Do you have a large, sparse matrix or graph problem that you'd like to solve? Then this course is for you!
The course covers roughly three topics: the fundamentals of numerical linear algebra, iterative methods (such as CG and GMRES), and multilevel methods (for example geometric and algebraic multigrid).
The course involves several homeworks (usually bi-weekly) and two projects: a midsemester project with a focus on Krylov methods and a final project focussed on multigrid. There is also a strong participation grade based on your attendence (informal) and ability to keep up with handouts and other tasks. The tentative grade breakdown is:
This will be finalized by the first week of class.
The course homeworks and examples in class will be in Python. In particular, we'll use the sparse features in Scipy and the solvers in PyAMG.
We will be using Saad's book Iterative methods for sparse linear systems
In addition, there are several other helpful texts (optional):