Numerical Methods for Large Eigenvalue Problems by Yousef Saad
Book Contents :-
1. Background in Matrix Theory and Linear Algebra
2. Sparse Matrices
3. Perturbation Theory and Error Analysis
4. The Tools of Spectral Approximation
5. Subspace Iteration
6. Krylov Subspace Methods
7. Filtering and Restarting Techniques
8. Preconditioning Techniques
9. Non-Standard Eigenvalue Problems
10. Origins of Matrix Eigenvalue Problems
About this book :-
"Numerical Methods for Large Eigenvalue Problems" by Yousef Saad is a well-known and highly respected book that focuses on solving eigenvalue problems for "large and sparse matrices", which commonly arise in scientific and engineering applications. Rather than relying on classical methods that do not scale well, the book introduces modern numerical techniques designed for efficiency, accuracy, and real computational environments.
The core strength of the book lies in its detailed treatment of "Krylov subspace methods", including the Lanczos and Arnoldi algorithms. Saad explains not only how these algorithms work, but also why they are effective, covering convergence behavior, numerical stability, and practical implementation issues. Both symmetric and nonsymmetric eigenvalue problems are discussed, making the book broadly applicable across disciplines.
This book is widely used by graduate students, researchers, and professionals working in "numerical linear algebra", computational physics, and engineering simulations. It balances mathematical insight with algorithmic clarity, making it an essential reference for anyone who needs reliable and scalable eigenvalue solvers for large-scale problems.
Book Detail :-
Title:
Numerical Methods for Large Eigenvalue Problems by Yousef Saad
Publisher:
SIAM
Year:
2011
Pages:
285
Type:
PDF
Language:
English
ISBN-10 #:
1611970725
ISBN-13 #:
9781611970722
License:
External Educational Resource
Amazon:
Amazon
About Author :-
The author
Yousef Saad
is a renowned mathematician and computer scientist best known for his contributions to "numerical linear algebra" and large-scale scientific computing. He is Professor Emeritus at the University of Minnesota and has authored influential research on efficient algorithms for solving complex matrix problems. His work focuses on "Krylov subspace methods", "iterative algorithms", and eigenvalue computations used in engineering, physics, and high-performance computing. Saad’s writing is valued for combining mathematical depth with practical implementation, making his books essential resources for graduate students and researchers.
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