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.
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