2nd Edition

Exploring Linear Algebra Labs and Projects with Mathematica ®

By Crista Arangala Copyright 2025
164 Pages
by Chapman & Hall

164 Pages
by Chapman & Hall

164 Pages
by Chapman & Hall

This text focuses on the primary topics in a first course in Linear Algebra. The author includes additional advanced topics related to data analysis, singular value decomposition, and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica ® demonstrations and Mathematica ® coding. The book includes interesting examples... Read more

1. Matrix Operations                                                                                                      

2. Invertibility                                                                                                                     

3. Vector Spaces

4. Orthogonality                                                                                                            

5. Matrix Decomposition with Applications                                             

6. Applications to Differential Equations

Biography

Dr. Crista Arangala is Professor of Mathematics and Chair of the Department of Mathematics and Statistics at Elon University in North Carolina. She has been teaching and researching in a variety of fields, including inverse problems, applied partial differential equations, applied linear algebra, mathematical modeling, and service learning education. She runs a traveling science museum with her Elon University students in Kerala, India. Dr. Arangala was chosen to be a Fulbright Scholar in 2014 as a visiting lecturer at the University of Colombo where she continued her projects in inquiry learning in Linear Algebra and began working with a modeling team focusing on Dengue fever research. Dr. Arangala has published several textbooks that implore inquiry learning techniques, including Exploring Linear Algebra: Labs and Projects with MATLAB®, Mathematical Modeling: Branching Beyond Calculus, and Linear Algebra with Machine Learning and Data.