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Algorithms for sparse linear systems

Scott, Jennifer.

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Algorithms for sparse linear systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Algorithms for sparse linear systems/ by Jennifer Scott, Miroslav Tůma.
作者:	Scott, Jennifer.
其他作者:	Tůma, Miroslav.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xix, 242 p. :ill. (some col.), digital ;24 cm.
內容註:	An introduction to sparse matrices -- Sparse matrices and their graphs -- Introduction to matrix factorizations -- Sparse Cholesky sovler: The symbolic phase -- Sparse Cholesky solver: The factorization phase -- Sparse LU factorizations -- Stability, ill-conditioning and symmetric indefinite factorizations -- Sparse matrix ordering algorithms -- Algebraic preconditioning and approximate factorizations -- Incomplete factorizations -- Sparse approximate inverse preconditioners.
Contained By:	Springer Nature eBook
標題:	Sparse matrices. -
電子資源:	https://doi.org/10.1007/978-3-031-25820-6
ISBN:	9783031258206

Algorithms for sparse linear systems
Scott, Jennifer.

Algorithms for sparse linear systems[electronic resource] /by Jennifer Scott, Miroslav Tůma. - Cham :Springer International Publishing :2023. - xix, 242 p. :ill. (some col.), digital ;24 cm. - Necas center series,2523-3351. - Necas center series..

An introduction to sparse matrices -- Sparse matrices and their graphs -- Introduction to matrix factorizations -- Sparse Cholesky sovler: The symbolic phase -- Sparse Cholesky solver: The factorization phase -- Sparse LU factorizations -- Stability, ill-conditioning and symmetric indefinite factorizations -- Sparse matrix ordering algorithms -- Algebraic preconditioning and approximate factorizations -- Incomplete factorizations -- Sparse approximate inverse preconditioners.

Open access.

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.

ISBN: 9783031258206

Standard No.: 10.1007/978-3-031-25820-6doiSubjects--Topical Terms:

904745
Sparse matrices.

LC Class. No.: QA188

Dewey Class. No.: 512.9434

Algorithms for sparse linear systems
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505 0 $a An introduction to sparse matrices -- Sparse matrices and their graphs -- Introduction to matrix factorizations -- Sparse Cholesky sovler: The symbolic phase -- Sparse Cholesky solver: The factorization phase -- Sparse LU factorizations -- Stability, ill-conditioning and symmetric indefinite factorizations -- Sparse matrix ordering algorithms -- Algebraic preconditioning and approximate factorizations -- Incomplete factorizations -- Sparse approximate inverse preconditioners.
506 $a Open access.
520 $a 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.
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