An Improved Hierarchical ACA Technique for Sound Absorbent Materials

Alberto Milazzo; null Brancati; null Aliabadi

An Improved Hierarchical ACA Technique for Sound Absorbent Materials

Alberto Milazzo, Brancati, Aliabadi

Ingegneria

Risultato della ricerca: Article › peer review

11 Citazioni (Scopus)

Abstract

This paper presents an improved adaptive cross approximation (ACA)approach developed in conjunction with the Hierarchical format matrix and theGMRES solver. A novel scheme to generate the cluster tree (based upon preliminaryconsiderations of the prescribed boundary conditions) and an improved ACAalgorithm (approximating the system matrix for mixed Robin conditions) are described.The asymptotic smoothness property of a kernel generated by a linearcombination of two asymptotic smooth kernels is demonstrated. Numerical resultsshow the new approach to be up to 50% faster than the conventional ACAapproach.

Lingua originale	English
pagine (da-a)	1-24
Numero di pagine	24
Rivista	COMPUTER MODELING IN ENGINEERING & SCIENCES
Volume	78
Stato di pubblicazione	Published - 2011

All Science Journal Classification (ASJC) codes

Software
Modelling and Simulation
Computer Science Applications

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abstract = "This paper presents an improved adaptive cross approximation (ACA)approach developed in conjunction with the Hierarchical format matrix and theGMRES solver. A novel scheme to generate the cluster tree (based upon preliminaryconsiderations of the prescribed boundary conditions) and an improved ACAalgorithm (approximating the system matrix for mixed Robin conditions) are described.The asymptotic smoothness property of a kernel generated by a linearcombination of two asymptotic smooth kernels is demonstrated. Numerical resultsshow the new approach to be up to 50% faster than the conventional ACAapproach.",

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T1 - An Improved Hierarchical ACA Technique for Sound Absorbent Materials

AU - Milazzo, Alberto

AU - Brancati, null

AU - Aliabadi, null

PY - 2011

Y1 - 2011

N2 - This paper presents an improved adaptive cross approximation (ACA)approach developed in conjunction with the Hierarchical format matrix and theGMRES solver. A novel scheme to generate the cluster tree (based upon preliminaryconsiderations of the prescribed boundary conditions) and an improved ACAalgorithm (approximating the system matrix for mixed Robin conditions) are described.The asymptotic smoothness property of a kernel generated by a linearcombination of two asymptotic smooth kernels is demonstrated. Numerical resultsshow the new approach to be up to 50% faster than the conventional ACAapproach.

AB - This paper presents an improved adaptive cross approximation (ACA)approach developed in conjunction with the Hierarchical format matrix and theGMRES solver. A novel scheme to generate the cluster tree (based upon preliminaryconsiderations of the prescribed boundary conditions) and an improved ACAalgorithm (approximating the system matrix for mixed Robin conditions) are described.The asymptotic smoothness property of a kernel generated by a linearcombination of two asymptotic smooth kernels is demonstrated. Numerical resultsshow the new approach to be up to 50% faster than the conventional ACAapproach.

UR - http://hdl.handle.net/10447/63666

M3 - Article

VL - 78

SP - 1

EP - 24

JO - COMPUTER MODELING IN ENGINEERING & SCIENCES

JF - COMPUTER MODELING IN ENGINEERING & SCIENCES

SN - 1526-1492

ER -