On the linear convergence rate of Riemannian proximal gradient method
Author:
Funder
National Research Foundation of Korea
Institute of Information & communications Technology Planning & Evaluation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11590-024-02129-6.pdf
Reference14 articles.
1. Huang, W., Wei, K.: Riemannian proximal gradient methods. Math. Progr. 194, 371–413 (2022). https://doi.org/10.1007/s10107-021-01632-3
2. Wen, H., Ke, W.: An inexact riemannian proximal gradient method. Computat. Opt. Appl. 85, 1–32 (2023). https://doi.org/10.1007/s10589-023-00451-w
3. Chen, S., Ma, S., So, A.M.-C., Zhang, T.: Proximal gradient method for nonsmooth optimization over the stiefel manifold. SIAM J. Opt. 30(1), 210–239 (2020). https://doi.org/10.1137/18M122457X
4. Genicot, M., Huang, W., Trendafilov, N.T.: Weakly correlated sparse components with nearly orthonormal loadings. In: Nielsen, F., Barbaresco, F. (eds.) Geometric Science of Information, pp. 484–490. Springer, Cham (2015)
5. Zou, H., Xue, L.: A selective overview of sparse principal component analysis. Proc IEEE 106(8), 1311–1320 (2018)
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