The Intrinsic Riemannian Proximal Gradient Method for Nonconvex Optimization
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916790689333248 |
|---|---|
| author | Bergmann, Ronny Jasa, Hajg John, Paula Pfeffer, Max |
| author_facet | Bergmann, Ronny Jasa, Hajg John, Paula Pfeffer, Max |
| contents | We consider the proximal gradient method on Riemannian manifolds for functions that are possibly not geodesically convex. Starting from the forward-backward-splitting, we define an intrinsic variant of the proximal gradient method that uses proximal maps defined on the manifold and therefore does not require or work in the embedding. We investigate its convergence properties and illustrate its numerical performance, particularly for nonconvex or nonembedded problems that are hence out of reach for other methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09775 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Intrinsic Riemannian Proximal Gradient Method for Nonconvex Optimization Bergmann, Ronny Jasa, Hajg John, Paula Pfeffer, Max Optimization and Control Numerical Analysis Differential Geometry 90C26, 49Q99, 49M30, 65K10 We consider the proximal gradient method on Riemannian manifolds for functions that are possibly not geodesically convex. Starting from the forward-backward-splitting, we define an intrinsic variant of the proximal gradient method that uses proximal maps defined on the manifold and therefore does not require or work in the embedding. We investigate its convergence properties and illustrate its numerical performance, particularly for nonconvex or nonembedded problems that are hence out of reach for other methods. |
| title | The Intrinsic Riemannian Proximal Gradient Method for Nonconvex Optimization |
| topic | Optimization and Control Numerical Analysis Differential Geometry 90C26, 49Q99, 49M30, 65K10 |
| url | https://arxiv.org/abs/2506.09775 |