Numerical Optimization Video Lectures

Numerical Optimization
'Numerical Optimization' Video Lectures by Dr. Shirish K. Shevade from IISc Bangalore
"Numerical Optimization" - Video Lectures
1. Introduction
2. Mathematical Background
3. Mathematical Background (contd)
4. One Dimensional Optimization - Optimality Conditions
5. One Dimensional Optimization (contd)
6. Convex Sets
7. Convex Sets (contd)
8. Convex Functions
9. Convex Functions (contd)
10. Multi Dimensional Optimization - Optimality Conditions, Conceptual Algorithm
11. Line Search Techniques
12. Global Convergence Theorem
13. Steepest Descent Method
14. Classical Newton Method
15. Trust Region and Quasi-Newton Methods
16. Quasi-Newton Methods - Rank One Correction, DFP Method
17. i) Quasi-Newton Methods - Broyden Family ii) Coordinate Descent Method
18. Conjugate Directions
19. Conjugate Gradient Method
20. Constrained Optimization - Local and Global Solutions, Conceptual Algorithm
21. Feasible and Descent Directions
22. First Order KKT Conditions
23. Constraint Qualifications
24. Convex Programming Problem
25. Second Order KKT Conditions
26. Second Order KKT Conditions (contd)
27. Weak and Strong Duality
28. Geometric Interpretation
29. Lagrangian Saddle Point and Wolfe Dual
30. Linear Programming Problem
31. Geometric Solution
32. Basic Feasible Solution
33. Optimality Conditions and Simplex Tableau
34. Simplex Algorithm and Two-Phase Method
35. Duality in Linear Programming
36. Interior Point Methods - Affine Scaling Method
37. Karmarkar\'s Method
38. Lagrange Methods, Active Set Method
39. Active Set Method (contd)
40. Barrier and Penalty Methods, Augmented Lagrangian Method and Cutting Plane Method
41. Summary
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