• Corpus ID: 255186615

Riemannian stochastic approximation algorithms

@inproceedings{Karimi2022RiemannianSA,
  title={Riemannian stochastic approximation algorithms},
  author={Mohammad Reza Karimi and Ya-Ping Hsieh and P. Mertikopoulos and Andreas Krause},
  year={2022}
}
. We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds. Such algorithms arise naturally in the study of Riemannian optimization, game theory and optimal transport, but their behavior is much less understood compared to the Euclidean case because of the lack of a global linear structure on the manifold. We overcome this difficulty by introducing a suitable Fermi coordinate frame which allows us to map the asymptotic… 

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