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Numerical Linear Algebra with Applications cover

Numerical Linear Algebra with Applications

Using MATLAB and Octave

by William Ford, David Stapleton

2nd Edition

Publisher: Academic Press

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Book Details

Print ISBN9780443134760
eText ISBN9780443134777
PublisherAcademic Press
Publishing Year2025
Edition2nd Edition
LanguageEnglish
Pages678

Numerical Linear Algebra with Applications, 2nd Edition, authored by William Ford and David Stapleton, is an Academic Press textbook prepared for advanced undergraduate students, early graduate students, and researchers seeking a structured introduction to matrix computations. The text pairs theoretical mathematical principles directly with numerical algorithms and computer calculations.

The content spans foundational numerical error analysis through advanced computational routines. Specific areas of study include vector and matrix norms, floating point arithmetic, conditioning of problems, and the mathematical stability of algorithms. Subsequent chapters transition to high-level iterative framework subjects, covering basic iterative methods, Krylov subspace methods, and specialized procedures for large sparse eigenvalue problems.

To support flexible entry across different academic backgrounds, the textbook features six introductory chapters designed to establish prerequisite background concepts for readers who have not taken prior courses in applied or theoretical linear algebra. This organization enables upper-level students, graduate researchers, and practicing professionals to build the required mathematical foundation before addressing complex numerical linear systems.

Table of Contents

  1. Chapter 1: Matrices

  2. Chapter 2: Linear equations

  3. Chapter 3: Subspaces

  4. Chapter 4: Determinants

  5. Chapter 5: Eigenvalues and eigenvectors

  6. Chapter 6: Orthogonal vectors and matrices

  7. Chapter 7: Vector and matrix norms

  8. Chapter 8: Floating point arithmetic

  9. Chapter 9: Algorithms

  10. Chapter 10: Conditioning of problems and stability of algorithms

  11. Chapter 11: Gaussian elimination and the LU decomposition

  12. Chapter 12: Linear system applications

  13. Chapter 13: Important special systems

  14. Chapter 14: Gram-Schmidt decomposition

  15. Chapter 15: The singular value decomposition

  16. Chapter 16: Least-squares problems

  17. Chapter 17: Implementing the QR factorization

  18. Chapter 18: The algebraic eigenvalue problem

  19. Chapter 19: The symmetric eigenvalue problem

  20. Chapter 20: Basic iterative methods

  21. Chapter 21: Krylov subspace methods

  22. Chapter 22: Large sparse eigenvalue problems

  23. Chapter 23: Computing the singular value decomposition

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