Linear Algebra with Applications

Prepare for conceptual thinking in an abstract setting as Linear Algebra with Applications blends computational and conceptual topics through a large assortment of examples to improve your comfort with the rigors of the subject matter.

Jeff Holt has a B.A. from Humboldt State University and a Ph.D. from the University of Texas. He has been teaching mathematics for over 20 years, the last eleven at the University of Virginia. He currently has a joint appointment in the Department of Mathematics and the Department of Statistics at UVA.
During his career, Holt has won several awards for teaching. He has had NSF grants to support student math and science scholarships, the implementation of a computer-based homework system, and the development of an innovative undergraduate number theory course which later was turned into the text, Discovering Number Theory, coauthored with John Jones. In his spare time he enjoys lowering the value of his house with do-it-yourself home-improvement projects.

Preface
1. Systems of Linear Equations
1.1 Lines and Linear Equations
1.2 Linear Systems and Matrices
1.3 Applications of Linear Systems
1.4 Numerical Solutions
2. Euclidean Space
2.1 Vectors
2.2 Span
2.3 Linear Independence
3. Matrices
3.1 Linear Transformations
3.2 Matrix Algebra
3.3 Inverses
3.4 LU Factorization
3.5 Markov Chains
4. Subspaces
4.1 Introduction to Subspaces
4.2 Basis and Dimension
4.3 Row and Column Spaces
4.4 Change of Basis
5. Determinants
 5.1 The Determinant Function
5.2 Properties of the Determinant
5.3 Applications of the Determinant
6. Eigenvalues and Eigenvectors
6.1 Eigenvalues and Eigenvectors
6.2 Diagonalization
6.3 Complex Eigenvalues and Eigenvectors
6.4 Systems of Differential Equations
6.5 Approximation Methods
7. Vector Spaces
7.1 Vector Spaces and Subspaces
7.2 Span and Linear Independence
7.3 Basis and Dimension
8. Orthogonality
8.1 Dot Products and Orthogonal Sets
8.2 Projection and the Gram-Schmidt Process
8.3 Diagonalizing Symmetric Matrices and QR Factorization
8.4 The Singular Value Decomposition
8.5 Least Squares Regression
9. Linear Transformations
9.1 Definition and Properties
9.2 Isomorphisms
9.3 The Matrix of a Linear Transformation
9.4 Similarity
10. Inner Product Spaces
10.1 Inner Products
10.2 The Gram-Schmidt Process Revisited
10.3 Applications of Inner Products
11. Additional Topics and Applications
11.1 Quadratic Forms
11.2 Positive Definite Matrices
11.3 Constrained Optimization
11.4 Complex Vector Spaces
11.5 Hermitian Matrices
Glossary
Answers to Selected Exercises
Index