Logo

Numerical Methods for Large Eigenvalue Problems

Large book cover: Numerical Methods for Large Eigenvalue Problems

Numerical Methods for Large Eigenvalue Problems
by

Publisher: SIAM
ISBN/ASIN: 1611970725
ISBN-13: 9781611970722
Number of pages: 285

Description:
This book discusses numerical methods for computing eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications.

Home page url

Download or read it online for free here:
Download link
(2.2MB, PDF)

Similar books

Book cover: An Introduction to DeterminantsAn Introduction to Determinants
by
Every important principle has been illustrated by copious examples, a considerable number of which have been fully worked out. As my main object has been to produce a textbook suitable for beginners, many important theorems have been omitted.
(9355 views)
Book cover: Numerical Methods for Large Eigenvalue ProblemsNumerical Methods for Large Eigenvalue Problems
by
A comprehensive guide to computational techniques for finding eigenvalues and eigenvectors of large matrices. It emphasizes practical algorithms and software development, particularly for sparse matrices. The book covers background theory ...
(1601 views)
Book cover: n-Linear Algebra of Type IIn-Linear Algebra of Type II
by - InfoLearnQuest
This book is a continuation of the book n-linear algebra of type I. Most of the properties that could not be derived or defined for n-linear algebra of type I is made possible in this new structure which is introduced in this book.
(14353 views)
Book cover: The Hermitian Two Matrix Model with an Even Quartic PotentialThe Hermitian Two Matrix Model with an Even Quartic Potential
by - American Mathematical Society
The authors consider the two matrix model with an even quartic potential and an even polynomial potential. The main result is the formulation of a vector equilibrium problem for the limiting mean density for the eigenvalues of one of the matrices.
(7994 views)