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A Python/C library for bound-constrained global optimization with continuous GRASP

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Abstract

This paper describes \({\texttt{libcgrpp}}\) , a GNU-style dynamic shared Python/C library of the continuous greedy randomized adaptive search procedure (C-GRASP) for bound constrained global optimization. C-GRASP is an extension of the GRASP metaheuristic (Feo and Resende, 1989) and has been used to solve unstable and nondifferentiable problems, as well as hard global optimization problems, such as chemical equilibrium systems and robot kinematics applications (Hirsch et al. in Optim lett 1:201–212, 2007). After a brief introduction to C-GRASP, we show how to download, install, configure, and use the library through an illustrative example.

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Authors and Affiliations

  1. Centro de Informática (CIn), Federal University of Pernambuco, Av. Prof. Luís Freire s/n, Cidade Universitária, Recife, PE, Brazil

    R. M. A. Silva

  2. Algorithms and Optimization Research Department, AT&T Labs Research, 180 Park Avenue, Room C241, Florham Park, NJ, 07932, USA

    M. G. C. Resende

  3. Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, Gainesville, FL, 32611, USA

    P. M. Pardalos

  4. Raytheon Company, Intelligence and Information Systems, 300 Sentinel Drive, Annapolis Junction, MD, 20701, USA

    M. J. Hirsch

Authors
  1. R. M. A. Silva
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  2. M. G. C. Resende
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  3. P. M. Pardalos
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  4. M. J. Hirsch
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Corresponding author

Correspondence to M. G. C. Resende.

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Silva, R.M.A., Resende, M.G.C., Pardalos, P.M. et al. A Python/C library for bound-constrained global optimization with continuous GRASP. Optim Lett 7, 967–984 (2013). https://doi.org/10.1007/s11590-012-0475-7

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  • DOI: https://doi.org/10.1007/s11590-012-0475-7

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