This is a graduate level introductory course focusing on numerical computation and their practical applications. Some slide content is adapted from Prof. Vandenberghe's ECE133A course at UCLA and Profs. Boyd and Vandenberghe's book slides.

Lecture slides (Spring 2026)

• Introduction

• 1. Numerical precision and errors

• 2. Vectors

Lecture slides (Fall 2024)

• Matrices

• Linear models

• Matrix inverses

• LU factorization

• QR factorization

• Least squares

• Least squares data-fitting

• Constrained least squares

• Nonlinear equations and optimization

• Nonlinear least squares

• Constrained optimization

• Singular value decomposition

• Computing eigenvalues and eigenvectors