Course Program for Optimisation 2006

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28/8 Introduction (Chapter 1). Line search (Chapter 2).
29/8 2:1,2,3(5 iterations only),4ac,5 1:10b,2ae,3,4,5
30/8 Multidimensional search: Steepest descent, Newton's method, modified Newton methods (3.1-3.4)

4/9 Some matrix theory. (Appendix A). Conjugate directions. (3.5.1-3.5.2)
5/9 1:13 A:1,2,5 3:1,3,7,8,9,11 1:11
6/9 Methods using conjugate directions. The least squares problem. (3.5-3.7)

11/9 Convex sets. (4.1-4.2) Farkas' theorem. Cones. (4.3-4.5)
12/9 3:13,15,17,18,20 4:5,6,7,8,11,12
13/9 Linear programming. (5.1-5.2)

18/9 Linear programming. (5.2-5.4)
19/9 4:17,19 5:6,7,8,12,13,14
20/9 Convex functions. (6.1-6.2)

25/9 Optimization of convex functions. (6.3-6.4). Introduction to constrained optimization. (7.1-7.2)
26/9 5:15 6:2,7,8,9,10,12,17,18,19
27/9 Constrained optimization, necessary conditions. (7.2-7.3)

2/10 Constrained optimization; sufficient conditions. (7.3-7.4)
3/10 7:3,6,7,8,10,11,12,14,16,18,24,26
4/10 More on constrained optimization. (7.4-7.6). Duality. (Chapter 8)

  9/10 Penalty and barrier functions. (Chapter 9).
10/10 8:1,2,4,5   9:1,3
11/10 Revision

Andrey Ghulchak