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| 24/10 |
Introduction (Chapter 1). Line search (Chapter 2). |
| 25/10 |
2:1a,2,3,4ac,5,6 1:10b,2ae,3,4,5
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| 26/10 |
Multidimensional search: Steepest descent, Newton's method,
modified Newton methods (3.1-3.4) |
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| 31/10 |
Some matrix theory. (Appendix A). Conjugate directions.
(3.5.1-3.5.2) |
| 1/11 |
1:13 A:1,2,4
3:1,3,7,8,9,12 1:11
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| 2/11 |
Methods using conjugate directions. The least squares
problem. (3.5-3.7) |
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| 7/11 |
Convex sets. (4.1-4.2) Farkas' theorem. Cones. (4.3-4.5) |
| 8/11 |
3:13,15,17,18,20 4:5,6,7,8,11,12
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| 9/11 |
Linear programming. (5.1-5.2) |
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| 14/11 |
Linear programming. (5.2-5.4) |
| 15/11 |
4:17,19 5:6,7,8,13,14,15
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| 16/11 |
Convex functions. (6.1-6.2) |
18/11
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Seminar exercise
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| 21/11 |
Optimization of convex functions. (6.3-6.4). Introduction
to constrained optimization. (7.1-7.2) |
| 22/11 |
5:22 6:2,7,8,9,10,12,17,18,19
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| 23/11 |
Constrained optimization, necessary conditions. (7.2-7.3) |
25/11
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Seminar exercise
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| 28/11 |
Constrained optimization; sufficient conditions. (7.3-7.4) |
| 29/11 |
7:1,6,7,8,10,11,12,14,16,18,23,29 |
| 30/11 |
More on constrained optimization. (7.4-7.6). Duality.
(Chapter 8) |
2/12
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Seminar exercise
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| 5/12 |
Penalty and barrier functions. (Chapter 9). |
| 6/12 |
8:1,2,4,5 9:1,3
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| 7/12 |
Revision |
9/12
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Repetition
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