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On an Efficient Use of Gradient Information for Accelerating Interval Global Optimization Algorithms

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Abstract

This paper analyzes and evaluates an efficient n-dimensional global optimization algorithm. It is a natural n-dimensional extension of the algorithm of Casado et al. [1]. This algorithm takes advantage of all available information to estimate better bounds of the function. Numerical comparison made on a wide set of multiextremal test functions has shown that on average the new algorithm works faster than a traditional interval analysis global optimization method.

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Authors and Affiliations

  1. Computer Architecture & Electronics Department, University of Almería Cta. Sacramento SN, 04120, Almería, Spain

    J.A. Martínez, L.G. Casado & I. García

  2. DEIS, Universita' della Calabria, Italy, and University of Nizhni Novgorod, Nizhni Novgorod, Russia

    Ya.D. Sergeyev

  3. Department of Applied Informatics, University of Szeged, H-6701, Szeged, Hungary

    B. Tóth

Authors
  1. J.A. Martínez
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  2. L.G. Casado
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  3. I. García
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  4. Ya.D. Sergeyev
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  5. B. Tóth
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Martínez, J., Casado, L., García, I. et al. On an Efficient Use of Gradient Information for Accelerating Interval Global Optimization Algorithms. Numerical Algorithms 37, 61–69 (2004). https://doi.org/10.1023/B:NUMA.0000049456.81410.fc

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  • DOI: https://doi.org/10.1023/B:NUMA.0000049456.81410.fc

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