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Numerical Algorithms for Computing & ML, fall 2025 (lecture 7): Applications of eigenvalues
Applied Numerical Algorithms, fall 2023 (lecture 16): Constraints; intro to conjugate gradients
Numerical Algorithms for Computing & ML, fall 2025 (lecture 12): Broyden's method, root finding
Numerical Algorithms for Computing & ML, fall 2025 (lecture 6): QR factorization
Numerical Algorithms for Computing & ML, fall 2025 (lecture 16): Constrained optim., KKT conditions
Numerical Algorithms for Computing & ML, fall 2025 (lecture 17): Active set, barrier, intro to CG
Numerical Algorithms for Computing & ML, fall 2025 (lecture 20): Alternating optimization and ADMM
Numerical Algorithms for Computing & ML, fall 2025 (lecture 19): Gauss-Newton, Levenberg-Marquardt
Numerical Algorithms for Computing & ML, fall 2025 (lecture 3): LU factorization, least-squares
Numerical Algorithms for Computing & ML, fall 2025 (lecture 9): QR iteration, intro to SVD
Numerical Algorithms for Computing & ML, fall 2025 (lecture 10): Re-deriving SVD, SVD applications
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Last Updated: October 19, 2026
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Summary
Yeah Um so so basically think about the relative error So by the way there's a lot of ... simplest ordinary differential equation it has a closed form solution does it make sense to use our 2xxrpose V Thank you This goes faster if you put me out of my misery Um okay And what is the Say okayals the identity so the condition It's a good question I think for today's ... said well specifically if my function is a quadratic form then I know how to optimize it using this ... down and and and derived this new technique And in particular in our last Ah fabulous question So so far to to construct our our matrix I mean you could imagine a really naive
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