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Algorithms for Sparse Linear Systems (Paperback, 1st ed. 2023) Loot Price: R1,393
Discovery Miles 13 930
Algorithms for Sparse Linear Systems (Paperback, 1st ed. 2023): Jennifer Scott, Miroslav Tuma

Algorithms for Sparse Linear Systems (Paperback, 1st ed. 2023)

Jennifer Scott, Miroslav Tuma

Series: Necas Center Series

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Loot Price R1,393 Discovery Miles 13 930 | Repayment Terms: R131 pm x 12*

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Large sparse linear systems of equations are ubiquitous in science, engineering and beyond. This open access monograph focuses on factorization algorithms for solving such systems. It presents classical techniques for complete factorizations that are used in sparse direct methods and discusses the computation of approximate direct and inverse factorizations that are key to constructing general-purpose algebraic preconditioners for iterative solvers. A unified framework is used that emphasizes the underlying sparsity structures and highlights the importance of understanding sparse direct methods when developing algebraic preconditioners. Theoretical results are complemented by sparse matrix algorithm outlines. This monograph is aimed at students of applied mathematics and scientific computing, as well as computational scientists and software developers who are interested in understanding the theory and algorithms needed to tackle sparse systems. It is assumed that the reader has completed a basic course in linear algebra and numerical mathematics.

General

Imprint: Birkhauser Verlag AG
Country of origin: Switzerland
Series: Necas Center Series
Release date: April 2023
First published: 2023
Authors: Jennifer Scott • Miroslav Tuma
Dimensions: 235 x 155mm (L x W)
Format: Paperback
Pages: 160
Edition: 1st ed. 2023
ISBN-13: 978-3-03-125819-0
Categories: Books > Science & Mathematics > Mathematics > Numerical analysis
Books > Science & Mathematics > Mathematics > Algebra > General
Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
LSN: 3-03-125819-3
Barcode: 9783031258190

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