| Day |
Section |
Topic |
Assigned / Due |
| M 8/29 |
1 |
describe 5 examples and brainstorm on their solution
5 examples description (PDF)
comparison of Matlab/Octave/Python (PDF) |
Assignment #1 (PDF) |
| W 8/31 |
|
"laptop day": work in class on 5 examples; learn some Matlab |
|
| F 9/2 |
|
brute force solutions:
tsp.m and solution description (PDF)
beam.m, plotbeam.m, and solution description (PDF)
|
|
| M 9/5 |
|
no class: Labor Day |
|
| W 9/7 |
2.1 |
unconstrained optimization |
|
| F 9/9 |
|
review Taylor theorem (see also appendix A.1) |
A #1 DUE
Assignment #2 (PDF) |
| M 9/12 |
|
Taylor cont.
contourexample.m |
|
| W 9/14 |
|
necessary and sufficient conditions |
|
| F 9/16 |
2.2 |
overview of algorithms: steepest descent and Newton |
A #2 DUE
last day for drops
Assignment #3 (PDF)
(LaTex source as .tex)
|
| M 9/19 |
|
cont. |
|
| W 9/21 |
3.1 |
line search |
|
| F 9/23 |
|
Wolfe conditions |
|
| M 9/26 |
3.2 |
Zoutendijk theorem (theorem 3.2) |
A #3 DUE |
| W 9/28 |
|
cont.
slides demonstrating steepest descent, Newton method, and back-tracking (PDF)
|
A #3 DUE (REVISED) |
| F 9/30 |
A.2 |
rates of convergence |
Assignment #4 (PDF) |
| M 10/3 |
3.3 |
convergence rate of steepest descent on quadratics |
|
| W 10/5 |
|
convergence rate of Newton; initial implementation of BFGS |
|
| F 10/7 |
|
cont. |
|
| M 10/10 |
A.1 |
review linear algebra
discuss project expectations:
on your project (PDF)
LaTeX source for a blank project (.tex)
... compiled blank project (PDF)
|
A #4 DUE |
| W 10/12 |
|
cont.; compare computational cost of SD, BFGS, Newton |
|
| F 10/14 |
15.1 |
preview of constrained optimization: elimination, penalty methods, barrier methods |
Assignment #5 (PDF)
(LaTeX source as .tex)
|
| M 10/17 |
5.1 |
conjugate directions |
|
| W 10/19 |
|
cont.
review guide for Midterm Exam (PDF) |
|
| F 10/21 |
|
linear conjugate gradient method
Shewchuk's conjugate gradient guide (PDF) |
A #5 DUE |
| M 10/24 |
|
MIDTERM EXAM in class |
MIDTERM EXAM |
| W 10/26 |
|
cont. |
Assignment #6 (PDF)
(LaTeX source as .tex) |
| F 10/27 |
5.2 |
nonlinear conjugate gradient (NCG) |
|
| M 10/31 |
6.1 |
BFGS |
|
| W 11/2 |
|
BFGS finished
skipping through (not covering): 7.1, 7.2, 8.1, 8.2, 9.1, 9.5 |
|
| F 11/4 |
10.1 |
least-squares problems
PROJECT I DUE |
last day for withdrawals
PROJECT I DUE |
| M 11/7 |
10.2, 10.3 |
Gauss-Newton |
A #6 DUE
Assignment #7 (PDF) |
| W 11/9 |
11.1 |
Newton's method for equations (not optimization) |
|
| F 11/11 |
11.2 |
line search Newton's method |
|
| M 11/14 |
|
examples
fiverootsLS.png
fiverootsNO.png |
A #7 DUE |
| W 11/16 |
12.1 |
constrained optimization; Lagrange multipliers |
A #7 DUE (REVISED)
Assignment #8 (PDF) |
| F 11/18 |
12.2 |
constraint qualifications |
|
| M 11/21 |
12.3 |
KKT conditions |
|
| W 11/23 |
|
cont. |
|
| F 11/24 |
|
no class: Thanksgiving |
|
| M 11/28 |
13.1 |
linear programming
template for simplex method (PDF)
example 13.1 CORRECTED (PDF) |
A #8 DUE
Assignment #9 (PDF)
(LaTeX source as .tex) |
| W 11/30 |
13.2 |
cont. |
|
| F 12/2 |
13.2 |
simplex method
sample rubric for Project Part II (PDF) |
|
| M 12/5 |
|
cont. |
A #9 DUE |
| W 12/7 |
15.1 |
algorithms for constrained optimization
take-home Final Exam (PDF) |
A #9 DUE (REVISED) |
| F 12/9 |
15.3, 19.1 |
elimination of linear constraints; two interpretations of log barrier method |
|
| T 12/13 |
|
PROJECT II DUE in my Chapman 101 box at 5pm
sample rubric for Project Part II (PDF) |
PROJECT II DUE |
| Th 12/15 |
|
take-home FINAL EXAM DUE in my Chapman 101 box at 5pm
take-home Final Exam (PDF) |
FINAL EXAM DUE |
