Even with a teaching staff consisting of 6 TAs and the professor, the
high enrollment in the class means you are going to have to explore material
for your project on your own. Of course we’ll help guide you, but we do
expect that you’ll spend some real time exploring on your own in between
consultations with course staff.
This page contains links to various interesting and useful sites
that relate in some way to convex optimization.
It goes without saying that you’ll be periodically checking things using
google and wikipedia.
The wikipedia entry on
convex optimization (and related topics) could be
improved or extended (yes, for project credit).
When you find something that is really interesting, please let us know
and we’ll add a link on this page.
Stephen Boyd’s research page.
There’s a lot of material there, and you don’t have to know every detail
in every paper, but you should certainly take an hour or more to
browse through these papers.
EE364a web page.
We expect you to know what’s in these pages.
EE364b web page.
Yes, we know, you’re already enrolled in this class.
What we mean is that we expect you to know what’s in future lectures
in EE364b. Not all the details, of course, but at least an idea of
what we will cover later. You can’t wait until the 8th week, when we
cover model predictive control (for example), if your project relies on
model predictive control.
Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, a
paper that covers ADMM, which we’ll also cover later in the course.
This is a good method for distributed optimization.
The Convex Optimization book.
You’re expected to know pretty well the material in this book.
Unless you have a really good memory, you should be periodically browsing
Additional Exercises for Convex Optimization, available on
the book web site. These exercises are from many application areas,
including finance, machine learning, networking, wireless systems,
signal and image processing, circuit design, and biology, to name
just a few.
We expect you to have browsed these exercises.
Lieven Vandenberghe’s page, and
especially, the course pages for
The last course has a lot of great material on large-scale and
nondifferentiable convex optimization.
Emmanuel Candes’ page, which has lots
of great material, including software, papers, and class notes.
He has promised to post more of the notes and slides for
Math 301 soon.
Athena Scientific books on optimization.
You can also check the MIT courses that use some
of these books.
Here are some links to related software.
sure to check out the every extensive library of
Indeed, feel free to add to it (yes, for project credit).
a Python-based library of solvers,
which also includes an extensive library of examples.
CVXMOD, a Python modeling layer like CVX, that
uses CVXOPT as the core solver.
CVXMOD is not being developed any more.
CVXPY, a newer Python-based
convex optimization modeling system being developed by Tomas Tinoco,
with source code available here.
Anyone interested in helping develop CVXPY should contact the staff.
CVXGEN, a code generator for convex optimization.
YALMIP, a Matlab
toolbox for optimization modeling.
toolbox for formulating and solving sums of squares
(SOS) optimization problems.