BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables

Margherita Porcelli, Philippe Toint

Research output: Contribution to journalArticle

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Abstract

A direct-search derivative-free Matlab optimizer for bound-constrained problems is described, whose remarkable features are its ability to handle a mix of continuous and discrete variables, a versatile interface as well as a novel self-training option. Its performance compares favorably with that of NOMAD (Nonsmooth Optimization by Mesh Adaptive Direct Search), a well-known derivative-free optimization package. It is also applicable to multilevel equilibrium- or constrained-type problems. Its easy-to-use interface provides a number of user-oriented features, such as checkpointing and restart, variable scaling, and early termination tools.

Original languageEnglish
Article number6
Number of pages28
JournalTransactions of the American Methematical Society on Mathematical Software
Volume44
Issue number1
DOIs
Publication statusPublished - 30 Jun 2017

Keywords

  • derivative-free optimization
  • trainable algorithms
  • bound constraints
  • mixed integer optimization
  • direct-search methods

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  • Projects

    DFO: Derivative free numerical algorithms for optimization

    TOINT, P., COLSON, B., Gratton, S., Tröltzsch, A. & RODRIGUES SAMPAIO, P.

    1/03/94 → …

    Project: Research

    Activities

    Università di Firenze

    Philippe Toint (Visiting researcher)

    15 Sep 201815 Oct 2018

    Activity: Visiting an external institution typesVisiting an external academic institution

    Università di Firenze

    Philippe Toint (Visiting researcher)

    1 Oct 201731 Oct 2017

    Activity: Visiting an external institution typesVisiting an external academic institution

    Prizes

    Oliver Smithies Fellow

    TOINT, Philippe (Recipient), Sep 2015

    Prize: Fellowship awarded competitively

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