Workshop: [W4] Wavelet methods in scientific computing
organized by Stephan Dahlke and Massimo Fornasier
12-16 NOV 2012

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Wavelets are by now a well-established tool in scientifc computing, in particular for the numerical treatment of operator equations. Compared to other methods, wavelets provide the following advantages. The strong analytical properties of wavelets, in particular their ability to characterize function spaces such as Sobolev or Besov spaces, can be used to design adaptive numerical schemes that are guaranteed to converge for a huge class of problems including operators of negative order. Moreover, the vanishing moments of wavelets give rise to compression strategies for densely populated matrices. Quite recently, it has also turned out that variants of the classical wavelet algorithms (tensor wavelets, orthogonal multiwavelets) have som potential to treat high-dimensional problems. Furthermore, the treatment of inverse problems by (adaptive) wavelet algorithms is currently one of the hot topics. Therefore, the aim of this workshop is to discuss the state of the art and the further perspectives of wavelet methods in scientific computing. The topics to be discussed include, but are not limited to: Adaptive wavelet algorithms Wavelets methods for integral equations Wavelet methods for high-dimensional problems Wavelet methods for inverse and ill-posed problems

abstracts