[mlpack] GSoC 2019

[danipozo at autistici.org]

Hello,

I am Daniel Pozo Escalona, fourth year computer science and mathematics 
student at the University of Granada, Spain.
I am interested in one of the projects proposed for GSoC 2019: 
implementing different variants of Particle Swarm Optimization.

If I have understood correctly the structure of the project, an 
implementation should:

- Add a directory to include/ensmallen_bits in the ensmallen repo, where 
the code for PSO would reside.
- Add a trait for functions that provide constraints.

I have some questions on these points:

- Given that there are many variants of PSO that seem worth 
implementing, such as using or not velocity clamping
   or a constriction coefficient to prevent velocity explosion, fully 
informed PSO, etc., how should I go about
   reflecting variations of the algorithm? I have observed that there are 
different directories for different versions
   of gradient descent, should I imitate this?

- From what I have read in some papers I have got the impression that 
the usual way to implement constrained optimization
   is by modelling constraints as penalty functions, using functions that 
take infinite values to represent hard constraints.
   Should traits for constraints reflect this?

Regards,
Daniel.