Mark J. Debonis

Introduction To Linear Algebra (eBook, PDF)

Computation, Application, and Theory

• Format: PDF

Produktbeschreibung

This book is designed for students who have never been exposed to the topics in a linear algebra course. The text is ¿lled with interesting and diverse application sections but is also a theoretical text which aims to train students to do succinct computation in a knowledgeable way.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 434
• Erscheinungstermin: 22. Februar 2022
• Englisch
• ISBN-13: 9781000541694
• Artikelnr.: 63493813

Autorenporträt

Mark J. DeBonis received his PhD in Mathematics from the University of California, Irvine, USA. He began his career as a theoretical mathematician in the field of group theory and model theory, but in later years switched to applied mathematics, in particular to machine learning. He spent some time working for the US Department of Energy at Los Alamos National Lab as well as the US Department of Defense at the Defense Intelligence Agency as an applied mathematician of machine learning. He is an Associate Professor of Mathematics at Manhattan College in New York City and is also currently working for the US Department of Energy at Sandia National Lab as a Principal Data Analyst. His research interests include machine learning, statistics, and computational algebra.

Inhaltsangabe

1. Examples of Vector Spaces. 1.1. First Vector Space: Tuples. 1.2. Dot
Product. 1.3. Application: Geometry. 1.4. Second Vector Space: Matrices.
1.5. Matrix Multiplication. 2. Matrices and Linear Systems. 2.1. Systems of
Linear Equations. 2.2. Gaussian Elimination. 2.3. Application: Markov
Chains. 2.4. Application: The Simplex Method. 2.5. Elementary Matrices and
Matrix Equivalence. 2.6. Inverse of a Matrix. 2.7. Application: The Simplex
Method Revisited. 2.8. Homogeneous/Nonhomogeneous Systems and Rank. 2.9.
Determinant. 2.10. Applications of the Determinant. 2.11. Application: Lu
Factorization. 3. Vector Spaces. 3.1. Definition and Examples. 3.2.
Subspace. 3.3. Linear Independence. 3.4. Span. 3.5. Basis and Dimension.
3.6. Subspaces Associated with a Matrix. 3.7. Application: Dimension
Theorems. 4. Linear Transformations. 4.1. Definition and Examples. 4.2.
Kernel and Image. 4.3. Matrix Representation. 4.4. Inverse and Isomorphism.
4.5. Similarity of Matrices. 4.6. Eigenvalues and Diagonalization. 4.7.
Axiomatic Determinant. 4.8. Quotient Vector Space. 4.9. Dual Vector Space.
5. Inner Product Spaces. 5.1. Definition, Examples and Properties. 5.2.
Orthogonal and Orthonormal. 5.3. Orthogonal Matrices. 5.4. Application: QR
Factorization. 5.5. Schur Triangularization Theorem. 5.6. Orthogonal
Projections and Best Approximation. 5.7. Real Symmetric Matrices. 5.8.
Singular Value Decomposition. 5.9. Application: Least Squares Optimization.
6. Applications in Data Analytics. 6.1. Introduction. 6.2. Direction of
Maximal Spread. 6.3. Principal Component Analysis. 6.4. Dimensionality
Reduction. 6.5. Mahalanobis Distance. 6.6. Data Sphering. 6.7. Fisher
Linear Discriminant Function. 6.8. Linear Discriminant Functions in Feature
Space. 6.9. Minimal Square Error Linear Discriminant Function. 7. Quadratic
Forms. 7.1. Introduction to Quadratic Forms. 7.2. Principal Minor
Criterion. 7.3. Eigenvalue Criterion. 7.4. Application: Unconstrained
Nonlinear Optimization. 7.5. General Quadratic Forms. Appendix A. Regular
Matrices. Appendix B. Rotations and Reflections in Two Dimensions. Appendix
C. Answers to Selected Exercises.