Py-BOBYQA: Derivative-Free Optimizer for Bound-Constrained Minimization

Release: 1.1a0

Date: 03 August 2018

Author: Lindon Roberts

Py-BOBYQA is a flexible package for finding local solutions to nonlinear, nonconvex minimization problems (with optional bound constraints), without requiring any derivatives of the objective. Py-BOBYQA is a Python implementation of the BOBYQA solver by Powell (documentation here). It is particularly useful when evaluations of the objective function are expensive and/or noisy.

That is, Py-BOBYQA solves

\[\begin{split}\min_{x\in\mathbb{R}^n} &\quad f(x)\\ \text{s.t.} &\quad a \leq x \leq b\end{split}\]

Full details of the Py-BOBYQA algorithm are given in our paper: C. Cartis, J. Fiala, B. Marteau and L. Roberts, Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, technical report, University of Oxford, (2018).

If you are interested in solving least-squares minimization problems, you may wish to try DFO-LS, which has the same features as Py-BOBYQA (plus some more), and exploits the least-squares problem structure, so performs better on such problems.

New feature: global optimization heuristic (July 2018)! Py-BOBYQA now has a heuristic for global optimization (see Using Py-BOBYQA for details). As this is a heuristic, there are no guarantees it will find a global minimum, but it is more likely to escape local minima if there are better values nearby.

Py-BOBYQA is released under the GNU General Public License. Please contact NAG for alternative licensing.


This software was developed under the supervision of Coralia Cartis, and was supported by the EPSRC Centre For Doctoral Training in Industrially Focused Mathematical Modelling (EP/L015803/1) in collaboration with the Numerical Algorithms Group.