
Numerical Linear Algebra with Applications
Using MATLAB and Octave
by William Ford, David Stapleton
2nd Edition
Publisher: Academic Press
Book Details
| Print ISBN | 9780443134760 |
| eText ISBN | 9780443134777 |
| Publisher | Academic Press |
| Publishing Year | 2025 |
| Edition | 2nd Edition |
| Language | English |
| Pages | 678 |
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
Chapter 1: Matrices
Chapter 2: Linear equations
Chapter 3: Subspaces
Chapter 4: Determinants
Chapter 5: Eigenvalues and eigenvectors
Chapter 6: Orthogonal vectors and matrices
Chapter 7: Vector and matrix norms
Chapter 8: Floating point arithmetic
Chapter 9: Algorithms
Chapter 10: Conditioning of problems and stability of algorithms
Chapter 11: Gaussian elimination and the LU decomposition
Chapter 12: Linear system applications
Chapter 13: Important special systems
Chapter 14: Gram-Schmidt decomposition
Chapter 15: The singular value decomposition
Chapter 16: Least-squares problems
Chapter 17: Implementing the QR factorization
Chapter 18: The algebraic eigenvalue problem
Chapter 19: The symmetric eigenvalue problem
Chapter 20: Basic iterative methods
Chapter 21: Krylov subspace methods
Chapter 22: Large sparse eigenvalue problems
Chapter 23: Computing the singular value decomposition
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