Authors: Raycho Lazarov, Shuai Lu, Sergey Pereverzev
Title of the contribution: On the balancing principle for the choice of weight in penalization methods
Abstract:
We discuss a choice of weight in penalization methods. The motivation for the use of penalization in Computational
Mathematics is to improve the conditioning of the numerical solution. One example of such improvement is a
regularization, where a penalization substitutes ill-posed problem for well-posed one. In modern numerical methods
for PDE a penalization is used, for example, to enforce a continuity of approximate solution on non-matching grids. A
choice of penalty weight should provide a balance between error components related with convergence and stability,
which are usually unknown.
In the talk we propose and analyze a simple adaptive strategy for the choice of penalty weight which does not rely on
a priori estimates of above mentioned components. It is shown that under natural assumptions the accuracy provided by
our adaptive strategy is worse only by a constant factor than one could achieve in the case of known stability and
convergence rates. Finally, we successfully apply our strategy for self-regularization of Volterra-type severely
ill-posed problems, such as sideway heat equation, and for the choice of a weight in interior penalty discontinuous
approximation on non-matching grids. Numerical experiments on a series of model problems support theoretical
results.
URL: www.ricam.oeaw.ac.at/specsem/sscm/srs_ev/nepomnyaschikh/abstracts/abstract_pereverzev.php
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