This is an efficient, C++ implementation of several Bayesian optimization
algorithms. See References for some of the papers.
-Apart from the standard C++ interface, it also provides interfaces with plain C, Python and Matlab/Octave.
It combines the use of a stochastic process as a surrogate function
with some "active learning" criterion to find the optimum of an "arbitrary"
-function using very few iterations.
+function using very few iterations. It can also be used for sequential experimental
+design and stochastic bandits by selecting the adequate criterion.
-It can also be used for sequential experimental design and stochastic bandits by
-selecting the adequate criterion.
+Using old C++ standards, it can be used with many compilers in Windows,
+Linux, Mac OS. There are APIs for C/C++, Python and Matlab/Octave.
**Important:** This code is free to use. However, if you are using, or plan to use, the library, //specially if it is for research or academic purposes//, please send me an email with your name, institution and a brief description of your interest for this code (one or two lines).