duality

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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▶1.1 IntroductionCombinatorial OptimizationNotes
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17. 17▶2.4 The Simplex Method, Part IIICombinatorial OptimizationNotes
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18. 18▶3.1 LP Duality, Part ICombinatorial OptimizationNotes
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19. 19▶3.4 The Primal Dual Framework, Part ICombinatorial OptimizationNotes
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20. 20▶3.5 The Primal Dual Framework, Part IICombinatorial OptimizationNotes
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21. 21▶2.3 The Simplex Method, Part IICombinatorial OptimizationNotes
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22. 22▶4.4 Ford Fulkerson, Part ICombinatorial OptimizationNotes
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23. 23▶4.5 Ford Fulkerson, Part IICombinatorial OptimizationNotes
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24. 24▶3.3 LP Duality, Part IIICombinatorial OptimizationNotes
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25. 25▶4.2 Primal Dual Applied to Shortest PathCombinatorial OptimizationNotes
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26. 26▶Lecture 15: Barrier methodFall 2016: Convex Optimization (10-725/36-725)Notes
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27. 27▶Lecture 14: Newton's methodFall 2016: Convex Optimization (10-725/36-725)Notes
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28. 28▶Lecture 13: Duality uses and correspondencesFall 2016: Convex Optimization (10-725/36-725)Notes
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29. 29▶Lecture 12: KKT conditionsFall 2016: Convex Optimization (10-725/36-725)Notes
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30. 30▶Lecture 11:Duality in general programsFall 2016: Convex Optimization (10-725/36-725)Notes
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31. 31▶Lecture 10: Duality in linear programsFall 2016: Convex Optimization (10-725/36-725)Notes
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32. 32▶Lecture 9: Proximal gradient descent and acceleration (continued)Fall 2016: Convex Optimization (10-725/36-725)Notes
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33. 33▶Lecture 8: Subgradient method (continued); Proximal gradient descent and accelerationFall 2016: Convex Optimization (10-725/36-725)Notes
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34. 34▶Lecture 7: Subgradients (continued); Subgradient methodFall 2016: Convex Optimization (10-725/36-725)Notes
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35. 35▶Lecture 6: Gradient descent (continued); SubgradientsFall 2016: Convex Optimization (10-725/36-725)Notes
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