Szczegóły publikacji
Opis bibliograficzny
Manifolds.jl: An extensible Julia framework for data analysis on manifolds / Seth D. Axen, Mateusz BARAN, Ronny Bergmann, Krzysztof RZECKI // ACM Transactions on Mathematical Software ; ISSN 0098-3500. — 2023 — vol. 49 no. 4 art. no. 33, s. 1–23. — Bibliogr. s. 21–23, Abstr. — Publikacja dostępna online od: 2023-09-02. — M. Baran, K. Rzecki – dod. afiliacja: Cracow University of Technology, Poland
Autorzy (4)
- Axen Seth D.
- AGHBaran Mateusz
- Bergmann Ronny
- AGHRzecki Krzysztof
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151008 |
|---|---|
| Data dodania do BaDAP | 2024-02-01 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3618296 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | ACM Transactions on Mathematical Software |
Abstract
We present the Julia package Manifolds.jl, providing a fast and easy-to-use library of Riemannian manifolds and Lie groups. This package enables working with data defined on a Riemannian manifold, such as the circle, the sphere, symmetric positive definite matrices, or one of the models for hyperbolic spaces. We introduce a common interface, available in ManifoldsBase.jl, with which new manifolds, applications, and algorithms can be implemented. We demonstrate the utility of Manifolds.jl using Bézier splines, an optimization task on manifolds, and principal component analysis on nonlinear data. In a benchmark, Manifolds.jl outperforms all comparable packages for low-dimensional manifolds in speed; over Python and Matlab packages, the improvement is often several orders of magnitude, while over C/C++ packages, the improvement is two-fold. For high-dimensional manifolds, it outperforms all packages except for Tensorflow-Riemopt, which is specifically tailored for high-dimensional manifolds.