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Hierarchical Kronecker tensor-product approximations
118
Citations
31
References
2005
Year
Spectral TheoryMathematical ProgrammingNew FormatsEngineeringMatrix FactorizationKronecker FactorsMultilinear Subspace LearningMatrix MethodComputer ScienceMatrix TheoryFunctional AnalysisMatrix AnalysisApproximation TheoryHierarchical MatricesLow-rank Approximation
The goal of this work is the presentation of some new formats which are useful for the approximation of (large and dense) matrices related to certain classes of functions and nonlocal (integral, integro-differential) operators, especially for high-dimensional problems. These new formats elaborate on a sum of few terms of Kronecker products of smaller-sized matrices (cf. [37,38]). In addition to this we need that the Kronecker factors possess a certain data-sparse structure. Depending on the construction of the Kronecker factors we are led to so-called 'profile-low-rank matrices' or hierarchical matrices (cf. [18,19]). We give a proof for the existence of such formats and expound a gainful combination of the Kronecker-tensor-product structure and the arithmetic for hierarchical matrices.
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