| Date | Lectures | Practice sessions |
|---|---|---|
| 11.11.2021 | Lecture 1. Floating point numbers, veсtors and vector norms [GitHub] | Seminar 1 |
| 18.11.2021 | Lecture 2. Matrices, their properties and norms. Lowrank approximation, SVD and applications. [GitHub] | Seminar 2 |
| 25.11.2021 | Lecture 3. Linear systems. LU decomposition. [GitHub] | Seminar 3 |
| 02.12.2021 | Lecture 4. Condition number. QR decomposition. Linear least-squares problem. [GitHub] | Seminar 4 |
| 09.12.2021 | Lecture 5. Sparse matrices and LU for them [GitHub] | Seminar 5 |
| 16.12.2021 | Lecture 6. Eigendecomposition. Schur theorem and QR algorithm [GitHub] | Seminar 6 |
| 23.12.2021 | Lecture 7. Introduction to numerical methods for linear systems [GitHub] | Seminar 7 |
| 10.02.2022 | Lecture 8. Intro to optimization methods. Random search and gradient descent [GitHub] | Seminar 8 |
| 17.02.2022 | Lecture 9. Gradient descent and heavy-ball method [GitHub] | Seminar 9 |
| 03.03.2022 | Lecture 10. Accelerated gradient method and Newton method [GitHub] | Seminar 10 |
| 10.03.2022 | Lecture 11. Intro to stochastic gradient methods [GitHub] | Seminar 11 |
| 17.03.2022 | Lecture 12. Quasi-Newton methods [GitHub] | Seminar 12 |
- For beginners
- More advanced books
- Gene H. Golub, Charles. F. Van Loan, "Matrix computations" (4th edition)
- Lloyd N. Trefethen and David Bau III, "Numerical Linear Algebra"
- Eugene. E. Tyrtyshnikov, "Brief introduction to numerical analysis"
- James W. Demmel, "Numerical Linear Algebra"