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