Python Jupyter Notebooks for Linear Algebra

This repository contains the linear algebra chapter that I wrote for the book Python Jupyter Notebooks for College Math Teachers. The book is available online at: https://timothyprojectgig.github.io/JB_Math_Textbook/Linear.html

Book Editors: Paul Isihara, Peter Jantsch, Thomas VanDrunen

Technical Editor: Claire Wagner

Faculty Contributors: Soheil Anbouhi, Laura Gross, Paul Isihara, Peter Jantsch, Ying Li, Yiheng Liang, Rachel Petrik, Inne Singge

Student Contributors: Samuel Carlson, Claire Wagner, Ziling Zhong, Jonathan Zhu

The linear algebra chapter serves as introductory exploration of fundamental concepts in linear algebra with a focus on computational aspects with Python. It also serves as foundational background for the subsequent chapter, “Linear Algebra and Optimization for Data Analysis”.

Each section begins with a brief overview of the objectives and followed by several examples exercises to enhance understanding and facilitate learning. Moreover, I aim to include numerical notes wherever appropriate. These notes aim to explain and compare different computational approaches to problem-solving. My goal is to write my own code (whenever possible) to solve problems. However, for matrices of higher dimensions, we take advantage of the NumPy linear algebra module which provide efficient implementations of standard linear algebra algorithms.

Getting Started: These notebooks are designed for motivated students and teachers! Feel free to clone this repository and explore the notebooks. Or simply click on the following links to open them in Google Colab:

-Introduction

-Linear Systems and their solutions

-Matrix Algebra

-Linear Transformation

-Eigenvalues and Eigenvectors

-Analytic Geometry

-Solutions