NLA. Home

	slides	page	markdown	notebook
Python Introduction	.html	.html	.md	.ipynb
Brief Python Intro	.html	.html	.md	.ipynb
SVD applications	.html	.html	.md	.ipynb
Matrix calculus	.html	.html	.md	.ipynb
01 Floating-point arithmetic, vector norms	.html	.html	.md	.ipynb
02 Matrix norms and unitary matrices	.html	.html	.md	.ipynb
03 Matvecs and matmuls, memory hierarchy, Strassen algorithm	.html	.html	.md	.ipynb
04 Matrix rank, low-rank approximation, SVD	.html	.html	.md	.ipynb
05 Linear systems	.html	.html	.md	.ipynb
06 Eigenvalues and eigenvectors	.html	.html	.md	.ipynb
07 Matrix decompositions and how we compute them	.html	.html	.md	.ipynb
08 Symmetric eigenvalue problem and SVD	.html	.html	.md	.ipynb
Overview of the first part of the course	.html	.html	.md	.ipynb
09 From dense to sparse linear algebra	.html	.html	.md	.ipynb
10 Sparse direct solvers	.html	.html	.md	.ipynb
11 Intro to iterative methods	.html	.html	.md	.ipynb
12 Great Iterative Methods	.html	.html	.md	.ipynb
13 Iterative methods and preconditioners	.html	.html	.md	.ipynb
14 Structured matrices, FFT, convolutions, Toeplitz matrices	.html	.html	.md	.ipynb
15 Matrix functions and matrix equations	.html	.html	.md	.ipynb

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