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:
J. Zhang, J. Hu, M. Johansson, Composite federated learning with heterogeneous data, [arxiv]
G. Goldshlager, J. Hu, L. Lin, Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing, [arxiv]
Z. Deng, J. Hu\(^\dagger\), K. Deng, Z. Wen, An efficient primal dual semismooth Newton method for semidefinite programming, [arxiv]
K. Deng, J. Hu\(^\dagger\), J. Wu, Z. Wen, Oracle complexity of augmented Lagrangian methods for nonsmooth manifold optimization, [arxiv]
J. Hu, K. Deng, Improving the communication in decentralized manifold optimization through single-step consensus and compression, [arxiv]
K. Deng, J. Hu\(^\dagger\), Decentralized projected Riemannian gradient method for smooth optimization on compact submanifolds. [arxiv]
J. Wang\(^*\), J. Hu\(^*\), S. Chen, Z. Deng, A. M.-C. So, Decentralized Weakly Convex Optimization Over the Stiefel Manifold. [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. [journal]
J. Hu, J. Zhang, K. Deng, Achieving Local Consensus over Compact Submanifolds. IEEE Transactions on Automatic Control, 2025. [journal]
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. [journal]
J. Wu, J. Hu\(^\dagger\), H. Zhang, Z. Wen, Convergence analysis of an adaptively regularized natural gradient method. IEEE Transactions on Signal Processing, 2024. [journal]
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. [journal]
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. [journal]
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. [journal]
[journal]
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.
[journal] [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.
[journal] [code]
Selected conference papers:
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. [conf]
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. [conf]
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 [link]
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. [link]
\(^*\): Equal contribution, \(^\dagger\): Corresponding author.
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