NLA. Home

	slides	colab	html	markdown	notebook
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

Lectures