Book
H. Liu, J. Hu, Y. Li, Z. Wen, Computational Methods For Optimization (in Chinese), Higher Education Press. [content]
H. Liu, J. Hu, Y. Li, Z. Wen, Optimization: Modeling, Algorithm, and Theory (in Chinese), Higher Education Press. [content]
Research articles
Preprint:
S. Li, J. Hu\(^\dagger\), Stochastic Subgradient Descent at Sharply Repulsive Points: A Bounded Noise Counterexample and Gaussian Noise Avoidance, 2026. [arxiv]
S. Li, J. Hu\(^\dagger\), PLADOS: A Parameter-free Landing Algorithm for Decentralized Optimization on the Stiefel Manifold, 2026. [arxiv]
J. Hu, K. Deng, Improving the communication in decentralized manifold optimization through single-step consensus and compression, [arxiv]
Z. Deng, J. Hu\(^\dagger\), K. Deng, Z. Wen, An efficient primal dual semismooth Newton method for semidefinite programming. Mathematics of Operations Research, accepted September 2026. [arxiv]
J. Wang\(^*\), J. Hu\(^\dagger\), S. Chen, A. M.-C. So, Decentralized Weakly Convex Optimization Over the Stiefel Manifold. IEEE Transactions on Signal Processing, 2026. [publication] [arxiv]
Y. Zhang, J. Hu\(^\dagger\), Z. Lai, L. Lin, Z. Wen, Retraction-Free Optimization over the Stiefel Manifold for the LoRA Fine-Tuning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026. [publication] [arxiv]
Z. Lai, D. An, J. Hu and Z. Wen, “A Grover-compatible manifold optimization algorithm for
quantum search”. SIAM Journal on Scientific Computing, 2026. [arxiv]
G. Goldshlager, J. Hu, L. Lin, Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing. SIAM Journal on Matrix Analysis and Applications, 2026. [arxiv]
J. Zhang, J. Hu, M. Johansson, Non-convex composite federated learning with heterogeneous data. Automatica, 2026, 183, 112695. [publication] [arxiv]
K. Deng, J. Hu\(^\dagger\), Decentralized projected Riemannian gradient method for smooth optimization on compact submanifolds embedded in the Euclidean space. Numerische Mathematik, 2025. [publication] [arxiv]
K. Deng, J. Hu\(^\dagger\), J. Wu, Z. Wen, Oracle Complexities of Augmented Lagrangian Methods for Nonsmooth Composite Optimization on a Compact Submanifold. Mathematics of Operations Research, 2025. [publication] [arxiv]
J. Hu, T. Tian, S. Pan, Z. Wen, On the local convergence of the semismooth Newton method for composite optimization. Journal of Scientific Computing, 2025. [publication]
J. Hu, J. Zhang, K. Deng, Achieving Local Consensus over Compact Submanifolds. IEEE Transactions on Automatic Control, 2025. [publication]
Z. Deng, K. Deng, J. Hu\(^\dagger\), Z. Wen, An Augmented Lagrangian Primal-Dual Semismooth Newton Method for Multi-block Composite Optimization. Journal of Scientific Computing, 2025. [publication]
J. Wu, J. Hu\(^\dagger\), H. Zhang, Z. Wen, Convergence analysis of an adaptively regularized natural gradient method. IEEE Transactions on Signal Processing, 2024. [publication]
J. Hu, K. Deng, J. Wu, Q. Li, A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity. Journal of Machine Learning Research, 2024, 25(56), 1-32. [publication]
J. Hu, R. Ao, A. M.-C. So, M. Yang, Z. Wen, Riemannian Natural Gradient Methods. SIAM Journal on Scientific Computing, 2024, 46(1), A204-A231. [publication]
J. Hu, X. Liu, Z. Wen, Y. Yuan, A Brief Introduction to Manifold Optimization, Journal of the Operations Research Society of China, 2020, 8, 199-248. [publication]
J. Hu, B. Jiang, L. Lin, Z. Wen, Y. Yuan, Structured Quasi-Newton Methods for Optimization
with Orthogonality Constraints, SIAM Journal on Scientific Computing, 2019, 41(4), A2239-A2269.
[publication] [code]
J. Hu, A. Milzarek, Z. Wen, Y. Yuan. Adaptive Quadratically Regularized Newton Method
for Riemannian Optimization. SIAM Journal on Matrix Analysis and Applications, 2018, 39(3), 1181-1207.
[publication] [code]
Selected conference papers:
G. Goldshlager, J. Hu, L. Lin, A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms. International Conference on Machine Learning (ICML) 2026, [publication] [arxiv]
K. Deng, J. Jin, J. Hu, H. Wang, Adaptive Riemannian ADMM for Nonsmooth Optimization: Optimal Complexity without Smoothing. Advances in Neural Information Processing Systems 38: Proceedings of the 2025 Conference (NeurIPS 2025), 2025. [publication] [arxiv]
K. Deng, J. Hu\(^\dagger\), Decentralized projected Riemannian stochastic recursive momentum method for smooth optimization on compact submanifolds. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2025), 2025. [publication]
J. Zhang, J. Hu\(^\dagger\), A. So, M. Johansson, Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. Advances in Neural Information Processing Systems 37: Proceedings of the 2024 Conference (NeurIPS 2024), 2024. [publication]
J. Zhang, J. Hu, M. Johansson, Composite federated learning with heterogeneous data. ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. won Best Paper Award! 1/2826 [publication]
J. Wang\(^*\), J. Hu\(^*\), S. Chen, Z. Deng, A. M.-C. So, Decentralized Non-smooth Optimization Over the Stiefel Manifold. Proceedings of the 13th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2024), 2024. [publication] [arxiv]
\(^*\): Equal contribution, \(^\dagger\): Corresponding author.
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