|
| 29/8 |
Introduction (Chapter 1). Line search (Chapter 2). |
| 30/8 |
2:1,2,3(5
iterations only),4ac,5
1:10b,2ae,3,4,5 |
| 31/8 |
Multidimensional search: Steepest descent, Newton's method,
modified Newton methods (3.1-3.4) |
|
| 5/9 |
Some matrix theory. (Appendix A). Conjugate directions. (3.5.1-3.5.2)
|
| 6/9 |
1:13 A:1,2,5 3:1,3,7,8,9,11
1:11 |
| 7/9 |
Methods using conjugate directions. The least squares
problem. (3.5-3.7) |
|
| 12/9 |
Convex sets. (4.1-4.2) Farkas' theorem. Cones. (4.3-4.5) |
| 13/9 |
3:13,15,17,18,20 4:5,6,7,8,11,12 |
| 14/9 |
Linear programming. (5.1-5.2) |
|
| 19/9 |
Linear programming. (5.2-5.4) |
| 20/9 |
4:17,19 5:6,7,8,12,13,14 |
| 21/9 |
Convex functions. (6.1-6.2) |
|
| 26/9 |
Optimization of convex functions. (6.3-6.4). Introduction to
constrained optimization. (7.1-7.2) |
| 27/9 |
5:15 6:2,7,8,9,10,12,17,18,19 |
| 28/9 |
Constrained optimization, necessary conditions. (7.2-7.3) |
|
| 3/10 |
Constrained optimization; sufficient conditions. (7.3-7.4)
|
| 4/10 |
7:3,6,7,8,10,11,12,14,16,18,24,26 |
| 5/10 |
More on constrained optimization. (7.4-7.6). Duality. (Chapter
8) |
|
| 10/10 |
Penalty and barrier functions. (Chapter 9). |
| 11/10 |
8:1,2,4,5 9:1,3 |
| 12/10 |
Revision |
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