 Название: Algorithms for Sparse Linear Systems
Автор: Jennifer Scott, Miroslav Tuma
Издательство: Springer
Год: 2023
Страниц: 254
Язык: английский
Формат: pdf (true), epub
Размер: 15.5 MB
Large sparse linear systems of equations are ubiquitous in science, engineering and beyond. This 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.
Contents:
1. An Introduction to Sparse Matrices
2. Sparse Matrices and Their Graphs
3. Introduction to Matrix Factorizations
4. Sparse Cholesky Solver: The Symbolic Phase
5. Sparse Cholesky Solver: The Factorization Phase
6. Sparse LU Factorizations
7. Stability, Ill-Conditioning, and Symmetric Indefinite Factorizations
8. Sparse Matrix Ordering Algorithms
9. Algebraic Preconditioners and Approximate Factorizations
10. Incomplete Factorizations
11. Sparse Approximate Inverse Preconditioners
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Algorithms for Sparse Linear Systems 
