/nla2020

Github repository for NLA2020 course

Primary LanguageJupyter NotebookMIT LicenseMIT

Numerical linear algebra course, @SkolTech, Term 2, 2020

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]
Lecture 1. Floating point arithmetic, vector norms [GitHub]
Lecture 2. Matrix norms and unitary matrices [GitHub]
Lecture 3. Matvecs and matmuls, memory hierarchy, Strassen algorithm [GitHub]
Brief Python intro
JAX intro
Home assignment 1
Deadline: November, 8, 23:59 MSK
2 Lecture 4. Matrix rank, low-rank approximation, SVD [GitHub]
Lecture 5. Linear systems [Github]
Lecture 6. Eigenvalues and eigenvectors [GitHub]
PyTorch intro
3 Lecture 7. Matrix decompositions and how we compute them [GitHub]
Lecture 8. Symmetric eigenvalue problem and SVD [GitHub]
Lecture 9. From dense to sparse linear algebra [GitHub]
Home assignment 2
Deadline: November, 23, 23:59 MSK
4 Lecture 10. Sparse direct solvers [GitHub]
Lecture 11. Intro to iterative methods [GitHub]
Lecture 12. Great iterative methods [GitHub]
CG convergence Exam questions
Theoretical minimum questions
5 Lecture 13. Iterative methods and preconditioners [GitHub]
Lecture 14. Structured matrices, FFT, convolutions, Toeplitz matrices [GitHub]
Lecture 15. Matrix functions and matrix equations [GitHub]
Home assignment 3
Deadline: December, 4, 23:59 MSK
6 Lecture 16. Large scale eigenvalue problem [GitHub]
Lecture 17. Tensors and tensor decompositions [GitHub]