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A fast and oblivious matrix compression algorithm for Volterra integral operators

Dölz, J. ; Egger, H. ; Shashkov, V. (2024)
A fast and oblivious matrix compression algorithm for Volterra integral operators.
In: Advances in Computational Mathematics, 2021, 47 (6)
doi: 10.26083/tuprints-00023482
Artikel, Zweitveröffentlichung, Verlagsversion

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Kurzbeschreibung (Abstract)

The numerical solution of dynamical systems with memory requires the efficient evaluation of Volterra integral operators in an evolutionary manner. After appropriate discretization, the basic problem can be represented as a matrix-vector product with a lower diagonal but densely populated matrix. For typical applications, like fractional diffusion or large-scale dynamical systems with delay, the memory cost for storing the matrix approximations and complete history of the data then becomes prohibitive for an accurate numerical approximation. For Volterra integral operators of convolution type, the fast and oblivious convolution quadrature method of Schädle, Lopez-Fernandez, and Lubich resolves this issue and allows to compute the discretized evaluation with N time steps in O(N log N) complexity and only requires O(log N)active memory to store a compressed version of the complete history of the data. We will show that this algorithm can be interpreted as an H-matrix approximation of the underlying integral operator. A further improvement can thus be achieved, in principle, by resorting to H2-matrix compression techniques. Following this idea, we formulate a variant of the H2-matrix-vector product for discretized Volterra integral operators that can be performed in an evolutionary and oblivious manner and requires only O(N)operations and O(log N)active memory. In addition to the acceleration, more general asymptotically smooth kernels can be treated and the algorithm does not require a priori knowledge of the number of time steps. The efficiency of the proposed method is demonstrated by application to some typical test problems.

Typ des Eintrags: Artikel
Erschienen: 2024
Autor(en): Dölz, J. ; Egger, H. ; Shashkov, V.
Art des Eintrags: Zweitveröffentlichung
Titel: A fast and oblivious matrix compression algorithm for Volterra integral operators
Sprache: Englisch
Publikationsjahr: 30 April 2024
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 2021
Ort der Erstveröffentlichung: Dordrecht
Verlag: Springer Science
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Advances in Computational Mathematics
Jahrgang/Volume einer Zeitschrift: 47
(Heft-)Nummer: 6
Kollation: 24 Seiten
DOI: 10.26083/tuprints-00023482
URL / URN: https://tuprints.ulb.tu-darmstadt.de/23482
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Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (Abstract):

The numerical solution of dynamical systems with memory requires the efficient evaluation of Volterra integral operators in an evolutionary manner. After appropriate discretization, the basic problem can be represented as a matrix-vector product with a lower diagonal but densely populated matrix. For typical applications, like fractional diffusion or large-scale dynamical systems with delay, the memory cost for storing the matrix approximations and complete history of the data then becomes prohibitive for an accurate numerical approximation. For Volterra integral operators of convolution type, the fast and oblivious convolution quadrature method of Schädle, Lopez-Fernandez, and Lubich resolves this issue and allows to compute the discretized evaluation with N time steps in O(N log N) complexity and only requires O(log N)active memory to store a compressed version of the complete history of the data. We will show that this algorithm can be interpreted as an H-matrix approximation of the underlying integral operator. A further improvement can thus be achieved, in principle, by resorting to H2-matrix compression techniques. Following this idea, we formulate a variant of the H2-matrix-vector product for discretized Volterra integral operators that can be performed in an evolutionary and oblivious manner and requires only O(N)operations and O(log N)active memory. In addition to the acceleration, more general asymptotically smooth kernels can be treated and the algorithm does not require a priori knowledge of the number of time steps. The efficiency of the proposed method is demonstrated by application to some typical test problems.

Freie Schlagworte: Volterra integral operators, Convolution quadrature, H2-matrices, Matrix compression
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-234828
Zusätzliche Informationen:

Mathematics Subject Classification (2010): 65D20 · 45D05

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 510 Mathematik
Fachbereich(e)/-gebiet(e): 04 Fachbereich Mathematik
04 Fachbereich Mathematik > Numerik und wissenschaftliches Rechnen
Hinterlegungsdatum: 30 Apr 2024 12:40
Letzte Änderung: 08 Mai 2024 11:47
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