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Numerical linear algebra course, @SkolTech, Term 2, 2021

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]
Floating point operations demo
Matmul performance demo
Home assignment 1
Deadline: 14.11.2021, 23:59 MSK
2 Lecture 4. Matrix rank, low-rank approximation, SVD. [GitHub]
Lecture 5. Linear systems [GitHub]
Lecture 6. Eigenvalues and eigenvectors [GitHub]
3 Lecture 7. Matrix decompositions review. How to compute QR decomposition and Schur decomposition [GitHub]
Lecture 8. Symmetric eigenvalue problem and SVD. [GitHub]
Lecture 9. From dense to sparse linear algebra [GitHub]
Home assignment 2
Deadline: 30.11.2021, 23:59 MSK
4 Lecture 10. Sparse direct solvers [GitHub]
Lecture 11. Intro to iterative methods [GitHub]
Lecture 12. Great iterative methods [GitHub]
Exam questions
Theoretical minimum questions
5 Lecture 13. Iterative methods and preconditioners [GitHub]
Lecture 14. Structured matrices, FFT, convolutions, Toeplitz matrices [GitHub]
Lecture 15. Large scale eigenvalue problem [GitHub]
Home assignment 3
Deadline: 09.12.2021, 23:59 MSK
6 Lecture 16. Matrix functions and randomized methods in NLA [GitHub]
Lecture 17. Tensor decompositions [GitHub]

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