Rivista storica italiana

• Record Nr.: UNICAMPANIASUN0000668
• Pubbl/distr/stampa: volumi ; 25 cm
• ISSN: 0035-7073
• Edizione: [A. 1, n. 1 (1884)-]
• Descrizione fisica: Trimestrale; bimestrale 1896-1901; quadrimestrale dal 1981
• Disciplina: 905
• Soggetti: Italia - Storia - Periodici
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Periodico

Algorithms for Sparse Linear Systems / / Jennifer Scott, Miroslav Tůma

• Record Nr.: UNINA9910717411903321
• Autore: Scott Jennifer
• Pubbl/distr/stampa: Cham : , : Springer International Publishing, , 2023
• ISBN: 3-031-25820-7
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (xix, 242 pages) : illustrations (some color)
• Collana: Nečas Center Series
• Disciplina: 511.8
• Soggetti: Algorithms; Matrius disperses; Sistemes lineals; Algorismes; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.