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A Trust-Region Algorithm for Global Optimization

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Abstract

We consider the global minimization of a bound-constrained function with a so-called funnel structure. We develop a two-phase procedure that uses sampling, local optimization, and Gaussian smoothing to construct a smooth model of the underlying funnel. The procedure is embedded in a trust-region framework that avoids the pitfalls of a fixed sampling radius. We present a numerical comparison to three popular methods and show that the new algorithm is robust and uses up to 20 times fewer local minimizations steps.

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Authors and Affiliations

  1. Dipartimento Sistemi e Informatica, Università di Firenze, via di S. Marta 3, Firenze, 50129, Italy

    Bernardetta Addis

  2. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA

    Sven Leyffer

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  1. Bernardetta Addis
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  2. Sven Leyffer
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Correspondence to Bernardetta Addis.

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Addis, B., Leyffer, S. A Trust-Region Algorithm for Global Optimization. Comput Optim Applic 35, 287–304 (2006). https://doi.org/10.1007/s10589-006-8716-2

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  • DOI: https://doi.org/10.1007/s10589-006-8716-2

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