Riemannian Stochastic Variance-Reduced Cubic Regularized Newton Method for Submanifold Optimization

Author:

Zhang Dewei,Davanloo Tajbakhsh SamORCID

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Management Science and Operations Research,Control and Optimization

Reference83 articles.

1. Absil, P.A., Baker, C.G., Gallivan, K.A.: Trust-region methods on Riemannian manifolds with applications in numerical linear algebra. In: Proceedings of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS2004), Leuven, Belgium, pp. 5–9 (2004)

2. Absil, P.A., Baker, C.G., Gallivan, K.A.: Trust-region methods on Riemannian manifolds. Found. Comput. Math. 7(3), 303–330 (2007)

3. Absil, P.A., Hosseini, S.: A collection of nonsmooth Riemannian optimization problems. In: Nonsmooth Optimization and Its Applications, pp. 1–15. Springer (2019)

4. Absil, P.A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2009)

5. Agarwal, N., Boumal, N., Bullins, B., Cartis, C.: Adaptive regularization with cubics on manifolds. Mathematical Programming pp. 1–50 (2020)

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1. Riemannian Natural Gradient Methods;SIAM Journal on Scientific Computing;2024-01-22

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