Numerical Linear Algebra
Matrix factorizations, conditioning, least squares, eigenvalue problems, and modern computational methods.
Overview
This course develops the mathematical and computational foundations of numerical linear algebra, with attention to stability, conditioning, efficient implementation, and the structure of large-scale problems.
Topics
- Orthogonal factorizations and least-squares problems
- Eigenvalue and singular-value computations
- Conditioning, stability, and backward error
- Iterative and randomized methods for large problems
Materials
Selected lecture notes, assignments, and supplementary computational examples may be posted here during the semester.