PDF | Introduction to Linear Algebra (5th Edition)

Introduction to Linear Algebra (5th Edition)

Format: PDF eTextbooks
ISBN-13: 978-0980232776
ISBN-10: 0980232775
Delivery: Instant Download
Authors: Gilbert Strang
Publisher: WELLESLEY -CAMBRIDGE PRESS

Gilbert Strang’s textbooks have changed the entire approach to learning linear algebra — away from abstract vector spaces to specific examples of the four fundamental subspaces: the column space and nullspace of A and A’.

This new fifth edition has become more than a textbook for the basic linear algebra course. That is its first purpose and always will be. The new chapters about applications of the SVD, probability and statistics, and Principal Component Analysis in finance and genetics, make it also a textbook for a second course, plus a resource at work. Linear algebra has become central in modern applied mathematics. This book supports the value of understanding linear algebra.

Introduction to Linear Algebra, Fifth Edition includes challenge problems to complement the review problems that have been highly praised in previous editions. The basic course is followed by eight applications: differential equations in engineering, graphs and networks, statistics, Fourier methods and the FFT, linear programming, computer graphics, cryptography, Principal Component Analysis, and singular values.

Audience: Thousands of teachers in colleges and universities and now high schools are using this book, which truly explains this crucial subject. This text is for readers everywhere, with support from the websites and video lectures. Every chapter begins with a summary for efficient review.

Contents: Chap. 1: Introduction to Vectors; Chap. 2: Solving Linear Equations; Chap. 3: Vector Spaces and Subspaces; Chap. 4: Orthogonality; Chap. 5: Determinants; Chap. 6: Eigenvalues and Eigenvectors; Chap. 7: Singular Value Decomposition; Chap. 8: Linear Transformations; Chap. 9: Complex Vectors and Matrices; Chap. 10: Applications; Chap. 11: Numerical Linear Algebra; Chap. 12: Linear Algebra in Probability and Statistics; Matrix Factorizations; Index; Six Great Theorems.