[mlpack] GSoC 2014 Simulated Annealing Project

[Abhishek Laddha]

Simulated Annealing is used for k-means:
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4771876&tag=1

A Simulated Annealing Methodology for clusterwise linear regression:
http://link.springer.com/article/10.1007%2FBF02296405

Could you tell me bug related to optimizers which can be helpful in
understanding the implementation of current optimizers?

On Tue, Feb 25, 2014 at 11:04 PM, Ryan Curtin <gth671b at mail.gatech.edu>wrote:

> On Tue, Feb 25, 2014 at 11:31:06AM +0530, Abhishek Laddha wrote:
> > Hi all,
> > I am Abhishek Laddha a 3rd year undergraduate student majoring in
> > Mathematics and Computing from Indian Institute of Technology Delhi
> (IITD),
> > India. I am really interested in field on machine learning and regularly
> > taking the similar courses in my undergraduate studies. I am familiar
> with
> > C++ and some of machine learning techniques.
> >
> > I would like to work on project "Simulated Annealing Optimizer" for gsoc
> > 2014. I have already used simulated annealing technique in my project.
> > Simulated annealing is a search in the solution space of constrained
> > optimization problem which is good in avoiding local minima but it is
> slow.
> > I have found the the some paper of using simulated annealing technique in
> > K-means algorithm and linear regression. Could you more elaborate on this
> > project ?
>
> Hi Abhishek,
>
> Can you link me to the paper where simulated annealing is used for
> k-means and linear regression?
>
> To understand the project better, take a look at the optimizers already
> present in src/mlpack/core/optimizers/ to get an idea of how they are
> written.  Most importantly, they are generic -- so, they can work with
> any potential function to be optimized, assuming that function
> implements the Evaluate() function and Gradient() function (although for
> simulated annealing, Gradient() is not necessary).
>
> > I have downloaded, build and  gone through the some mlpack tutorials.
> Could
> > you give me direction or provide resources for how to start working on
> this
> > project?
>
> If you are interested in contributing, a list of bugs can be found here:
>
> http://www.mlpack.org/trac/report/10
>
> To get an idea of how mlpack is actually used, it would probably be a
> good idea to download some datasets and perform tasks with them using
> mlpack; for instance, you could find a dataset to perform regression on
> with both linear_regression and LARS.  You could also perform
> nearest-neighbor search, furthest-neighbor search, max-kernel search,
> and other tasks like that.
>
> Let me know if I can answer any more questions.
>
> Thanks,
>
> Ryan
>
> --
> Ryan Curtin    | "Weeee!"
> ryan at ratml.org |   - Bobby
>



-- 
Abhishek Laddha
Mathematics And Computing
IIT Delhi
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