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So these days if I've learned anything from the MIT College of ... but it's a good question is is still very dominant like in the ... makes good sense right I mean basically the vast majority of of ... distractions we'll start with some applications of quadriure and then talk about the the basic ... nicely behaved right so essentially what what we're going to prove like our gradient descent That's exactly right yeah so um by the way just to dispel one additional myth out there in the
... like our algos for this were like LU they looked very much like like you know
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Numerical Algorithms for Computing & ML, fall 2025 (lecture 9): QR iteration, intro to SVD
Numerical Algorithms for Computing & ML, fall 2025 (lecture 22): 1D Quadrature/Numerical Integration
Numerical Algorithms for Computing & ML, fall 2025 (lecture 18): Conjugate gradient algorithm
Numerical Algorithms for Computing & ML, fall 2025 (lecture 14): Convergence of gradient descent
Numerical Algorithms for Computing & ML, fall 2025 (lecture 10): Re-deriving SVD, SVD applications
Numerical Algorithms for Computing & ML, fall 2025 (lecture 13): Golden sec search, Wolfe conditions
Numerical Algorithms for Computing & ML, fall 2025 (lecture 15): BFGS and Quasi-Newton Methods
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
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Last Updated: June 8, 2026
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