
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 (3.13.4) 

5/9 
Some matrix theory. (Appendix A). Conjugate directions. (3.5.13.5.2)

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. (3.53.7) 

12/9 
Convex sets. (4.14.2) Farkas' theorem. Cones. (4.34.5) 
13/9 
3:13,15,17,18,20 4:5,6,7,8,11,12 
14/9 
Linear programming. (5.15.2) 

19/9 
Linear programming. (5.25.4) 
20/9 
4:17,19 5:6,7,8,12,13,14 
21/9 
Convex functions. (6.16.2) 

26/9 
Optimization of convex functions. (6.36.4). Introduction to
constrained optimization. (7.17.2) 
27/9 
5:15 6:2,7,8,9,10,12,17,18,19 
28/9 
Constrained optimization, necessary conditions. (7.27.3) 

3/10 
Constrained optimization; sufficient conditions. (7.37.4)

4/10 
7:3,6,7,8,10,11,12,14,16,18,24,26 
5/10 
More on constrained optimization. (7.47.6). Duality. (Chapter
8) 

10/10 
Penalty and barrier functions. (Chapter 9). 
11/10 
8:1,2,4,5 9:1,3 
12/10 
Revision 
