Linear Algebra: Ideas and Applications, Fourth Edition provides a unified introduction to linear algebra while reinforcing and emphasizing a conceptual and hands-on understanding of the essential ideas. Promoting the development of intuition rather than the simple application of methods, this book successfully helps readers to understand not only how to implement a technique, but why its use is important.
CliffsQuickReview Linear Algebra demystifies the topic with straightforward explanations of the fundamentals. This comprehensive guide begins with a close look at vector algebra (including position vectors, the cross product, and the triangle inequality) and matrix algebra (including square matrices, matrix addition, and identity matrices). Once you have those subjects nailed down, you'll be ready to take on topics such asLinear systems, including Gaussian elimination and elementary row operationsReal Euclidean vector spaces, including the nullspace of a matrix, projection into a subspace, and the Rank Plus Nullity TheoremThe determinant, including definitions, methods, and Cramer’s RuleLinear transformations, including basis vectors, standard matrix, kernal and range, and compositionEigenvalues and Eigenvectors, including definitions and illustrations, Eigenspaces, and diagonalization
CliffsQuickReview Linear Algebra acts as a supplement to your textbook and to classroom lectures. Use this reference in any way that fits your personal style for study and review — the information is clearly arranged and offered in manageable units. Here are just a few of the features you’ll find in this guide:A review of core conceptsClear diagrams and loads of formulasEasy to understand definitions and explanationsPlenty of examples and detailed solutions
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Your hands-on guide to real-world applications of linear algebra
Does linear algebra leave you feeling lost? No worries —this easy-to-follow guide explains the how and the why of solving linear algebra problems in plain English. From matrices to vector spaces to linear transformations, you'll understand the key concepts and see how they relate to everything from genetics to nutrition to spotted owl extinction.Line up the basics — discover several different approaches to organizing numbers and equations, and solve systems of equations algebraically or with matrices Relate vectors and linear transformations — link vectors and matrices with linear combinations and seek solutions of homogeneous systems Evaluate determinants — see how to perform the determinant function on different sizes of matrices and take advantage of Cramer's rule Hone your skills with vector spaces — determine the properties of vector spaces and their subspaces and see linear transformation in action Tackle eigenvalues and eigenvectors — define and solve for eigenvalues and eigenvectors and understand how they interact with specific matrices
Open the book and find:Theoretical and practical ways of solving linear algebra problems Definitions of terms throughout and in the glossary New ways of looking at operations How linear algebra ties together vectors, matrices, determinants, and linear transformations Ten common mathematical representations of Greek letters Real-world applications of matrices and determinants
The third edition contains major improvements and revisions throughout the book. More than 300 new exercises have been added since the previous edition. Many new examples have been added to illustrate the key ideas of linear algebra. New topics covered in the book include product spaces, quotient spaces, and dual spaces. Beautiful new formatting creates pages with an unusually pleasant appearance in both print and electronic versions.
No prerequisites are assumed other than the usual demand for suitable mathematical maturity. Thus the text starts by discussing vector spaces, linear independence, span, basis, and dimension. The book then deals with linear maps, eigenvalues, and eigenvectors. Inner-product spaces are introduced, leading to the finite-dimensional spectral theorem and its consequences. Generalized eigenvectors are then used to provide insight into the structure of a linear operator.