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© 2017

Numerical Linear Algebra: Theory and Applications

Textbook

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 1-54
  3. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 55-68
  4. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 69-92
  5. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 93-162
  6. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 163-208
  7. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 209-229
  8. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 231-248
  9. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 249-289
  10. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 291-343
  11. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 345-374
  12. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 375-405
  13. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages 407-439
  14. Larisa Beilina, Evgenii Karchevskii, Mikhail Karchevskii
    Pages E1-E1
  15. Back Matter
    Pages 441-450

About this book

Introduction

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 combined with main direct and iterative numerical methods, least squares problems, and eigen problems.Numerical algorithms illustrated by computer programs written in MATLAB® are also provided as supplementary material on SpringerLink to give the reader a better understanding of professional numerical software for the solution of real-life problems.Perfect for a one- or two-semester course on numerical linear algebra, matrix computation, and large sparse matrices, this text will interest students at the advanced undergraduate or graduate level.

Keywords

Eigenvalues Eigenvectors Large Sparse Matrices Matrix Algorithms Matrix Computatioms Numerical Linear Algebra

Authors and affiliations

  1. 1.Department of Mathematical SciencesChalmers University of Technology and University of GothenburgGothenburgSweden
  2. 2.Department of Applied MathematicsKazan Federal UniversityKazan, Tatarstan RepublicRussia
  3. 3.Department of Computational MathematicsKazan Federal UniversityKazan, Tatarstan RepublicRussia

About the authors

Larisa Beilina is an Associate Professor in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University.

Evgenii Karchevskii and Mikhail Karchevskii are both professors at the Institute of Computer Mathematics and Information Technologies at Kazan Federal University, Russia. 

Bibliographic information

Industry Sectors
Finance, Business & Banking

Reviews

 “It provides a rock-solid theoretical background in a very approachable manner, a good overview of classical algorithms of numerical linear algebra and a good framework and guidance for numerical experiments.” (Cyril Fischer, zbMATH 1396.65001, 2018)