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Welcome to the RBFOpt Wiki

RBFOpt is a software for black-box (also known as derivative-free) optimization. It deals with problems of this form:

   min     f(x)

s.t.       x_L <=  x   <= x_U
           x_i in Z for all i in I and,
           x_i in R for all i not in I.

It does not assume that f(x) is known in analytical form: f(x) is simply a black-box that, given input values, produces output values. The bounds on the variables x_L, x_U are assumed to be finite. RBFOpt is especially targeted at problems for which each evaluation of the objective function f(x) is expensive (in terms of computing time, or cost, or some other measure) and we want to find a global minimum of the function with as few function evaluations as possible. Since this is a very difficult class of problems (we do not assume availability of first order derivatives), RBFOpt works best on problems that are relatively small dimensional (up to 20 variables, ideally less than 10) and for which the bounding box is not too large.

RBFOpt is implemented in Python 2.7. For questions that are not answered by the user documentation, you can use the official mailing list.

Downloads

The main RBFOpt repository is hosted on GitHub. The official page is:

https://github.com/coin-or/rbfopt

You can download the latest trunk version by obtaining a copy of the repository. Alternatively, you can download one of the available releases. Installation instructions are available together with the source on GitHub.

Documentation

Documentation for the library is available on ReadTheDocs. The HTML and PDF version are automatically built whenever the code is updated on GitHub. We recommend the HTML version, as it is easier to navigate.

http://rbfopt.readthedocs.org/en/latest/

Authors and contributors

Authors:

Contributors:

Contribute

Development of the library takes place on GitHub. You can contribute to the software on GitHub.

You can also use this page to submit a ticket if you find a bug.

Referencing RBFOpt

If you use RBFOpt, we would be grateful if you could cite the following paper (this list will be updated from time to time):

  1. Costa and G. Nannicini. RBFOpt: an open-source library for black-box optimization with costly function evaluations. Optimization Online, paper 4538.
Last modified 9 months ago Last modified on Sep 21, 2016 3:50:20 PM