Publikation: Greedy Sampling using Nonlinear Optimization
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2012
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1430-3558
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Zusammenfassung
We consider the reduced basis generation in the offline stage. As an alternative for standard Greedy-training methods based upon a-posteriori error estimates on a training subset of the parameter set, we consider a nonlinear optimization combined with a Greedy method. We define an optimization problem for selecting a new parameter value on a given reduced space. This new parameter is then used --in a Greedy fashion-- to determine the corresponding snapshot and to update the reduced basis. We show the well-posedness of this nonlinear optimization problem and derive first- and second-order optimality conditions. Numerical comparisons with the standard Greedy-training method are shown.
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510 Mathematik
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URBAN, Karsten, Stefan VOLKWEIN, Oliver ZEEB, 2012. Greedy Sampling using Nonlinear Optimization. ISSN 1430-3558BibTex
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