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Modified Extended Cutting Plane Algorithm for Mixed Integer Nonlinear Programming

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Optimization of Complex Systems: Theory, Models, Algorithms and Applications (WCGO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 991))

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Abstract

In this work, we propose a modification on the Extended Cutting Plane algorithm (ECP) that solves convex mixed integer nonlinear programming problems. Our approach, called Modified Extended Cutting Plane (MECP), is inspired on the strategy of updating the set of linearization points in the Outer Approximation algorithm (OA). Computational results over a set of 343 test instances show the effectiveness of the proposed method MECP, which outperforms ECP and is competitive to OA.

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Author information

Authors and Affiliations

  1. College of Computer Science, Federal University of Uberlandia, Uberlândia, Brazil

    Wendel Melo

  2. Institute of Mathematics and COPPE, Federal University of Rio de Janeiro, Janeiro, Brazil

    Marcia Fampa

  3. National Laboratory for Scientific Computing (LNCC) of the Ministry of Science, Technology and Innovation, Petrópolis, Brazil

    Fernanda Raupp

Authors
  1. Wendel Melo
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  2. Marcia Fampa
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  3. Fernanda Raupp
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Corresponding author

Correspondence to Wendel Melo .

Editor information

Editors and Affiliations

  1. Computer science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai An Le Thi

  2. Computer Science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai Minh Le

  3. Laboratory of Mathematics, National Institute for Applied Sciences (INSA)-Rouen Normadie, Saint-Étienne-du-Rouvray Cedex, France

    Tao Pham Dinh

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Melo, W., Fampa, M., Raupp, F. (2020). Modified Extended Cutting Plane Algorithm for Mixed Integer Nonlinear Programming. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_43

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