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Continuous Variable Neighborhood Search (C-VNS) for Solving Systems of Nonlinear Equations

Author

Listed:
  • Jun Pei

    (School of Management, Hefei University of Technology, Hefei 230009, China)

  • Zorica Dražić

    (Faculty of Mathematics, University of Belgrade, Belgrade 11000, Serbia)

  • Milan Dražić

    (Faculty of Mathematics, University of Belgrade, Belgrade 11000, Serbia)

  • Nenad Mladenović

    (Emirates College of Technology, Abu Dhabi 41009, United Arab Emirates, Ural Federal University, Yekaterinburg 620083, Russia)

  • Panos M. Pardalos

    (Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611-6595)

Abstract

In this paper, we propose the continuous variable neighborhood search method for finding all the solutions to a nonlinear system of equations (NSEs). We transform the NSE problem into an equivalent optimization problem, and we use a new objective function that allows us to find all the zeros. Instead of the usual sum-of-squares objective function, our objective function is presented as the sum of absolute values. Theoretical investigation confirms that our objective function provides more accurate solutions regardless of the optimization method used. In addition, we achieve a trade-off (i.e., increased precision at the expense of reduced smoothness). Computational analysis of standard test instances shows that the proposed method is more precise and much faster than two recently developed methods. Similar conclusions are drawn by comparing the proposed method with many other methods in the literature.

Suggested Citation

  • Jun Pei & Zorica Dražić & Milan Dražić & Nenad Mladenović & Panos M. Pardalos, 2019. "Continuous Variable Neighborhood Search (C-VNS) for Solving Systems of Nonlinear Equations," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 235-250, April.
  • Handle: RePEc:inm:orijoc:v:31:y:2019:i:2:p:235-250
    DOI: 10.1287/ijoc.2018.0876
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    References listed on IDEAS

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    1. Ricardo Silva & Mauricio Resende & Panos Pardalos, 2014. "Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm," Journal of Global Optimization, Springer, vol. 60(2), pages 289-306, October.
    2. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    3. Drazic, Milan & Lavor, Carlile & Maculan, Nelson & Mladenovic, Nenad, 2008. "A continuous variable neighborhood search heuristic for finding the three-dimensional structure of a molecule," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1265-1273, March.
    4. Andreas Fischer & Markus Herrich & Alexey Izmailov & Mikhail Solodov, 2016. "Convergence conditions for Newton-type methods applied to complementarity systems with nonisolated solutions," Computational Optimization and Applications, Springer, vol. 63(2), pages 425-459, March.
    5. Pierre Hansen & Nenad Mladenović & Raca Todosijević & Saïd Hanafi, 2017. "Variable neighborhood search: basics and variants," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 423-454, September.
    6. Yongquan Zhou & Qifang Luo & Huan Chen, 2013. "A Novel Differential Evolution Invasive Weed Optimization Algorithm for Solving Nonlinear Equations Systems," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-18, December.
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    Cited by:

    1. Angelo Sifaleras, 2023. "In memory of Professor Nenad Mladenović (1951–2022)," SN Operations Research Forum, Springer, vol. 4(1), pages 1-18, March.
    2. Vesna Radonjić Ɖogatović & Marko Ɖogatović & Milorad Stanojević & Nenad Mladenović, 2020. "Revenue maximization of Internet of things provider using variable neighbourhood search," Journal of Global Optimization, Springer, vol. 78(2), pages 375-396, October.
    3. Sergio Consoli & Jan Korst & Steffen Pauws & Gijs Geleijnse, 2020. "Improved metaheuristics for the quartet method of hierarchical clustering," Journal of Global Optimization, Springer, vol. 78(2), pages 241-270, October.
    4. Xiangjing Lai & Jin-Kao Hao & Renbin Xiao & Fred Glover, 2023. "Perturbation-Based Thresholding Search for Packing Equal Circles and Spheres," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 725-746, July.
    5. Shaowen Lan & Wenjuan Fan & Kaining Shao & Shanlin Yang & Panos M. Pardalos, 2022. "A column-generation-based approach for an integrated service planning and physician scheduling problem considering re-consultation," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3446-3476, December.

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