16,652 research outputs found
Numerical identification of a nonlinear diffusion law via regularization in Hilbert scales
We consider the reconstruction of a diffusion coefficient in a quasilinear
elliptic problem from a single measurement of overspecified Neumann and
Dirichlet data. The uniqueness for this parameter identification problem has
been established by Cannon and we therefore focus on the stable solution in the
presence of data noise. For this, we utilize a reformulation of the inverse
problem as a linear ill-posed operator equation with perturbed data and
operators. We are able to explicitly characterize the mapping properties of the
corresponding operators which allow us to apply regularization in Hilbert
scales. We can then prove convergence and convergence rates of the regularized
reconstructions under very mild assumptions on the exact parameter. These are,
in fact, already needed for the analysis of the forward problem and no
additional source conditions are required. Numerical tests are presented to
illustrate the theoretical statements.Comment: 17 pages, 2 figure
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
We consider linear inverse problems where the solution is assumed to have a
sparse expansion on an arbitrary pre-assigned orthonormal basis. We prove that
replacing the usual quadratic regularizing penalties by weighted l^p-penalties
on the coefficients of such expansions, with 1 < or = p < or =2, still
regularizes the problem. If p < 2, regularized solutions of such l^p-penalized
problems will have sparser expansions, with respect to the basis under
consideration. To compute the corresponding regularized solutions we propose an
iterative algorithm that amounts to a Landweber iteration with thresholding (or
nonlinear shrinkage) applied at each iteration step. We prove that this
algorithm converges in norm. We also review some potential applications of this
method.Comment: 30 pages, 3 figures; this is version 2 - changes with respect to v1:
small correction in proof (but not statement of) lemma 3.15; description of
Besov spaces in intro and app A clarified (and corrected); smaller pointsize
(making 30 instead of 38 pages
On sequential multiscale inversion and data assimilation
Multiscale approaches are very popular for example for solving partial differential equations and in many applied fields dealing with phenomena which take place on different levels of detail. The broad idea of a multiscale approach is to decompose your problem into different scales or levels and to use these decompositions either for constructing appropriate approximations or to solve smaller problems on each of these levels, leading to increased stability or increased efficiency. The idea of sequential multiscale is to first solve the problem in a large-scale subspace and then successively move to finer scale spaces.
Our goal is to analyse the sequential multiscale approach applied to an inversion or state estimation problem. We work in a generic setup given by a Hilbert space environment. We work out the analysis both for an unregularized and a regularized sequential multiscale inversion. In general the sequential multiscale approach is not equivalent to a full solution, but we show that under appropriate assumptions we obtain convergence of an iterative sequential multiscale version of the method. For the regularized case we develop a strategy to appropriately adapt the regularization when an iterative approach is taken.
We demonstrate the validity of the iterative sequential multiscale approach by testing the method on an integral equation as it appears for atmospheric temperature retrieval from infrared satellite radiances
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