Cvx is basically a convex solver that is called from matlab. Boyd wrote it. I'm not sure how easy it would be to divorce it from matlab, but there is a lot of existing instructional material built off of cvx, including examples in Boyd's book on convex optimization.

If you think you might be interested in this topic, it's worthwhile getting a matlab license. A lot of published academic code in this field is implemented with cvx.

From the web page:

Do we need to purchase a Matlab license to take this course?

You will be able to use Matlab under a special license provided to you as a course participant to use for the CVX101 course. This is a limited license for the duration of the course and is intended to be used only for course work and not for commercial purposes. Alternatively, you may choose to access Matlab via a web interface if you prefer not to install Matlab on your computer.

It's incredible how many of my colleagues work with Matlab.

Many enjoy the high integration of the environment. Thus it's somehow predestined for prototyping. I'm not sure if Octave can compete, considering the GUI features. However, python, especially ipython, is a real alternative to me. In fact, I use ipython for my prototyping which evolves into Fortran for the following implementation on the high performance cluster late on.

As one of the other commenters mentioned, Boyd or -- more likely -- his PhD/u/grads wrote CVX, so I'm not sure how much this course allows you to implement and play with ideas presented using python instead of Matlab.

Mathworks now give out student licenses for MOOCs. At least they did for some Udacity courses I took earlier this year.