optimization lecture

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1. 1▶Discrete Optimization Lecture 15: Dynamic ProgrammingDiscrete Optimization (University of Victoria Math 428/529)Notes
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2. 2▶Discrete Optimization Lecture 14: List ColouringDiscrete Optimization (University of Victoria Math 428/529)Notes
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3. 3▶Discrete Optimization Lecture 13: Edge ColouringDiscrete Optimization (University of Victoria Math 428/529)Notes
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4. 4▶Discrete Optimization Lecture 12: Graph Colouring, Perfect Graphs and the Stable Set PolytopeDiscrete Optimization (University of Victoria Math 428/529)Notes
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5. 5▶Discrete Optimization Lecture 11: Max-Flow and Ford–Fulkerson AlgorithmDiscrete Optimization (University of Victoria Math 428/529)Notes
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6. 6▶Discrete Optimization Lecture 10: Matching Polytope and Network FlowsDiscrete Optimization (University of Victoria Math 428/529)Notes
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7. 7▶Discrete Optimization Lecture 9: Stable Marriage Problem and Tutte–Berge FormulaDiscrete Optimization (University of Victoria Math 428/529)Notes
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8. 8▶Discrete Optimization Lecture 8: Weighted Matching and Perfect Matching Problems in Bipartite GraphsDiscrete Optimization (University of Victoria Math 428/529)Notes
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9. 9▶Discrete Optimization Lecture 7: Introduction to Matchings, Kőnig's TheoremDiscrete Optimization (University of Victoria Math 428/529)Notes
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10. 10▶Discrete Optimization Lecture 6: Strong Duality for Linear ProgrammingDiscrete Optimization (University of Victoria Math 428/529)Notes
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11. 11▶Discrete Optimization Lecture 5: Linear Programming Basics and Weak DualityDiscrete Optimization (University of Victoria Math 428/529)Notes
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12. 12▶Discrete Optimization Lecture 4: Introduction to Linear ProgrammingDiscrete Optimization (University of Victoria Math 428/529)Notes
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13. 13▶Discrete Optimization Lecture 3: Reductions, hardness, NP-completeness, SAT, 3-SAT, undecidabilityDiscrete Optimization (University of Victoria Math 428/529)Notes
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14. 14▶Discrete Optimization Lecture 2: Decision Problems, Complexity classes. Philosophy of DualityDiscrete Optimization (University of Victoria Math 428/529)Notes
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15. 15▶Discrete Optimization Lecture 1: Introduction, algorithms, Big O, Shortest PathDiscrete Optimization (University of Victoria Math 428/529)Notes
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16. 16▶[CS292F 2020 Spring] Convex Optimization: Lecture 15 Follow the Regularized LeaderConvex Optimization Spring 2020Notes
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17. 17▶[CS292F 2020 Spring] Convex Optimization: Lecture 14 Online (Projected) Gradient DescentConvex Optimization Spring 2020Notes
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18. 18▶[CS292F 2020 Spring] Convex Optimization: Lecture 13 Learning from Expert AdviceConvex Optimization Spring 2020Notes
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19. 19▶[CS292F 2020 Spring] Convex Optimization: Lecture 12 Interior Point MethodsConvex Optimization Spring 2020Notes
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20. 20▶[CS292F 2020 Spring] Convex Optimization: Lecture 11 Newton's MethodConvex Optimization Spring 2020Notes
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21. 21▶[CS292F 2020 Spring] Convex Optimization: Lecture 10 KKT Conditions and Usage of DualityConvex Optimization Spring 2020Notes
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22. 22▶[CS292F 2020 Spring] Convex Optimization: Lecture 9 DualityConvex Optimization Spring 2020Notes
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23. 23▶[CS292F 2020 Spring] Convex Optimization: Lecture 8 Stochastic (Sub)gradient MethodConvex Optimization Spring 2020Notes
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24. 24▶[CS292F 2020 Spring] Convex Optimization: Lecture 7 Proximal Gradient Descent (Part II)Convex Optimization Spring 2020Notes
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25. 25▶[CS292F 2020 Spring] Convex Optimization: Lecture 6 Subgradient Method and Proximal Gradient DescentConvex Optimization Spring 2020Notes
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26. 26▶[CS292F 2020 Spring] Convex Optimization: Lecture 5 SubgradientConvex Optimization Spring 2020Notes
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27. 27▶[CS292F 2020 Spring] Convex Optimization: Lecture 4 Gradient DescentConvex Optimization Spring 2020Notes
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28. 28▶[CS292F 2020 Spring] Convex Optimization: Lecture 3 Canonical Problem FormsConvex Optimization Spring 2020Notes
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29. 29▶[CS292F 2020 Spring] Convex Optimization: Lecture 2 Convex Optimization BasicsConvex Optimization Spring 2020Notes
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30. 30▶[CS292F 2020 Spring] Convex Optimization: Lecture 1 Intro to convex optimizationConvex Optimization Spring 2020Notes
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31. 31▶Ke Wei: Low rank matrix recovery From iterative hard thresholding to Riemannian optimizationHIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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32. 32▶Hans Feichtinger: Fourier Analysis via the Banach Gelfand TripleHIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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33. 33▶Suvrit Sra: Lecture series on Aspects of Convex, Nonconvex, and Geometric Optimization (Lecture 1)HIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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34. 34▶Suvrit Sra: Lecture series on Aspects of Convex, Nonconvex, and Geometric Optimization (Lecture 2)HIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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35. 35▶Suvrit Sra: Lecture series on Aspects of Convex, Nonconvex, and Geometric Optimization (Lecture 3)HIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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36. 36▶Karlheinz Gröchenig: Gabor Analysis and its Mysteries (Lecture 1)HIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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37. 37▶Felix Krahmer: The Restricted Isometry Property for Random Gabor Synthesis MatricesHIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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38. 38▶David Gross: The Toric Code Finite WH meets Topological OrderHIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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39. 39▶Romanos Malikiosis: Full spark Gabor frames in finite dimensionsHIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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40. 40▶Xiadong Li: Phase Retrieval from Convex to Nonconvex MethodsHIM Lectures: Trimester Program "Mathematics of Signal Processing"Notes
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