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Mathematics of Computation

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Adaptive wavelet algorithms for elliptic PDE's on product domains

Authors: Christoph Schwab and Rob Stevenson
Journal: Math. Comp. 77 (2008), 71-92
MSC (2000): Primary 41A25, 41A46, 41A63, 65D32, 65N12, 65T60
Published electronically: May 14, 2007
MathSciNet review: 2353944
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Abstract: With standard isotropic approximation by (piecewise) polynomials of fixed order in a domain $ D\subset \mathbb{R}^d$, the convergence rate in terms of the number $ N$ of degrees of freedom is inversely proportional to the space dimension $ d$. This so-called curse of dimensionality can be circumvented by applying sparse tensor product approximation, when certain high order mixed derivatives of the approximated function happen to be bounded in $ L_2$. It was shown by Nitsche (2006) that this regularity constraint can be dramatically reduced by considering best $ N$-term approximation from tensor product wavelet bases. When the function is the solution of some well-posed operator equation, dimension independent approximation rates can be practically realized in linear complexity by adaptive wavelet algorithms, assuming that the infinite stiffness matrix of the operator with respect to such a basis is highly compressible. Applying piecewise smooth wavelets, we verify this compressibility for general, non-separable elliptic PDEs in tensor domains. Applications of the general theory developed include adaptive Galerkin discretizations of multiple scale homogenization problems and of anisotropic equations which are robust, i.e., independent of the scale parameters, resp. of the size of the anisotropy.

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Additional Information

Christoph Schwab
Affiliation: Seminar for Applied Mathematics, ETHZ HG G58.1, ETH Zürich, CH 8092 Zürich, Switzerland

Rob Stevenson
Affiliation: Department of Mathematics, Utrecht University, P.O. Box 80.010, NL-3508 TA Utrecht, The Netherlands

Keywords: Elliptic PDE's on (high) dimensional product domains, multiple scale homogenization, tensor product approximation, sparse grids, wavelets, best $N$-term approximation, optimal computational complexity, matrix compression
Received by editor(s): May 8, 2006
Received by editor(s) in revised form: December 6, 2006
Published electronically: May 14, 2007
Additional Notes: This work was performed in part in the IHP network “Breaking Complexity” of the EC under contract HPRN-CT-2002-00286 and supported by the Netherlands Organization for Scientific Research and by the Swiss BBW under grant BBW 02.0418
Article copyright: © Copyright 2007 American Mathematical Society
The copyright for this article reverts to public domain 28 years after publication.

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