Update benchmark with PaGMO and NLopt.

The problem is that I do not have NAG, so @thomas-moulard will need to install NLopt and PaGMO so that all tests run on the same platform. Still, AFAIK our machines should have similar performances, so for now I kept your NAG results and added my results for the rest. This gives us a rough idea.

For PaGMO, you will need to set `pagmo.pc` manually (I opened an issue on their GitHub project). Note that there are tons of way to use this solver, and for now it's doing global monothread optimization without any particular tweaking (my plugin is still in alpha phase), hence the slowness. Also, since it is evolutionary, different runs lead to different results. We'll need to tackle this issue when benchmarking (e.g. give a percentage of success + average time?).

Finally, I took the [`bench.sh`](https://gist.github.com/thomas-moulard/9757879) and made a Python script out of it: [`log_to_yaml.py`](https://github.com/roboptim/roboptim-analysis/blob/master/src/roboptim/analysis/log_to_yaml.py). It relies on https://github.com/roboptim/roboptim-core/pull/57 and can be improved easily (e.g. to add a YAML file containing problem data: objective function, constraints, bounds etc.).