Commit ec17c45 brings usable minos type confidence intervals computation to the library.

This is still experimental and though unit-tested, it has not been thoroughly tested on the regular function set. In order to try it out at this early stage, for a deviation of 0.1 from the found mininum, and using otherwise default parameters for computing the profile likelihood:

./test_functions -fname fsphere -dim 10 -le

yields:

confidence intervals:
dim 0 in [-0.285108,0.285108]
dim 1 in [-0.288124,0.288124]
dim 2 in [-0.289218,0.289218]
dim 3 in [-0.285644,0.285644]
dim 4 in [-0.287044,0.287044]
dim 5 in [-0.298543,0.298543]
dim 6 in [-0.281206,0.281206]
dim 7 in [-0.297881,0.297881]
dim 8 in [-0.275348,0.275348]
dim 9 in [-0.294037,0.294037]

and

./test_functions -fname elli -dim 10 -le

yields:

confidence intervals:
dim 0 in [-0.281297,0.281297]
dim 1 in [-0.131263,0.131263]
dim 2 in [-0.0621184,0.0621184]
dim 3 in [-0.0288797,0.0288797]
dim 4 in [-0.013169,0.013169]
dim 5 in [-0.00582101,0.00582101]
dim 6 in [-0.00281503,0.00281503]
dim 7 in [-0.00131911,0.00131911]
dim 8 in [-0.000600367,0.000600367]
dim 9 in [-0.000287616,0.000287616]

The library also embeds the ability to compute a 'full' profile likelihood 'curve', that is a set of points such that confidence intervals can later be obtained for various values of the deviation from the original minimum.

At this stage, the confidence intervals returned on rosenbrock and rastrigin cannot be validated and may well be faulty (results vary with values of -le_samplesize).

Implement the finding of true confidence intervals (aka MINOS) #30

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