Nonlinear Programming
Fall 2016
MATP6600 / ISYE6780
Course basics:
Grades have been posted on SIS, they should be available from Dec 10th.
Have a good break!
Course outline.
LMS.
Scores on the homeworks, midterm, and final will be available on LMS.
The course notes are available via LMS (as well as directly from this page),
and the course version of AMPL is also available via LMS.
The scores are now available on LMS.
Aggregate scores over the homeworks plus the midterm and final,
out of 530, are:
515
492
486
484
477
465
461
448
445
444
413
406
405
395
393
384
330
Material
on reserve in the library.
Exams
 Here are the
solutions to
the final.
The mean was 73% and the median was 74%.
The scores on the final were
100
86
84
81
81
78
77
74
74
72
68
67
66
63
60
60
42
 The final exam will be in class on Thursday, December 8.
You can bring one 8.5inch by 11inch sheet of handwritten notes.
You can write on both sides.
The final will cover everything in the course.
 Here are some old exams.
 The midterm exam
is due at the beginning of class on November 3.
 Here are the
solutions to
the midterm.
Scores on the midterm, out of 100, were:
100
98
95
94
89
89
81
78
77
70
68
68
66
60
54
54
42
Homework
Handwritten notes:
Introduction,
including
compressed sensing.
(Lecture 1).
Convex sets:
Convex functions
Linear programming
Optimality conditions for nonlinear programming
Duality
Algorithms
Handouts:
Linear algebra
(Lecture 1).
Subspaces, affine sets,
convex sets, and cones
(Lecture 2).
2 theorems on convex functions
(Lecture 4).
Differentiable functions
(Lecture 4).
Hessians of
smooth convex functions (Lecture 5).
Normal cones
(Lecture 7).
Extreme points and rays,
and resolution
(Lecture 8).
Dimension and faces
(Lecture 8).
The simplex
algorithm
(Lecture 9).
An iteration of the
simplex algorithm
(Lecture 9).
An example of
solving a Lagrangian dual problem.
(Lecture 17).
Nonlinear programming
packages on NEOS.
For a more detailed survey of nonlinear programming algorithms,
see
a paper
by Leyffer and Mahajan.
(Lecture 24).
Resources:
Convex Optimization
by Boyd and Vandenberghe.
A
nonlinear programming FAQ, including links to collections of
test problems.
The NEOS Server
has some nonlinear programming packages available.
An
introduction to the conjugate gradient method without the agonizing pain,
by Jonathan Shewchuk.
A survey of pattern
search and related methods
by
Charles Audet.
Issue 78
of the Mathematical Optimization Society newsletter
Optima,
discussing smoothing methods.
Slides on the
alternating direction method of multipliers,
by Stephen Boyd.
Here's the underlying
survey
paper.
John Mitchell's homepage

Dept of Mathematical Sciences Course Materials