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In order to treat very large problems, the structure of hierarchical matrices can be improved:
H<sup>2</sup>-matrices
<ref name="HAKHSA02">{{cite journal|last=Hackbusch|first=Wolfgang|last2=Khoromskij|first2=Boris N.|last3=Sauter|first3=Stefan|date=2002|title=On H<sup>2</sup>-matrices|journal=Lectures on Applied Mathematics|pages=
<ref name="BO10b">{{cite book|last=Börm|first=Steffen|date=2010|title=Efficient Numerical Methods for Non-local Operators: H<sup>2</sup>-Matrix Compression, Algorithms and Analysis|publisher=EMS Tracts in Mathematics|url=http://www.ems-ph.org/books/book.php?proj_nr=125}}</ref>
replace the general low-rank structure of the blocks by a hierarchical representation closely related to the
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In the context of boundary integral operators, replacing the fixed rank <math>k</math> by block-dependent ranks
leads to approximations that preserve the rate of convergence of the underlying boundary element method
at a complexity of <math>O(n).</math><ref name="SA00">{{cite journal|last=Sauter|first=Stefan|date=2000|title=Variable order panel clustering|journal=Computing|volume=64|pages=
<ref name="BOSA05">{{cite journal|last=Börm|first=Steffen|last2=Sauter|first2=Stefan|date=2005|title=BEM with linear complexity for the classical boundary integral operators|journal=Math. Comp.|volume=74|pages=
Arithmetic operations like multiplication, inversion, Cholesky or LR factorization of H<sup>2</sup>-matrices
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and the low-rank update of submatrices.
While the matrix-vector multiplication is straightforward, implementing efficient low-rank updates with
adaptively optimized cluster bases poses a significant challenge<ref name="HARE14">{{cite journal|last=Börm|first=Steffen|last2=Reimer|first2=Knut|date=2015|title=Efficient arithmetic operations for rank-structured matrices based on hierarchical low-rank updates|journal=Comp. Vis. Sci.|volume=16|issue=6|pages=
== Literature ==
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