Overview
This course explores the mathematical foundations and computational techniques of matrix analysis, covering decomposition methods (SVD, LU, Cholesky), orthogonalization and QR factorization, linear system conditioning and numerical stability, Gaussian elimination with pivoting strategies, eigenvalue computation through iterative diagonalization, and advanced matrix transformations including tridiagonal, Hessenberg, and Householder matrices, culminating in the QR algorithm.
This class used MATLAB for numerical computations. I chose to use LaTeX to write my homework. It was taught by Dr. Peter Blomgren.
GitHub Repository
All LaTeX source code, MATLAB scripts, and project files are available on GitHub. The repository includes the complete LaTeX documentation for all lab reports and data analysis scripts used throughout the course.
Homework
These homework are especially important, as they include the MATLAB code for the numerical computations of the course.