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. Memory hierarchy, matrix multiplication, Strassen algorithm [Github] |
HW1 (Deadline: November, 20, 23:59 MSK) |
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| 2 | Lecture 4. Pytorch and Jax tutorials. Lecture 5. Matrix rank, skeleton decomposition, SVD. [GitHub] Lecture 6. Linear systems [GitHub] |
JAX Tutorial [GitHub] PyTorch Tutorial [GitHub] |
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| 3 | Lecture 7. Eigenvalues and eigenvectors. [GitHub] Lecture 8. Matrix decompositions and how we compute them [GitHub] Lecture 9. Symmetric eigenvalue problem and SVD [GitHub] |
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| 4 | Lecture 10. Randomized linear algebra [GitHub] Lecture 11. From dense to sparse linear algebra [GitHub] Lecture 12. Midterm Exam |
HW2 (Deadline: December, 11, 23:59 MSK) |
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| 5 | Lecture 13. Sparse direct solvers [GitHub] Lecture 14. Intro to iterative methods [Github] Lecture 15. Great iterative methods [Github] |
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| 6 | Lecture 16. Iterative methods and preconditioners [Github] Lecture 17. Structured matrices, FFT, convolutions, Toeplitz matrices [Github] Lecture 18. Iterative methods for large scale eigenvalue problems [Github] |
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| 7 | Lecture 19. Matrix functions and matrix equations [Github] Lecture 20. Tensors and tensor decompositions [Github] Lecture 21. Final Exam (day 1) |
Exam questions Theoretical minimum questions |
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| 8 | Lecture 22. Final Exam (day 2) Lecture 23. Project Presentation (day 1) Lecture 24. Project Presentation (day 2) |