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 backtracking (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 
leastsquares problems
PROJECT I DUE 
last day for withdrawals
PROJECT I DUE 
M 11/7 
10.2, 10.3 
GaussNewton 
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
takehome 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 

takehome FINAL EXAM DUE in my Chapman 101 box at 5pm
takehome Final Exam (PDF) 
FINAL EXAM DUE 