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. Yang, J. Hu, C. Shen, A dual adaptive algorithm for matrix optimization with sparse group lasso regularization. [Researchgate]
K. Deng, J. Hu\(^\dagger\), H. Wang, Decentralized Douglas-Rachford splitting methods for smooth optimization over compact submanifolds. [arxiv]
Z. Deng, K. Deng, J. Hu\(^\dagger\), Z. Wen, An Augmented Lagrangian Primal-Dual Semismooth Newton Method for Multi-block Composite Optimization. In submission. [arxiv]
J. Hu, J. Zhang, K. Deng, Achieving Consensus over Compact Submanifolds. [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, K. Deng, N. Li, Q. Li, Decentralized Riemannian natural gradient methods with Kronecker-product approximations. [arxiv]
J. Hu, T. Tian, S. Pan, Z. Wen, On the local convergence of the semismooth Newton method for composite optimization. [arxiv]
Published:
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]
S. Kim, H. Ren, J. Charton, J. Hu, et al. Assessment of valve regurgitation severity via contrastive learning and multi-view video integration. Physics in Medicine & Biology, 2024, 69(4), 045020. [journal]
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 [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]
Z. Peng, W. Hu, J. Hu, K. Deng, Riemannian smoothing gradient type algorithms for nonsmooth optimization problem on manifolds. Applied Mathematics & Optimization, 2023, 88(3), 85. [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]
J. Hu, B. Jiang, X. Liu, Z. Wen. A note on semidefinite programming relaxations for
polynomial optimization over a single sphere. Science China Mathematics, 2016, 59(8), 1543-1560.
[journal]
\(^*\): Equal contribution, \(^\dagger\): Corresponding author.
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