Sergei Solodky, "Optimization of numerical differentiation methods. Approximation and information aspects."
- https://www.uni-giessen.de/en/faculties/f07/departments/mathematics/research_groups/numerics/advanced-seminar-in-numerical-methods/past-lectures/lecturesolodky
- 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
When
Jun 05, 2025 from 04:00 to 05:00 (Europe/Berlin / UTC200)
Where
Online
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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.