More Books:

Linear Algebra
Language: en
Pages: 624
Authors: Ward Cheney, David Kincaid
Categories: Computers
Type: BOOK - Published: 2010-12-29 - Publisher: Jones & Bartlett Publishers

Ward Cheney and David Kincaid have developed Linear Algebra: Theory and Applications, Second Edition, a multi-faceted introductory textbook, which was motivated by their desire for a single text that meets the various requirements for differing courses within linear algebra. For theoretically-oriented students, the text guides them as they devise proofs
Numerical Linear Algebra: Theory and Applications
Language: en
Pages: 450
Authors: Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
Categories: Mathematics
Type: BOOK - Published: 2017-09-19 - Publisher: Springer

This book combines a solid theoretical background in linear algebra with practical algorithms for numerical solution of linear algebra problems. Developed from a number of courses taught repeatedly by the authors, the material covers topics like matrix algebra, theory for linear systems of equations, spectral theory, vector and matrix norms
Introduction to Linear Algebra
Language: en
Pages: 250
Authors: Peter V. O'Neil
Categories: Algebras, Linear
Type: BOOK - Published: 1979 - Publisher:

Books about Introduction to Linear Algebra
Linear Algebra: Theory and Applications
Language: en
Pages: 503
Authors: Kenneth Kuttler
Categories: Mathematics
Type: BOOK - Published: 2012-01-29 - Publisher: The Saylor Foundation

This is a book on linear algebra and matrix theory. While it is self contained, it will work best for those who have already had some exposure to linear algebra. It is also assumed that the reader has had calculus. Some optional topics require more analysis than this, however. I
Matrix Algebra
Language: en
Pages: 648
Authors: James E. Gentle
Categories: Mathematics
Type: BOOK - Published: 2017-10-21 - Publisher: Springer

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as