Abstract
We present a numerical algorithm for solving large scale Tikhonov Regularization problems. The approach we consider introduces a splitting of the regularization functional which uses a domain decomposition, a partitioning of the solution and modified regularization functionals on each sub domain. We perform a feasibility analysis in terms of the algorithm and software scalability, to this end we use the scale-up factor which measures the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Data Assimilation problem for oceanographic models).
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Acknowledgments
This work has been realised thanks to the use of the SCoPE computing infrastructure at the University of Naples, also in the framework of PON “Rete di Calcolo per SuperB e le altre applicazioni” (ReCaS) project. This work was developed within the research activity of the H2020-MSCA-RISE-2016 NASDAC Project N. 691184.
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Arcucci, R., D’Amore, L., Celestino, S., Laccetti, G., Murli, A. (2016). A Scalable Numerical Algorithm for Solving Tikhonov Regularization Problems. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science(), vol 9574. Springer, Cham. https://doi.org/10.1007/978-3-319-32152-3_5
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DOI: https://doi.org/10.1007/978-3-319-32152-3_5
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