Abstract
Chapter 5 presents Iterative Algorithms of Solution of Linear Equations, which are better suited for large and sparse matrices. Presented algorithms include Jacobi ierations, Gauss-Seidel iterations, 3 algorithms based on Lanczos (Conjugate Gradient, Minimum Residual and Mimimum Error) for symmetric matrices, 11 algorithms based on Arnoldi (Full Orthogonalization, Generalized Minimum Residual, Generalized Minimum Error, and 8 other variants) for unsymmetric matrices, 4 special algorithms for normal equations. The algorithms are complemented by an overview of iterative algorithms and plots of convergence patterns. Four important topics: pre-conditioning, parallel computation, algebraic multigrid method and domain decomposition methods, are also discussed.
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