# Linear Algebra

David Cherney, UC Davis

Tom Denton, The Fields Institute and York University

Andrew Waldon, UC Davis

Pub Date: 2016

Publisher: Independent

Language: English

## Read this book

## Conditions of Use

Attribution-NonCommercial-ShareAlike

CC BY-NC-SA

## Reviews

Treats the standard topics in Linear Algebra, plus Linear Optimization (Simplex Method and applications), and the SVD. An exploratory introductory first chapter is non-standard but interesting for engaging students right away in seeing and asking... read more

This text covers the material expected in a first term course on undergraduate Linear Algebra, especially in the considerations of a course with many engineering majors. Major focus is on solving systems of linear equations, Gaussian elimination,... read more

## Table of Contents

Chapter 1: What is Linear Algebra?

Chapter 2: Systems of Linear Equations

Chapter 3: The Simplex Method

Chapter 4: Vectors in Space, n-Vectors

Chapter 5: Vector Spaces

Chapter 6: Linear Transformations

Chapter 7: Matrices

Chapter 8: Determinants

Chapter 9: Subspaces and Spanning Sets

Chapter 10: Linear Independence

Chapter 11: Basis and Dimension

Chapter 12: Eigenvalues and Eigenvectors

Chapter 13: Diagonalization

Chapter 14: Orthonormal Bases and Complements

Chapter 15: Diagonalizing Symmetric Matrices

Chapter 16: Kernel, Range, Nullity, Rank

Chapter 17: Least squares and Singular Values

## About the Book

We believe the entire book can be taught in twenty five 50-minute lectures to a sophomore audience that has been exposed to a one year calculus course. Vector calculus is useful, but not necessary preparation for this book, which attempts to be self-contained. Key concepts are presented multiple times, throughout the book, often first in a more intuitive setting, and then again in a definition, theorem, proof style later on. We do not aim for students to become agile mathematical proof writers, but we do expect them to be able to show and explain why key results hold. We also often use the review exercises to let students discover key results for themselves; before they are presented again in detail later in the book.

The book has been written such that instructors can reorder the chapters (using the La- TeX source) in any (reasonable) order and still have a consistent text. We hammer the notions of abstract vectors and linear transformations hard and early, while at the same time giving students the basic matrix skills necessary to perform computations. Gaussian elimination is followed directly by an “exploration chapter” on the simplex algorithm to open students minds to problems beyond standard linear systems ones. Vectors in Rn and general vector spaces are presented back to back so that students are not stranded with the idea that vectors are just ordered lists of numbers. To this end, we also labor the notion of all functions from a set to the real numbers. In the same vein linear transformations and matrices are presented hand in hand. Once students see that a linear map is specified by its action on a limited set of inputs, they can already understand what a basis is. All the while students are studying linear systems and their solution sets, so after matrices determinants are introduced. This material can proceed rapidly since elementary matrices were already introduced with Gaussian elimination. Only then is a careful discussion of spans, linear independence and dimension given to ready students for a thorough treatment of eigenvectors and diagonalization. The dimension formula therefore appears quite late, since we prefer not to elevate rote computations of column and row spaces to a pedestal. The book ends with applications–least squares and singular values. These are a fun way to end any lecture course. It would also be quite easy to spend any extra time on systems of differential equations and simple Fourier transform problems.

## About the Contributors

### Authors

**David Cherney,** Lecturer, Mathematics, UC Davis. Ph.D., 2010, University of California, Davis.

**Tom Denton. **York University and the Fields Institute, Toronto, Canada. Postdoctoral research with Nantel Bergeron and Mike Zabrocki on k-Schur functions and other topics in algebraic combinatorics. Fulbright Scholar, Maseno University, Kenya. Project concerned using e-learning platforms and emerging technologies to improve the teaching of mathematics in the developing world. Led math camps for secondary students, and co-founded a new technology hub in Kisumu, Kenya. PhD, University of California, Davis.

**Andrew Waldron. **Professor, Mathematics, UC Davis. Waldron's research is devoted to a broad range of problems in theoretical and mathematical physics. In particular he has made an important contribution to the conjectured Banks-Fishler-Shenker-Susskind (BFSS) matrix model of string theory and M-theory. String theory is a proposed perturbative theory that would unify all of the fundamental forces of physics; in other words it is a "theory of everything". Its non-perturbative counterpart is a hypothetical theory called M-theory.