## 86-08 Computational methods

### Refine

#### Language

- English (3) (remove)

#### Keywords

- Multivariate Approximation (2)
- approximate identity (2)
- Approximation (1)
- Approximative Identität (1)
- Faltung (1)
- Faltung <Mathematik> (1)
- Gleichmäßige Approximation (1)
- Kompakter Träger <Mathematik> (1)
- Konvergenz (1)
- Kugel (1)

- Fast Approximation on the 2-Sphere by Optimally Localized Approximate Identities (2006)
- We introduce a method to construct approximate identities on the 2-sphere which have an optimal localization. This approach can be used to accelerate the calculations of approximations on the 2-sphere essentially with a comparably small increase of the error. The localization measure in the optimization problem includes a weight function which can be chosen under some constraints. For each choice of weight function existence and uniqueness of the optimal kernel are proved as well as the generation of an approximate identity in the bandlimited case. Moreover, the optimally localizing approximate identity for a certain weight function is calculated and numerically tested.

- Locally Supported Approximate Identities on the Unit Ball (2006)
- We present a constructive theory for locally supported approximate identities on the unit ball in \(\mathbb{R}^3\). The uniform convergence of the convolutions of the derived kernels with an arbitrary continuous function \(f\) to \(f\), i.e. the defining property of an approximate identity, is proved. Moreover, an explicit representation for a class of such kernels is given. The original publication is available at www.springerlink.com

- Spherical Fast Multiscale Approximation by Locally Compact Orthogonal Wavelets (2009)
- Using a stereographical projection to the plane we construct an O(N log(N)) algorithm to approximate scattered data in N points by orthogonal, compactly supported wavelets on the surface of a 2-sphere or a local subset of it. In fact, the sphere is not treated all at once, but is split into subdomains whose results are combined afterwards. After choosing the center of the area of interest the scattered data points are mapped from the sphere to the tangential plane through that point. By combining a k-nearest neighbor search algorithm and the two dimensional fast wavelet transform a fast approximation of the data is computed and mapped back to the sphere. The algorithm is tested with nearly 1 million data points and yields an approximation with 0.35% relative errors in roughly 2 minutes on a standard computer using our MATLAB implementation. The method is very flexible and allows the application of the full range of two dimensional wavelets.