Linear Algebra: A Geometric Approach, now in its second edition and written by Malcolm Adams and Ted Shifrin, presents the standard computational aspects of linear algebra. The textbook also includes a variety of intriguing interesting applications that would be interesting to motivate science and engineering students, as well as help mathematics students make the transition to more abstract advanced courses.
This textbook is an ideal book for any course covering the multifaceted and complex topic of linear algebra. The text guides students on how to think about mathematical concepts, understand how they can be applied, and help them build the skills they'll need in order to write rigorous mathematical arguments. As such, it's an ideal resource for both teaching and learning the subject.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Editions-Typ
Produkt-Hinweis
Illustrationen
Maße
Höhe: 265 mm
Breite: 210 mm
Dicke: 24 mm
Gewicht
ISBN-13
978-1-4292-1521-3 (9781429215213)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Ted Shifrin; Malcolm Adams
Preface
Foreword to the Instructor
Foreword to the Student Chapter 1. Vectors and Matrices
1. Vectors
2. Dot Product
3. Hyperplanes in Rn
4. Systems of Linear Equations and Gaussian Elimination
5. The Theory of Linear Systems
6. Some Applications Chapter 2. Matrix Algebra
1. Matrix Operations
2. Linear Transformations: An Introduction
3. Inverse Matrices
4. Elementary Matrices: Rows get Equal Time
5. The Transpose Chapter 3. Vector Spaces
1. Subspaces of Rn2. The Four Fundamental Subspaces
3. Linear Independence and Basis
4. Dimension and Its Consequences
5. A Graphic Example
6. Abstract Vector Spaces Chapter 4. Projections and Linear Transformations
1. Inconsistent Systems and Projection
2. Orthogonal Bases
3. The Matrix of a Linear Transformation and the Change-of-Basis Formula
4. Linear Transformations on Abstract Vector Spaces Chapter 5. Determinants
1. Properties of Determinants
2. Cofactors and Cramer's Rule
3. Signed Area in R2 and Signed Volume in R2 Chapter 6. Eigenvalues and Eigenvectors
1. The Characteristic Polynomial
2. Diagonalizability
3. Applications
4. The Spectral Theorem Chapter 7. Further Topics
1. Complex Eigenvalues and Jordan Canonical Form
2. Computer Graphics and Geometry
3. Matrix Exponentials and Differential Equations For Further Reading
Answers to Selected Exercises
List of Blue Boxes
Index
