# 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 (a 10-40 variables) and for which the bounding box is not too large. However, it has been successfully employed on problems on much larger sizes.

**RBFOpt** is implemented in Python 3 and 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:

- Giacomo Nannicini : project manager, main developer. http://researcher.watson.ibm.com/researcher/view.php?person=us-nannicini

Contributors:

- Alberto Costa : numerical testing, useful discussion and ideas. http://www.lix.polytechnique.fr/~costa/

- Giorgio Sartor : numerical testing, useful discussion and ideas.

## 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):

- Costa and G. Nannicini.
*RBFOpt: an open-source library for black-box optimization with costly function evaluations.*Optimization Online, paper 4538.