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 and how we compute them	.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. 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: Matrix functions and matrix equations	.html	link	.md	.ipynb
Lecture 16: Iterative methods for large scale eigenvalue problems	.html	link	.md	.ipynb
Lecture 17: Tensors and tensor decompositions	.html	link	.md	.ipynb

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Python intro	.html	link	.md	.ipynb
Automatic differentiation with JAX	.html	link	.md	.ipynb
NLA and neural networks	.html	link	.md	.ipynb
Convergence of CG for different spectrum	.html	link	.md	.ipynb
Numerical linear algebra, Skoltech, Fall 2020, general course info	.html	link	.md	.ipynb

Lectures