On Mon, Mar 20, 2017 at 11:48:05PM -0600, Tyler Zhu wrote:
> Dear All,
> 
> Nice to meet you guys here! My name is Rui, a PhD student in
> University of Alberta. My research interests include distributed
> machine learning, low-rank matrix completion, and other exciting
> topics. I have been an MLPack user for a while in my research and I’m
> really fascinated by this great library and I’m willing to make
> contributions to this great machine learning framework.

Hi Rui!

It seems GSoC is quite popular among University of Alberta students this
year. :)

> I’m quite interested in “Low rank/sparse optimization using
> Frank-Wolfe". Frank-Wolfe is an old but famous algorithm in
> optimization. It has the same convergence rate with (projected)
> gradient descent, which is a more popular method in optimization. If
> it is not efficient to calculate the projection, Frank-Wolfe is one of
> the best alternative. Such example can be found in various papers,
> e.g., low-rank matrix completion and orthogonal matching pursuit. 
> 
> I think this algorithm can be implemented as a new optimizer since it
> can be used in may applications. My preliminary plan is to borrow some
> ideas from existing implementations of other optimizers for this
> project, and the major work is to implement the “argmin” step, or also
> called “conditional gradient” in the literature.

I think this is a good approach.  Keep in mind that just the
implementation of F-W might not be enough to fill a whole summer, so it
may be worthwhile to propose some additional work on top of that.  Maybe
some extensions of F-W or some applications might work.  There has been
quite some discussion on this project over the years on the mailing
list; you might check out some of those posts for more information:

http://lists.mlpack.org/pipermail/mlpack/2017-March/003185.html
http://lists.mlpack.org/pipermail/mlpack/2016-March/002575.html

Let me know if I can clarify anything.

Thanks,

Ryan

-- 
Ryan Curtin    | "I am a golden god!"
[email protected] |   - Russell
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