Sergei Solodky, "Optimization of numerical differentiation methods. Approximation and information aspects."
- https://www.uni-giessen.de/de/fbz/fb07/fachgebiete/mathe/arbeitsgruppen/numerik/oberseminar-numerik/akvor/vortragsolodky
- Sergei Solodky, "Optimization of numerical differentiation methods. Approximation and information aspects."
- 2025-06-05T16:00:00+02:00
- 2025-06-05T17:00:00+02:00
Wann
05.06.2025 von 16:00 bis 17:00 (Europe/Berlin / UTC200)
Wo
digital
Termin zum Kalender hinzufügen
Abstract: We incorporate the so-called self-regularization into the numerical differentiation of bivariate functions. The proposed approach combines the truncation Legendre method and a discretization scheme using the idea of a hyperbolic cross. It is shown that numerical differentiation methods constructed in this way have a simple implementation and are optimal in terms of accuracy and amount of discrete information used.