numerical algorithms

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1. 1▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 16): Constrained optim., KKT conditions6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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2. 2▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 15): BFGS and Quasi-Newton Methods6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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3. 3▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 14): Convergence of gradient descent6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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4. 4▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 13): Golden sec search, Wolfe conditions6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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5. 5▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 12): Broyden's method, root finding6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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6. 6▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 11): Procrustes problem, root finding6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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7. 7▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 10): Re-deriving SVD, SVD applications6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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8. 8▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 9): QR iteration, intro to SVD6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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9. 9▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 7): Applications of eigenvalues6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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10. 10▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 6): QR factorization6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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11. 11▶Applied Numerical Algorithms, fall 2023 (lecture 15): Constrained optimization, KKT conditions6.S955: Applied Numerical Algorithms (fall 2023)Notes
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12. 12▶Applied Numerical Algorithms, fall 2023 (lecture 14): BFGS, DFP, Quasi-Newton optimization6.S955: Applied Numerical Algorithms (fall 2023)Notes
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13. 13▶Applied Numerical Algorithms, fall 2023 (lecture 13): Gradient descent, line search6.S955: Applied Numerical Algorithms (fall 2023)Notes
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14. 14▶Applied Numerical Algorithms, fall 2023 (lecture 12): Broyden low-rank updates, 1D optimization6.S955: Applied Numerical Algorithms (fall 2023)Notes
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15. 15▶Applied Numerical Algorithms, fall 2023 (lecture 11): Root-finding, Newton's/Broyden's methods6.S955: Applied Numerical Algorithms (fall 2023)Notes
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16. 16▶Applied Numerical Algorithms, fall 2023 (lecture 10): Applications of SVD; Procrustes problem6.S955: Applied Numerical Algorithms (fall 2023)Notes
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17. 17▶Applied Numerical Algorithms, fall 2023 (lecture 9): QR iteration, SVD6.S955: Applied Numerical Algorithms (fall 2023)Notes
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18. 18▶Applied Numerical Algorithms, fall 2023 (lecture 8): Eigenvalue iteration, deflation6.S955: Applied Numerical Algorithms (fall 2023)Notes
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19. 19▶Applied Numerical Algorithms, fall 2023 (lecture 7): Applications of eigenvectors6.S955: Applied Numerical Algorithms (fall 2023)Notes
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20. 20▶Applied Numerical Algorithms, fall 2023 (lecture 6): QR factorization6.S955: Applied Numerical Algorithms (fall 2023)Notes
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