Computational scales of Sobolev norms with application to preconditioning

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Abstract
This paper provides a framework for developing computationally efficient multilevel preconditioners and representations for Sobolev norms. Specifically, given a Hilbert space and a nested sequence of subspaces , we construct operators which are spectrally equivalent to those of the form . Here , , are positive numbers and is the orthogonal projector onto with . We first present abstract results which show when is spectrally equivalent to a similarly constructed operator defined in terms of an approximation of , for . We show that these results lead to efficient preconditioners for discretizations of differential and pseudo-differential operators of positive and negative order. These results extend to sums of operators. For example, singularly perturbed problems such as can be preconditioned uniformly independently of the parameter . We also show how to precondition an operator which results from Tikhonov regularization of a problem with noisy data. Finally, we describe how the technique provides computationally efficient bounded discrete extensions which have applications to domain decomposition.

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