linear systems

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1. 1▶Applied Numerical Algorithms, fall 2023 (lecture 15): Constrained optimization, KKT conditions6.S955: Applied Numerical Algorithms (fall 2023)Notes
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2. 2▶Applied Numerical Algorithms, fall 2023 (lecture 14): BFGS, DFP, Quasi-Newton optimization6.S955: Applied Numerical Algorithms (fall 2023)Notes
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3. 3▶Applied Numerical Algorithms, fall 2023 (lecture 13): Gradient descent, line search6.S955: Applied Numerical Algorithms (fall 2023)Notes
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4. 4▶Applied Numerical Algorithms, fall 2023 (lecture 12): Broyden low-rank updates, 1D optimization6.S955: Applied Numerical Algorithms (fall 2023)Notes
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5. 5▶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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6. 6▶Applied Numerical Algorithms, fall 2023 (lecture 10): Applications of SVD; Procrustes problem6.S955: Applied Numerical Algorithms (fall 2023)Notes
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7. 7▶Applied Numerical Algorithms, fall 2023 (lecture 9): QR iteration, SVD6.S955: Applied Numerical Algorithms (fall 2023)Notes
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8. 8▶Applied Numerical Algorithms, fall 2023 (lecture 8): Eigenvalue iteration, deflation6.S955: Applied Numerical Algorithms (fall 2023)Notes
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9. 9▶Applied Numerical Algorithms, fall 2023 (lecture 7): Applications of eigenvectors6.S955: Applied Numerical Algorithms (fall 2023)Notes
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10. 10▶Applied Numerical Algorithms, fall 2023 (lecture 6): QR factorization6.S955: Applied Numerical Algorithms (fall 2023)Notes
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11. 11▶2-7 Givens rotationsNumerical Linear AlgebraNotes
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12. 12▶3-2 Least squares problems and the normal equationsNumerical Linear AlgebraNotes
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13. 13▶3-1 Introduction to least squares problemsNumerical Linear AlgebraNotes
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14. 14▶2-1 Intro to linear systemsNumerical Linear AlgebraNotes
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15. 15▶2-2 LU decompositionNumerical Linear AlgebraNotes
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16. 16▶2-3 Condition number of a matrixNumerical Linear AlgebraNotes
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17. 17▶2-4 Cholesky decompositionNumerical Linear AlgebraNotes
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18. 18▶2-5 Solving linear systems using QR decompositionNumerical Linear AlgebraNotes
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19. 19▶2-6 Householder transformationNumerical Linear AlgebraNotes
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20. 20▶1-6 Schur decompositionNumerical Linear AlgebraNotes
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21. 21▶01: direct methods for sparse linear systems (lecture 1 of 42)Direct Methods for Sparse Linear SystemsNotes
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22. 22▶14: direct methods for sparse linear systems (lecture 14 of 42)Direct Methods for Sparse Linear SystemsNotes
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23. 23▶13: direct methods for sparse linear systems (lecture 13 of 42)Direct Methods for Sparse Linear SystemsNotes
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24. 24▶10: direct methods for sparse linear systems (lecture 10 of 42)Direct Methods for Sparse Linear SystemsNotes
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25. 25▶02: direct methods for sparse linear systems (lecture 2 of 42)Direct Methods for Sparse Linear SystemsNotes
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26. 26▶03: direct methods for sparse linear systems (lecture 3 of 42)Direct Methods for Sparse Linear SystemsNotes
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27. 27▶04: direct methods for sparse linear systems (lecture 4 of 42)Direct Methods for Sparse Linear SystemsNotes
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28. 28▶05: direct methods for sparse linear systems (lecture 5 of 42)Direct Methods for Sparse Linear SystemsNotes
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29. 29▶06: direct methods for sparse linear systems (lecture 6 of 42)Direct Methods for Sparse Linear SystemsNotes
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30. 30▶07: direct methods for sparse linear systems (lecture 7 of 42)Direct Methods for Sparse Linear SystemsNotes
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31. 31▶Lecture 6: Eigenproblems: How they arise, properties (part II)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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32. 32▶Lecture 6: Eigenproblems: How they arise, properties (part I)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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33. 33▶Lecture 5: Column spaces and QR (part I)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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34. 34▶Lecture 5: Column spaces and QR (part II)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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35. 35▶Lecture 5: Column spaces and QR (part III)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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36. 36▶Lecture 4: Designing linear systems; special structure (part I)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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37. 37▶Lecture 4: Designing linear systems; special structure (part II)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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38. 38▶Lecture 4: Designing linear systems; special structure (part III)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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39. 39▶Lecture 3: More LU; conditioning and sensitivity (part I)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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40. 40▶Lecture 3: More LU; conditioning and sensitivity (part II)CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)Notes
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