[mlpack] GSoC 2014 simulated annealing optimizer

[Zhihao Lou]

Dear all,

I'm a PhD student in Computer Science Department at the University of
Chicago, currently working on a scalable parallel simulated annealing
algorithm, which I recently presented a poster at SIAM Parallel Processing
2014. (Anyone interested in my poster please feel free to let me know. The
file is a few megabytes so I'm not sure if I can sent it to the mailing
list.) I'm greatly excited when I learned you need someone to work on a
simulated annealing optimizer in your Google Summer of Code 2014 ideas
posting. I believe my understanding in the simulated annealing algorithm
itself, its parallelization and implementation can add greatly to your
project.

On the other side, my committee requires me to have a lot more problems to
test against than the one test problem I shown in my poster. Working on
your project will help me to learn how to structure an optimization
algorithm to be a general purpose c++ library, which I urgently needed. I
believe such a collaboration will be mutually beneficial.

Currently, I have a working C++ template based implementation, both serial
and parallel (using MPI), that aims at maximum modularity, rather than
performance. Naturally, as an on going research project, I'll have to test
the effects of different cooling schedule or move generation independent of
other components in the code. Besides the genetic pattern formation model
(which is the main reason behind this project), I tested it on Rastrigin
function http://en.wikipedia.org/wiki/Rastrigin_function and it can solve a
1000-dimension problem in 40 minutes, with final results around 0.0005
given 0 as theoretical optimal. (And as I said, there're a lot to gain in
performance once the algorithm settles.)

Although I never used or worked with your mlpack library, I'm confident I
can learn quickly. Any help on where should I started will be deeply
appreciated.

In addtion, there's a question I'd like to ask, though. After glancing at
the paper presented at the BigLearning workshop, I'm feeling the
scalability in the paper means something different than what I'm familiar
with, i.e., parallel scalability. Can someone please elaborate a little bit
on this, and tell me what the ideal use case of mlpack would be?

I believe I'll be the perfect fit for this GSoC14 project and looking
forward to work with you.

Best regards,

Zhihao Lou
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