Introduction To Computational Linear Algebra
Discusses the fundamentals needed in numerical linear algebra, including eigenvalues, vector and matrix norms, orthogonal matrices, the Gram-Schmidt process, and singular value decomposition Illustrates algorithms for eigenvalue problems with examples from population dynamics and Google matrices Covers iterative methods for solving a system of linear equations, including stationary methods based o...
Hardcover: 259 pages
Publisher: Chapman and Hall/CRC; 1 edition (June 26, 2015)
Product Dimensions: 6.2 x 0.8 x 9.2 inches
Amazon Rank: 3217945
Format: PDF ePub TXT book
- 9781482258691 epub
- 978-1482258691 epub
- Nabil Nassif epub
- Nabil Nassif books
- Science and Math epub books
matrix splitting and Krylov methods Explains the implementation of algorithms using MATLAB's syntax Expresses the numerical methods using pseudo-code or a detailed MATLAB program Includes numerous exercises and computer projects that test students' understanding of both the mathematics of numerical methods and the art of computer programming Solutions manual and figure slides available upon qualifying course adoption Summary Teach Your Students Both the Mathematics of Numerical Methods and the Art of Computer Programming Introduction to Computational Linear Algebra presents classroom-tested material on computational linear algebra and its application to numerical solutions of partial and ordinary differential equations. The book is designed for senior undergraduate students in mathematics and engineering as well as first-year graduate students in engineering and computational science. The text first introduces BLAS operations of types 1, 2, and 3 adapted to a scientific computer environment, specifically MATLAB®. It next covers the basic mathematical tools needed in numerical linear algebra and discusses classical material on Gauss decompositions as well as LU and Cholesky's factorizations of matrices. The text then shows how to solve linear least squares problems, provides a detailed numerical treatment of the algebraic eigenvalue problem, and discusses (indirect) iterative methods to solve a system of linear equations. The final chapter illustrates how to solve discretized sparse systems of linear equations. Each chapter ends with exercises and computer projects.