This toolbox contains the implementation of what I consider to be fundamental algorithms
for non-smooth convex optimization of structured functions. These algorithms might not be the fasted
(although they certainly are quite efficient), but they all have a simple implementation in term
of black boxes (gradient and proximal mappings, given as callbacks). However, you should have
some knowledge about what is a gradient operator and a proximal mapping in order to be able
to use this toolbox on your own problems. I suggest you have a look at the
"suggested readings" for some more information about all this.
Actually, I am working with unmixing techniques for images and I want to apply sparse positive matrix factorization. I find your code could be very useful to me but, I do not have clear how to use the code. Can you help me sending me maybe a readme file with more details. I am really excited to use these functions.
Thanks.
02 Aug 2008
dafav ffaffb
There are some file lost in you code.Please give a whole code.
02 Apr 2008
Prasad Gurlahosur
29 Nov 2007
Jianwei Ma
Hello Gabriel, Thanks very much for your code. But it will be better to provide a readme file with more detailed description.