nla2021_important's Introduction

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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