A Cubic Regularized Newton's Method over Riemannian Manifolds
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Abstract (arXiv)
In this paper we present a cubic regularized Newton's method to minimize a smooth function over a Riemannian manifold. The proposed algorithm is shown to reach a second-order ε-stationary point within O(1/ε^frac32) iterations, under the condition that the pullbacks are locally Lipschitz continuous, a condition that is shown to be satisfied if the manifold is compact. Furthermore, we present a local superlinear convergence result under some additional conditions.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 May 2018.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2025Scalable Second-order Riemannian Optimization for K-means Clusteringcited by 2
- 2022Riemannian Stochastic Gradient Method for Nested Composition Optimizationcited by 1
- 2022Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairnesscited by 0
- 2023Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularizationcited by 0
- 2025Scalable Second-order Riemannian Optimization for K-means Clustering25 Sep 2025
- 2023Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization22 Feb 2023
- 2022Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairness2 Sep 2022
- 2022Riemannian Stochastic Gradient Method for Nested Composition Optimization19 Jul 2022