nla2018's Introduction

Numerical linear algebra course, @SkolTech, Term 2, 2018

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] Lecture 1. Floating-point arithmetic, vector norms [GitHub, Nbviewer] Lecture 2. Matrix norms and unitary matrices [GitHub, Nbviewer]	Python intro	Requirements Problem set 1 Deadline: 11/11/18 (23:59)	Pre-term test
2	Lecture 3. Matvecs and matmuls, memory hierarchy, Strassen algorithm [GitHub, Nbviewer] Lecture 4. Matrix rank, low-rank approximation, SVD [GitHub, Nbviewer] Lecture 5. Linear systems [GitHub, Nbviewer]	Notes on matrix calculus [GitHub, Nbviewer]
3	Lecture 6. Eigenvalues and eigenvectors [GitHub, Nbviewer] Lecture 7. Matrix decompositions and how we compute them [GitHub, Nbviewer] Lecture 8. Symmetric eigenvalue problem and SVD [GitHub, Nbviewer]	Examples of projects	Problem set 2 Deadline: 27/11/18 (00:02)
4	Lecture 9. From dense to sparse linear algebra [GitHub, Nbviewer] Lecture 10. Sparse direct solvers [GitHub, Nbviewer] Lecture 11. Intro to iterative methods [GitHub, Nbviewer]
5	Lecture 12. Great iterative methods [GitHub, Nbviewer] Lecture 13. Iterative methods and preconditioners [GitHub, Nbviewer]		Problem set 3 Deadline: 05/12/18 (23:59)	Exam questions
6	Lecture 14. Iterative methods for large scale eigenvalue problems [GitHub, Nbviewer] Lecture 15. Structured matrices, FFT, convolutions, Toeplitz matrices [GitHub, Nbviewer] Lecture 16. Matrix functions and matrix equations [GitHub, Nbviewer] Lecture 17. Tensors and tensor decompositions [GitHub, Nbviewer]			NLA basics

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