The basic idea of spectral methods is to choose a finite set of functions and calculate the optimal approximation of the exact solution by these functions. These basis functions are often part of an orthonormal basis of a Hilbert space, and more specifically trigonometric functions, hence the name “spectral” methods.

Details

The following paragraph is meant as an introduction to the method for pure mathematicians with a background in functional analysis.

For illustrative purposes we will make some simplifying assumptions. Let’s assume that we have an infinite topological vector space T, its topological dual $T^*$ and a (differential) operator

$A: T \to T$

with a unique solution of the equation

$A(f) = 0$

We omit initial and boundary conditions for the moment. In order to calculate an approximation to the exact solution $f$, we need to turn the infinite dimensional problem to a finite dimensional one.

The basic idea of spectral methods is to choose a finite dimensional subspace of T spanned by a given set of functions $\{g_1, ..., g_n \}$, which are called in this context trial, expansion or approximation functions.

Claudio Canuto, M. Yousuff Hussaini, Alfio Quarteroni, Thomas A. Zang: Spectral methods. Fundamentals in single domains. (Springer 2006, ZMATH)

Claudio Canuto, M. Yousuff Hussaini, Alfio Quarteroni, Thomas A. Zang: Spectral methods. Evolution to complex geometries and applications to fluid dynamics. (Springer 2007, ZMATH)

David A. Kopriva: Implementing spectral methods for partial differential equations. Algorithms for scientists and engineers. (Springer 2009, ZMATH)

Revision from February 24, 2011 09:15:49 by
Tim van Beek