Separation of variables for function generated high-order tensors (Q474980)
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scientific article; zbMATH DE number 6373690
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Separation of variables for function generated high-order tensors |
scientific article; zbMATH DE number 6373690 |
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Separation of variables for function generated high-order tensors (English)
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25 November 2014
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The authors employ the previous ideas by \textit{M. Bebendorf} [Constr. Approx. 34, No. 2, 149--179 (2011; Zbl 1248.41049)] for extending adaptive cross approximation (ACA) to multivariate ACA. They separate the set of dimensions into two clusters, and repeat the procedure until the 2-way ACA method can be applied. The recursive subdivision of the set of dimensions yields a hierarchical structure, which differs from the PARATREE format.
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adaptive cross approximation
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tensor decomposition
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dimension clustering
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recursive subdivision
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