| Day |
Sections |
Topic |
Assigned / Due |
| M 8/27 |
1.1, 1.2 |
introduction; describe five examples; brainstorm on their solution
|
Assignment #1 (PDF) |
| W 8/29 |
1.3, 1.4, 1.5, 1.6 |
cont.; optimization overview |
|
| F 8/31 |
2.1, 2.2 |
basic definitions |
|
| M 9/3 |
|
no class: Labor Day |
|
| W 9/5 |
2.3 |
convexity |
A #1 due
(at start of class)
Assign. #2 (PDF) |
| F 9/7 |
B.4, B.5, B.6, B.7 |
derivatives (gradient, Hessian, Jacobian) and convexity |
|
| M 9/10 |
|
cont. |
|
| W 9/12 |
|
cont. |
A #2 due
(at start of class) |
| F 9/14 |
2.4 |
iterative algorithms
Newton's method in Matlab (PDF) |
Assign. #3 (PDF) |
| M 9/17 |
2.7 |
Newton's method for systems of equations |
|
| W 9/19 |
2.6 |
Taylor's theorem in N dimensions
vistaylor.m
Newton convergence fractals: newtonfractal.m (images 1, 2)
and from Stefan A., the surprising image for 7.10 |
|
| F 9/21 |
2.5 |
rates of convergence |
A #3 due |
| M 9/24 |
3.1 |
linear constraints |
A #3 due
Assign. #4 (PDF)
|
| W 9/26 |
|
cont. |
|
| F 9/28 |
3.2 |
linear algebra |
|
| M 10/1 |
|
no lecture: Bueler away
slides on steepest descent (PDF) |
Assign. #5 (PDF) |
| W 10/3 |
4.1 |
linear programming problems |
|
| F 10/5 |
4.2 |
standard form |
A #4 due |
| M 10/8 |
4.3 |
basic feasible solutions |
A #5 due |
| W 10/10 |
4.4 |
theorems for standard form problems |
A #5 due
Assign. #6 (PDF) |
| F 10/12 |
5.1, 5.2 |
the simplex method |
|
| M 10/15 |
5.3 |
more on the simplex method
template for simplex method (PDF)
... filled-in example (PDF) |
|
| W 10/17 |
5.4 |
more on the simplex method
mysimplex.m
... booklpexample.m
... kleeminty.m |
|
| F 10/19 |
6.1,6.2 |
the dual problem |
A #6 due |
| M 10/22 |
|
strong duality
Review Guide for Midterm (PDF)
... page number "solutions" (PDF) |
|
| W 10/24 |
11.1, 11.2 |
unconstrained optimization |
Assign. #7 (PDF) |
| F 10/26 |
|
MIDTERM EXAM in class
... solutions to Midterm Exam (PDF) |
MIDTERM EXAM |
| M 10/29 |
11.3 |
Newton's method for optimization
description of the project (PDF)
... empty project (PDF)
... LaTex source for empty project (.tex) |
|
| W 10/31 |
11.4 |
guaranteeing descent |
|
| F 11/2 |
11.5 |
guaranteeing convergence with line search |
A #7 due |
| M 11/5 |
|
cont. |
PROJECT I DUE
A #7 due |
| W 11/7 |
12.2 |
on software; steepest descent |
Assign. #8 (PDF) |
| F 11/9 |
12.3 |
quasi-Newton methods
PROJECT PART I DUE in class |
PROJECT I DUE |
| M 11/12 |
|
cont.
image of table on board |
|
| W 11/14 |
|
cont.; overview of upcoming content
blank part I evaluation (PDF) |
|
| F 11/16 |
13.5, 12.4, 12.5 |
limited-memory quasi-Newton
finite difference derivatives
derivative-free methods |
A #8 due |
| M 11/19 |
13.2, 13.3, 13.4 |
(linear) conjugate gradients
nonlinear conjugate gradients
truncated Newton methods |
A #8 due
Assign. #9 (PDF) |
| 11/21-23 |
|
no class: Thanksgiving holiday |
|
| M 11/26 |
14.1 |
constrained nonlinear optimization |
|
| W 11/28 |
14.2, 15.2 |
linear equality constraints: optimality and reduced Newton |
|
| F 11/30 |
14.3 |
Lagrange multipliers |
|
| M 12/3 |
14.4 |
linear inequality constraints
Final Exam (PDF) |
A #9 due |
| W 12/5 |
14.5 |
KKT conditions |
|
| F 12/7 |
14.6 |
constrained methods (last lecture) |
|
| T 12/11 |
|
PROJECT PART II DUE in my Chapman 101 box at 5pm
blank part II evaluation (PDF) |
PROJECT II DUE |
| Th 12/13 |
|
take-home FINAL EXAM DUE in my Chapman 101 box at 5pm |
FINAL EXAM DUE |
