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Lecture 1: Floating-point arithmetic, vector norms .html link .html .md .ipynb
Lecture 2. Matrix norms and unitary matrices .html link .html .md .ipynb
Lecture 3: Matvecs and matmuls, memory hierarchy, Strassen algorithm .html link .html .md .ipynb
PyTorch .html link .html .md .ipynb
JAX Tutorial .html link .html .md .ipynb
Lecture 4: Matrix rank, low-rank approximation, SVD .html link .html .md .ipynb
Lecture 6: Linear systems .html link .html .md .ipynb
Lecture 7: Eigenvalues and eigenvectors .html link .html .md .ipynb
Lecture 8: Matrix decompositions review. How to compute QR decomposition and Schur decomposition .html link .html .md .ipynb
Lecture 9: Symmetric eigenvalue problem and SVD .html link .html .md .ipynb
Lecture 10: Randomized linear algebra .html link .html .md .ipynb
Lecture 11: From dense to sparse linear algebra .html link .html .md .ipynb
Lecture 13: Sparse direct solvers .html link .html .md .ipynb
Lecture 14. Intro to iterative methods .html link .html .md .ipynb
Lecture 15: Great Iterative Methods .html link .html .md .ipynb
Lecture 16: Iterative methods and preconditioners .html link .html .md .ipynb
Lecture 17: Structured matrices, FFT, convolutions, Toeplitz matrices .html link .html .md .ipynb
Lecture 18: Iterative methods for large scale eigenvalue problems .html link .html .md .ipynb
Lecture 19: Matrix functions and matrix equations .html link .html .md .ipynb


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Numerical linear algebra, Skoltech, Fall 2022, general course info .html link .html .md .ipynb