Course Schedule
Date Topic Homework Due 
1. We Jan 22 Introduction
2. Fr Jan 24 Linear Programming Modeling
3. Mo Jan 27 Transformations and Standard Form of LP
4. We Jan 29 LP Dual
5. Fr Jan 31 Linear, Affine and Convex Hulls
HW# 1 
6. Mo Feb 3 Basic Solutions of Linear Systems
7. We Feb 5 Polyhedral Sets
8. Fr Feb 7 Representation Theorem
HW# 2 
9. Mo Feb 10 Fundamental Theorem of LP
10. We Feb 12 Algebraic Characterization of Basic Solutions
11. Fr Feb 14 Derivation of the Simplex Algorithm
12. Mo Feb 17 Initialization
13. We Feb 19 Basis Updates (PFI, BTRAN, FTRAN)
14. Fr Feb 21 LU Factorization & Gaussian Elimination
HW# 3 
15. Mo Feb 24 Pivoting Strategies & Error Control
16. We Feb 26 Updating the LU Factorization
17. Fr Mar 28 Pivoting Strategies & Fill-in Control
18. Mo Mar 3 Pricing
19. We Mar 5 Steepest Edge Simplex Algorithms
20. Fr Mar 7 LP Duality and Parametric Analysis
HW# 4 
21. Mo Mar 10 Convergence of the Simplex Method
22. We Mar 12 Generalized Upper Bounding
23. Fr Mar 14 Stochastic Linear Programming
HW# 5 
24. Mo Mar 17 Benders Decomposition
25. We Mar 19 Column Generation
Take Home Exam # 1 distributed
26. Fr Mar 21 Lagrangian Dual & Subgradient LP Methods
Computational Project distributed
27. Mo Mar 31 Take Home Exam # 1 solutions
28. We Apr 2 Overview of Interior Point Methods
29. Fr Apr 4 Moving through the Interior
30. Mo Apr 7 Projection
HW# 6 
31. We Apr 9 Projective Scaling
32. Fr Apr 11 Karmarkar's LP
33. Mo Apr 14 Karmarkar's Algorithm
34. We Apr 16 Karmarkar's Algorithm
35. Fr Apr 18 Proof of Polynomiality
36. Mo Apr 21 Dealing with Unknown Optimal Value
37. We Apr 23 Affine Scaling
HW# 7 
38. Fr Apr 25 Primal Affine Scaling
39. Mo Apr 28 Potential Push Method
40. We Apr 30 Dual Affine Scaling
41. Fr May 2 Implementation Issues in Interior Point Methods
HW# 8 
42. Mo May 5 Semidefinite Programming
Computational Project is due
43. We May 7 Review
Take Home Exam # 2 distributed

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