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.
Introduction
1 Numerical precision and errors
2 Vectors
3 Norm and distance
4 Matrices
5 Linear models
6 Matrix inverses
7 LU factorization
8 QR factorization
9 Least squares
10 Least squares data-fitting
11 Constrained least squares
12 Nonlinear equations and optimization
13 Nonlinear least squares
14 Constrained optimization
15 Singular value decomposition
16 Computing eigenvalues and eigenvectors