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Linear Algebra, Numerical

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Matrix computations

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Numerical linear algebra (or matrix computations) is the design and analysis of matrix operations and algorithms. Matrix computations plays a vital role in enabling the majority of computational science and engineering applications. Examples of such applications include: computational fluid dynamics, structural mechanics, fluid-structure interaction, computational electromagnetics, image processing, web search (PageRank), and information retrieval, just to list a few. Matrix computations can be divided into two classes: (a) dense matrix computations, and (b) sparse matrix computations, with the former being rich in data locality and hence can readily achieve high performance on modern architectures. The basic standard problems in numerical linear algebra are: (i) solving linear systems of equations, (ii) solving linear least squares problems with or without constraints, (iii) solving standard and generalized eigenvalue problems, and (iv)...

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Bibliography

  1. Blackford L S, Choi J, Cleary A, D’Azevedo E, Demmel J, Dhillon I, Dongarra J, Hammarling S, Henry G, Petitet A, Stanley K, Walker D, Whaley RC (1996) ScaLAPACK Users’ Guide. Society for Industrial and Applied Mathematics, available on line at http://www.netlib.org/lapack/lug/

  2. Dongarra JJ, Duff IS, Sorensen DC, Van der Vorst H (1998) Numerical Linear Algebra for High Performance Computers. SIAM, Philadelphia

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  3. Gallivan KA, Plemmons RJ, Sameh AH (1990) Parallel algorithms for dense linear algebra computations. In: Gallivan KA, Heath MT, Ng E, Ortega JM, Peyton BW, Plemmons RJ, Romine CH, Sameh AH, Voigt RG (ed) Parallel algorithms computations. SIAM, Philadelphia, PA

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  4. Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. John Hopkins, New York

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© 2011 Springer Science+Business Media, LLC

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Philippe, B., Sameh, A. (2011). Linear Algebra, Numerical. In: Padua, D. (eds) Encyclopedia of Parallel Computing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09766-4_126

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