Global and local information in structured derivative free optimization with BFO

Margherita Porcelli, Philippe Toint

Research output: Working paper

4 Downloads (Pure)

Abstract

A structured version of derivative-free random pattern search optimization algorithms is introduced which is able to exploit coordinate partially
separable structure (typically associated with sparsity) often present in
unconstrained and bound-constrained optimization problems. This technique
improves performance by orders of magnitude and makes it possible to solve
large problems that otherwise are totally intractable by other derivative-free
methods. A library of interpolation-based modelling tools is also described,
which can be associated to the structured or unstructured versions of the
initial BFO pattern search algorithm. The use of the library further enhances
performance, especially when associated with structure. The significant gains
in performance associated with these two techniques are illustrated using a
new freely-available release of BFO which incorporates them. A interesting
conclusion of the results presented is that providing global structural
information on a problem can result in significantly less evaluations of the
objective function than attempting to building local Taylor-like models.
Original languageEnglish
PublisherArxiv
Number of pages27
Volume2001.04801
Publication statusPublished - 15 Jan 2020

Keywords

  • derivative-free optimization
  • direct-search methods
  • structured problems
  • interpolation models

Fingerprint Dive into the research topics of 'Global and local information in structured derivative free optimization with BFO'. Together they form a unique fingerprint.

  • Projects

    Cite this