Comprehensive Guide to Krylov Subspace Methods: Provides a thorough understanding of Krylov subspace methods for solving linear systems, including conjugate gradient, GMRES, and MINRES.
Theoretical Foundations and Practical Algorithms: Covers the mathematical principles behind Krylov subspace methods and presents efficient algorithms for their implementation.
Applications in Scientific Computing: Explores the use of Krylov subspace methods in solving large-scale linear systems arising from scientific and engineering applications.
Preconditioning Techniques: Discusses preconditioning techniques to enhance the performance of Krylov subspace methods and accelerate convergence.
* MATLAB Implementation: Includes MATLAB code examples to illustrate the concepts and algorithms presented in the book, making it accessible for practical use.