An Implementation and Evaluation of the AMLS Method for SparseEigenvalue Problems

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We describe an efficient implementation and present aperformance study of an algebraic multilevel sub-structuring (AMLS)method for sparse eigenvalue problems. We assess the time and memoryrequirements associated with the key steps of the algorithm, and compareitwith the shift-and-invert Lanczos algorithm in computational cost. Oureigenvalue problems come from two very different application areas: theaccelerator cavity design and the normal mode vibrational analysis of thepolyethylene particles. We show that the AMLS method, when implementedcarefully, is very competitive with the traditional method in broadapplication areas, especially when large numbers of eigenvalues aresought.

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Gao, Weiguo; Li, Xiaoye S.; Yang, Chao & Bai, Zhaojun February 14, 2006.

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We describe an efficient implementation and present aperformance study of an algebraic multilevel sub-structuring (AMLS)method for sparse eigenvalue problems. We assess the time and memoryrequirements associated with the key steps of the algorithm, and compareitwith the shift-and-invert Lanczos algorithm in computational cost. Oureigenvalue problems come from two very different application areas: theaccelerator cavity design and the normal mode vibrational analysis of thepolyethylene particles. We show that the AMLS method, when implementedcarefully, is very competitive with the traditional method in broadapplication areas, especially when large numbers of eigenvalues aresought.

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  • Journal Name: ACM Transactions on Mathematical Software; Journal Volume: 34; Journal Issue: 4; Related Information: Journal Publication Date: 09/19/2007

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  • Report No.: LBNL--57438
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 929006
  • Archival Resource Key: ark:/67531/metadc895918

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  • February 14, 2006

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  • Sept. 27, 2016, 1:39 a.m.

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  • Sept. 22, 2017, 3:03 p.m.

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Gao, Weiguo; Li, Xiaoye S.; Yang, Chao & Bai, Zhaojun. An Implementation and Evaluation of the AMLS Method for SparseEigenvalue Problems, article, February 14, 2006; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc895918/: accessed November 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.