Numerical linear algebra course, @SkolTech, Term 2, 2019

https://nla.skoltech.ru

This repository contains lectures and homeworks for Numerical linear algebra course. It will be updated as the class progresses.

| Week | Lecture notebooks | Supplementary materials | Homework | Tests | |:------:|:----------|:----------|:----------|-------| |1| General info [GitHub, Nbviewer] <br> Intro to Python [GitHub, Nbviewer] <br> SVD Applications, part 1 [GitHub, Nbviewer] <br> SVD Applications, part 2 [GitHub, Nbviewer] <br> Matrix calculus intro [GitHub, Nbviewer] | Data folder | | Pre-term test | | 2 | Lecture 1. Floating point arithmetic, vector norms [GitHub, Nbviewer] <br> Lecture 2. Matrix norms and unitary matrices [GitHub, Nbviewer] <br> Lecture 3. Matvecs and matmuls, memory hierarchy, Strassen algorithm [GitHub, Nbviewer] <br> Lecture 4. Matrix rank, low-rank approximation, SVD [GitHub, Nbviewer] | | Problem set 1 <br> (Deadline: 15.11.2019 23:59) | | 3 | Lecture 5. Linear systems [GitHub, Nbviewer] <br> Lecture 6. Eigenvalues and eigenvectors [GitHub, Nbviewer] <br> Lecture 7. Matrix decompositions and how we compute them [GitHub, Nbviewer] <br> Lecture 8. Symmetric eigenvalue problem and SVD [GitHub, Nbviewer] | Overview of part 1 [GitHub, Nbviewer] | Problem set 2 <br> (Deadline: 26.11.2019 23:59) | | | 4 | Lecture 9. From dense to sparse linear algebra [GitHub, Nbviewer] <br> Lecture 10. Sparse direct solvers [GitHub, Nbviewer] <br> Lecture 11. Intro to iterative methods [GitHub, Nbviewer] | | | Theoretical minimum questions <br> Exam questions | 5 | Lecture 12. Great iterative methods [GitHub, Nbviewer] <br> Lecture 13. Iterative methods and preconditioners [GitHub, Nbviewer] <br> Lecture 14. Structured matrices, FFT, convolutions, Toeplitz matrices [GitHub, Nbviewer] <br> Lecture 15. Matrix functions and matrix equations [GitHub, Nbviewer] | Notes from lectures 12 and 13 <br> Notes from lectures 14 and 15 | |