Inertial Newton Algorithms Avoiding Strict Saddle Points
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2021
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909100755910656 |
|---|---|
| author | Castera, Camille |
| author_facet | Castera, Camille |
| contents | We study the asymptotic behavior of second-order algorithms mixing Newton's method and inertial gradient descent in non-convex landscapes. We show that, despite the Newtonian behavior of these methods, they almost always escape strict saddle points. We also evidence the role played by the hyper-parameters of these methods in their qualitative behavior near critical points. The theoretical results are supported by numerical illustrations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_04596 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Inertial Newton Algorithms Avoiding Strict Saddle Points Castera, Camille Optimization and Control Machine Learning We study the asymptotic behavior of second-order algorithms mixing Newton's method and inertial gradient descent in non-convex landscapes. We show that, despite the Newtonian behavior of these methods, they almost always escape strict saddle points. We also evidence the role played by the hyper-parameters of these methods in their qualitative behavior near critical points. The theoretical results are supported by numerical illustrations. |
| title | Inertial Newton Algorithms Avoiding Strict Saddle Points |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2111.04596 |