[SciPy-Dev] GSoC SciPy Optimization Ideas

[Pamphile Roy]

> I tend to agree with Daniel here, randomly choosing 50 points in a high-dimensional optimization space is not going to give any advantage. And why 50? 
> 
> The initialization part is one of the most important (and difficult to get right) part of any optimization algorithm, but this is mostly true for global ones: differential evolution, SHGO, Dual Annealing they’re all have their own way. Some of these and many others (especially local algorithms) rely on the user to explicitly pass an initial guess and take it from there.

Agreed here, the random sampling must not be totally random in order to cover the parameter space in the most efficient way.
The global optimizers all use QMC methods (scipy.stats.qmc) so here you can just rely on these too.

Cheers,
Pamphile

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