Introduction to Applied Linear Algebra

This book is
meant to provide an introduction to vectors, matrices, and least squares
methods, basic topics in applied linear algebra. Our goal is to give the
beginning student, with little or no prior exposure to linear algebra, a good
grounding in the basic ideas, as well as an appreciation for how they are used
in many applications, including data fitting, machine learning and artificial
intelligence, tomography, image processing, finance, and automatic control
systems. Topics covered includes: Vectors, Norm and distance, Clustering,
Matrices, Linear equations, Matrix multiplication, Linear dynamical systems,
Least squares, Multi-objective least squares, Constrained least squares.

**Author(s):** Stephen Boyd and Lieven Vandenberghe

473 Pages