Stochastic Approximation on Riemannian manifolds
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Abstract (arXiv)
The standard theory of stochastic approximation (SA) is extended to the case when the constraint set is a Riemannian manifold. Specifically, the standard ODE method for analyzing SA schemes is extended to iterations constrained to stay on a manifold using a retraction mapping. In addition, for submanifolds of a Euclidean space, a framework is developed for a projected SA scheme with approximate retractions. The framework is also extended to non-differentiable constraint sets.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Nov 2017.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
7 papers.
- 2021EigenGame Unloaded: When playing games is better than optimizingcited by 13
- 2022Riemannian stochastic approximation algorithmscited by 2
- 2024Stochastic Modified Flows for Riemannian Stochastic Gradient Descentcited by 2
- 2022The Symmetric Generalized Eigenvalue Problem as a Nash Equilibriumcited by 0
- 2022A Generalized EigenGame with Extensions to Multiview Representation Learningcited by 0
- 2023Tame Riemannian Stochastic Approximationcited by 0
- 2026Stochastic gradient descent with discontinuity across a manifoldcited by 0
- 2026Stochastic gradient descent with discontinuity across a manifold7 Aug 2026
- 2024Stochastic Modified Flows for Riemannian Stochastic Gradient Descent2 Feb 2024
- 2023Tame Riemannian Stochastic Approximation1 Feb 2023
- 2022A Generalized EigenGame with Extensions to Multiview Representation Learning21 Nov 2022
- 2022Riemannian stochastic approximation algorithms14 Jun 2022
- 2022The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium10 Jun 2022
- 2021EigenGame Unloaded: When playing games is better than optimizing8 Feb 2021