NLA. Home

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Lecture 1: Floating-point arithmetic, vector norms	.html	link	.md	.ipynb
Lecture 2. Matrix norms and unitary matrices	.html	link	.md	.ipynb
Lecture 3: Matvecs and matmuls, memory hierarchy, Strassen algorithm	.html	link	.md	.ipynb
Lecture 4: Matrix rank, low-rank approximation, SVD	.html	link	.md	.ipynb
Lecture 5: Linear systems	.html	link	.md	.ipynb
Lecture 6: Eigenvalues and eigenvectors	.html	link	.md	.ipynb
Lecture 7: Matrix decompositions review. How to compute QR decomposition and Schur decomposition	.html	link	.md	.ipynb
Lecture 8: Symmetric eigenvalue problem and SVD	.html	link	.md	.ipynb
Lecture 9: From dense to sparse linear algebra	.html	link	.md	.ipynb
Lecture 10: Sparse direct solvers	.html	link	.md	.ipynb
Lecture 11. Direct solvers for sparse matrices (cont'd). Intro to iterative methods	.html	link	.md	.ipynb
Lecture 12: Great Iterative Methods	.html	link	.md	.ipynb
Lecture 13: Iterative methods and preconditioners	.html	link	.md	.ipynb
Lecture 14: Structured matrices, FFT, convolutions, Toeplitz matrices	.html	link	.md	.ipynb
Lecture 15: Iterative methods for large scale eigenvalue problems	.html	link	.md	.ipynb
Lecture 16: Matrix functions. Introduction to randomized linear algebra	.html	link	.md	.ipynb
Lecture 17: Tensors and tensor decompositions	.html	link	.md	.ipynb

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Just-in-time compiler for Python, see http://numba.pydata.org	.html	link	.md	.ipynb
Automatic differentiation with JAX	.html	link	.md	.ipynb
Numerical linear algebra, Skoltech, Fall 2021, general course info	.html	link	.md	.ipynb

Lectures