optimality conditions

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1. 1▶1.1 Introduction to Optimization and to MeOptimization AlgorithmsNotes
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2. 2▶1.2 What these Lectures do and do not coverOptimization AlgorithmsNotes
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3. 3▶3.1 Intro to Gradient and Subgradient DescentOptimization AlgorithmsNotes
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4. 4▶3.4 Convergence RatesOptimization AlgorithmsNotes
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5. 5▶3.3 Properties of Smooth and Strongly Convex FunctionsOptimization AlgorithmsNotes
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6. 6▶1.3 Some First ExamplesOptimization AlgorithmsNotes
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7. 7▶2.5 Optimality Conditions for Convex OptimizationOptimization AlgorithmsNotes
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8. 8▶4.1 Oracle Lower BoundsOptimization AlgorithmsNotes
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9. 9▶3.2 Smooth and Strongly Convex FunctionsOptimization AlgorithmsNotes
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10. 10▶2.6 Optimality Conditions and ProjectionOptimization AlgorithmsNotes
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11. 11▶Mod-01 Lec-01 IntroductionComputer - Numerical OptimizationNotes
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12. 12▶Mod-02 Lec-02 Mathematical BackgroundComputer - Numerical OptimizationNotes
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13. 13▶Mod-02 Lec-03 Mathematical Background (contd)Computer - Numerical OptimizationNotes
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14. 14▶Mod-06 Lec-11 Line Search TechniquesComputer - Numerical OptimizationNotes
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15. 15▶Mod-06 Lec-12 Global Convergence TheoremComputer - Numerical OptimizationNotes
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16. 16▶Mod-03 Lec-05 One Dimensional Optimization (contd)Computer - Numerical OptimizationNotes
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17. 17▶Mod-04 Lec-06 Convex SetsComputer - Numerical OptimizationNotes
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18. 18▶Mod-04 Lec-07 Convex Sets (contd)Computer - Numerical OptimizationNotes
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19. 19▶Mod-05 Lec-08 Convex FunctionsComputer - Numerical OptimizationNotes
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20. 20▶Mod-05 Lec-09 Convex Functions (contd)Computer - Numerical OptimizationNotes
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21. 21▶Lecture 6. Local and global minimum. Sufficient and necessary unconstrained optimality conditionsIntroduction to OptimizationNotes
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22. 22▶Lecture 4-5: Convex sets and functionsIntroduction to OptimizationNotes
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23. 23▶Lecture 2-3: Derivatives of multivariate functions: Gradient and HessianIntroduction to OptimizationNotes
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24. 24▶Lecture 1b, Linear algebra refresh:Introduction to OptimizationNotes
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25. 25▶Lecture 1a, Introduction; Examples of unconstrained and constrained optimization problems:Introduction to OptimizationNotes
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26. 26▶Lecture 16 Conic programming 1Introduction to OptimizationNotes
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27. 27▶Lecture 15 Minimax theorem, game theory and Lagrange dualityIntroduction to OptimizationNotes
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28. 28▶Lecture 14 Lagrange multipliers and penalty function method. Augmented LagrangianIntroduction to OptimizationNotes
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29. 29▶Lecture 13. Summary of unconstrained optimization. Optimization with constraintsIntroduction to OptimizationNotes
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30. 30▶Lecture 12 Sequential subspace optimization (SESOP) method and Quasi-Newton BFGSIntroduction to OptimizationNotes
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