For course specifics (e.g. syllabus and textbooks) consult Professor Gleich’s course page here
| Office: | HAAS G14 |
| Email: | kkloste--at--purdue--dot--edu |
| Office Hours: | Tuesday, 1:00pm - 2:00pm |
| Wednesday, 10:30am - 11:30am |
| Date | ~~~ | Topic |
|---|---|---|
| Nov 13 | Homework Day + Krylov Review | |
| Nov 18 | GMRES & Mod. Gram Schmidt | |
| Nov 20 | (HW 7 due) Orthogonal polynomials & Lanczos | |
| Nov 25 | Reduction to tridiagonal form for eigenvalues | |
| Nov 27 | Thanksgiving break | |
| Dec 2 | Preconditioning and CG convergence | |
| Dec 4 | (HW 8 due) Kronecker products and the Laplacian | |
| Dec 9 | (Ex Cred due) Optimal SOR omega | |
| Dec 11 | Final class! | |
| Dec 17 | Final exam: Wed 12/17, 8:00am, LAMB 108 |
| Lecture | Topic | ~ ~ ~ | Readings |
|---|---|---|---|
| 11, 12 | Least Squares | T & Bau: Lec 11 | |
| GvL: 5.3 - 5.3.8, skip 5.3.6 | |||
| GvL: 5.5 - 5.5.4, skip 5.5.3 | |||
| Advanced QR | T & Bau: Lec 7, 10 | ||
| GvL: 5.1 - 5.1.11; skip 5.1.7, 5.1.10 | |||
| GvL: 5.2 - 5.2.2; 5.2.5 - 5.2.9 |
UNIT 2: Iterative Methods
| Lecture | Topic | ~ ~ ~ | Readings |
|---|---|---|---|
| 1 | Intro & Sparse matrix storage / matvecs | T & Bau: Lec 32 | |
| 2 | Residuals & Linear Systems | sparsity demo | |
| Jacobi & Gauss-Seidel | GvL 11 before 11.1; then 11.2–11.2.5 | ||
| 3 | Convergence of Jacobi, eigenvalues | GvL 7.3–7.3.1; T & Bau Lec 24 | |
| 4 | Methods to compute eigenvalues, PageRank | GvL 7.3.1, T & Bau Lec 27; | |
| PageRank: there are loads of descriptions on the web; a text that I enjoyed is Langville and Meyer, Ch 4; class notes should suffice | |||
| For a more indepth description and applications, see Prof. Gleich’s nice overview | |||
| HW4 purdue-web.mat file | |||
| 5 | Gauss-Seidel and property A, SOR and Richardson | GvL 11.2.7; notes from Gene Golub |
Professor Gleich and I check posts on Piazza throughout the day (but don’t count on an answer after 8:00pm).
Of course, if you need to speak to me in person about something and my office hours don’t work for you, then please email me and I’ll do my best to set up a time to meet!
If you want to try using Python in the course, a great free place to get down the basics is the Code Academy