[mlpack] GSOC 2016 Aspirant - Parallel Stochastic Optimisation Methods

[Ryan Curtin]

On Tue, Mar 01, 2016 at 05:39:57PM +0530, Aditya Sharma wrote:
> Hello,
> 
> I'm Aditya Sharma, a senior at Birla Institute of Technology and Science,
> Pilani, India (BITS Pilani) in the final year of my Bachelor's degree in
> Electrical and Electronics Engineering and I graduate in August 2016.
> 
> Owing to my experience in the area, I am really interested in the '
> <https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas#parallel-stochastic-optimization-methods>Parallel
> stochastic optimisation methods'
> <https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas#parallel-stochastic-optimization-methods>
> project
> idea listed on the GSOC 2016 page of mlpack and would love to know more
> details regarding the direction in which the community wants to go with
> regards to this project.

Hi Aditya,

This is a pretty open-ended project.  The goal, of course, is to
implement parallel stochastic optimization methods.  The particular
algorithms that are chosen are less important and they are up to the
student.  Hogwild! might be an interesting one to look into, for
instance.

mlpack has a nice interface for optimizers; take a look at the existing
optimizers in src/mlpack/core/optimizers/ for some examples.  Any
optimizers implemented for the project should follow the same API, so
that they work with many mlpack methods, like logistic regression, or
softmax regression, or NCA, and so forth.

I hope this is helpful; please let me know if I can clarify anything.

Thanks!

Ryan

-- 
Ryan Curtin    | "But feel, to the very end, the triumph of being
ryan at ratml.org | alive!"  - Jöns