    preprint, 2024.
    Preliminary version accepted by NeurIPS 2025 Workshop on Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making.

  7. Rotation Group Synchronization via Quotient Manifold [arXiv][slides]
    Linglingzhi Zhu, Chong Li, Anthony Man-Cho So.
    preprint, 2023.

  8. Orthogonal Group Synchronization with Incomplete Measurements: Error Bounds and Linear Convergence of the Generalized Power Method [arXiv][slides]
    Linglingzhi Zhu, Jinxin Wang, Anthony Man-Cho So.
    preprint, 2021.

Journal Articles

  1. Variance-Reduced Shuffling Algorithms for Nonconvex-Strongly Concave Minimax Problems [arXiv]
    Xia Jiang, Linglingzhi Zhu, Anthony Man-Cho So, Shisheng Cui, Jian Sun.
    Accepted by IEEE Transactions on Automatic Control, 2026.

  2. Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis [pdf]
    Jiajin Li, Linglingzhi Zhu*, Anthony Man-Cho So.
    Mathematical Programming, Series A (2025) 214(1-2):591-641.
    Preliminary version appeared in NeurIPS 2022 Workshop on Optimization for Machine Learning (OPT 2022), Oral. [pdf][slides]

  3. Locally Differentially Private Online Federated Learning With Correlated Noise [pdf]
    Jiaojiao Zhang, Linglingzhi Zhu, Dominik Fay, Mikael Johansson.
    IEEE Transactions on Signal Processing (2025) 73:1518-1531.

  4. Riemannian Linearized Proximal Algorithms for Nonnegative Inverse Eigenvalue Problem [pdf]
    (α-β) Sangho Kum, Chong Li, Jinhua Wang, Jen-Chih Yao, Linglingzhi Zhu.
    Numerical Algorithms (2023) 94:1819-1848.

  5. Linearized Proximal Algorithms with Adaptive Stepsizes for Convex Composite Optimization with Applications [pdf][slides]
    (α-β) Yaohua Hu, Chong Li, Jinhua Wang, Xiaoqi Yang, Linglingzhi Zhu.
    Applied Mathematics & Optimization (2023) 87:52.

Conference Proceedings

  1. Dual Quaternion SE(3) Synchronization with Recovery Guarantees [arXiv]
    Jianing Zhao^{dagger}, Linglingzhi Zhu^{dagger *}, Anthony Man-Cho So.
    Accepted by 43rd International Conference on Machine Learning (ICML 2026), 2026.

  2. Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization [pdf]
    Xia Jiang, Linglingzhi Zhu, Taoli Zheng, Anthony Man-Cho So.
    Proceedings of the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025).

  3. Differentially Private Online Federated Learning with Correlated Noise [pdf]
    Jiaojiao Zhang, Linglingzhi Zhu, Mikael Johansson.
    Proceedings of the 63rd IEEE Conference on Decision and Control (CDC 2024), pp. 3140-3146, 2024.

  4. LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees [pdf]
    Shangyuan Liu, Linglingzhi Zhu, Anthony Man-Cho So.
    Advances in Neural Information Processing Systems 36 (NeurIPS 2023), pp. 80142-80164, 2023.

  5. Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization [pdf]
    Taoli Zheng, Linglingzhi Zhu, Anthony Man-Cho So, José Blanchet, Jiajin Li.
    Advances in Neural Information Processing Systems 36 (NeurIPS 2023), pp. 54075-54110, 2023.

Professional Service

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