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Two Newton methods on the manifold of fixed-rank matrices endowed with Riemannian quotient geometries

Author

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  • P.-A. Absil
  • Luca Amodei
  • Gilles Meyer

Abstract

We consider two Riemannian geometries for the manifold $${\mathcal{M }(p,m\times n)}$$ M ( p , m × n ) of all $$m\times n$$ m × n matrices of rank $$p$$ p . The geometries are induced on $${\mathcal{M }(p,m\times n)}$$ M ( p , m × n ) by viewing it as the base manifold of the submersion $$\pi :(M,N)\mapsto MN^\mathrm{T}$$ π : ( M , N ) ↦ M N T , selecting an adequate Riemannian metric on the total space, and turning $$\pi $$ π into a Riemannian submersion. The theory of Riemannian submersions, an important tool in Riemannian geometry, makes it possible to obtain expressions for fundamental geometric objects on $${\mathcal{M }(p,m\times n)}$$ M ( p , m × n ) and to formulate the Riemannian Newton methods on $${\mathcal{M }(p,m\times n)}$$ M ( p , m × n ) induced by these two geometries. The Riemannian Newton methods admit a stronger and more streamlined convergence analysis than the Euclidean counterpart, and the computational overhead due to the Riemannian geometric machinery is shown to be mild. Potential applications include low-rank matrix completion and other low-rank matrix approximation problems. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • P.-A. Absil & Luca Amodei & Gilles Meyer, 2014. "Two Newton methods on the manifold of fixed-rank matrices endowed with Riemannian quotient geometries," Computational Statistics, Springer, vol. 29(3), pages 569-590, June.
  • Handle: RePEc:spr:compst:v:29:y:2014:i:3:p:569-590
    DOI: 10.1007/s00180-013-0441-6
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    Citations

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    Cited by:

    1. P.-A. Absil & I. Oseledets, 2015. "Low-rank retractions: a survey and new results," Computational Optimization and Applications, Springer, vol. 62(1), pages 5-29, September.
    2. Marcio Antônio de A. Bortoloti & Teles A. Fernandes & Orizon P. Ferreira & Jinyun Yuan, 2020. "Damped Newton’s method on Riemannian manifolds," Journal of Global Optimization, Springer, vol. 77(3), pages 643-660, July.
    3. T. Bittencourt & O. P. Ferreira, 2017. "Kantorovich’s theorem on Newton’s method under majorant condition in Riemannian manifolds," Journal of Global Optimization, Springer, vol. 68(2), pages 387-411, June.
    4. Nickolay Trendafilov & Martin Kleinsteuber & Hui Zou, 2014. "Sparse matrices in data analysis," Computational Statistics, Springer, vol. 29(3), pages 403-405, June.
    5. Teles A. Fernandes & Orizon P. Ferreira & Jinyun Yuan, 2017. "On the Superlinear Convergence of Newton’s Method on Riemannian Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 173(3), pages 828-843, June.

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