
Convex Optimization (MOOC) by Stephen Boyd - rpm4321
https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about
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albertzeyer
Isn't the problem that in most real-world problems, the optimization function
is never convex? At least in many Machine Learning problems (e.g. optimizing a
neural network), it will not be convex.

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fmap
For many machine learning problems you can find an equivalent problem which is
convex. This seems to be the method of choice for dealing with things like
Support Vector Machines. Many academics seem to dislike neural networks
because of how easy it is to get stuck in local minima and how hard it is to
say anything useful about the result. This won't happen with techniques based
on convex optimization.

Outside of machine learning solving a convex relaxation of a discrete
optimization problem and then trying to round the result is one of the
standard techniques in optimization. This is used all over the place, from
straightforward linear relaxations for ILPs through the semidefinite
relaxations of problems such as max cut. You start with an intractable
problem, embed it into a tractable problem and use the result to gain
information about the solutions to your original problem.

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superfx
I took this class in person at Stanford, and it's hands down one of the best
and most useful courses offered in CS/ML/Stats. If the online course is
anything like the real course then this is a must.

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platz
"You will use matlab and CVX"

Not sure what CVX is, but if Octave isn't sufficient, pretty much a non-
starter.

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

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ics
Same for MIT2.01x I think. It's good product placement for them but definitely
makes the forums less interesting than other courses where you have people
working together to demonstrate how to use _some program /language_ to solve
problems in the course.

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gtani
from the page) the text:
[http://www.stanford.edu/~boyd/cvxbook/](http://www.stanford.edu/~boyd/cvxbook/)

which has a computational LA companion, which is great:

[http://www.ee.ucla.edu/~vandenbe/103/reader.pdf](http://www.ee.ucla.edu/~vandenbe/103/reader.pdf)

Eijkhout and Demmel's books would also be good reading

[http://www.tacc.utexas.edu/~eijkhout/Articles/EijkhoutIntroT...](http://www.tacc.utexas.edu/~eijkhout/Articles/EijkhoutIntroToHPC.pdf)

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rajacombinator
Great class. But I shed a tear for the TAs who will manage the MOOC process.
(Assuming they have to manage something.)

