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A new nonmonotone line-search trust-region approach for nonlinear systems

Author

Listed:
  • Morteza Kimiaei

    (University of Vienna)

  • Farzad Rahpeymaii

    (Payame Noor University)

Abstract

This paper introduces a new derivative-free trust-region algorithm for solving nonlinear systems, based on a new nonmonotone technique and an adaptive radius strategy. It is shown that we can generate the small (large) steps and radii in the cases where iterations are near (far away from) the optimizer. Such a nonmonotone strategy is embedded into the trust region framework and Armijo line search to face with problems which have the narrow curved valley. To prevent resolving the trust-region subproblem, the nonmonotone Armijo line search is used whenever iterations are unsuccessful. In each iteration, the adaptive radius strategy is constructed based on the norm of the best function values. The global and q-quadratic rate of convergence of the new algorithm is proved. Computational results are reported.

Suggested Citation

  • Morteza Kimiaei & Farzad Rahpeymaii, 2019. "A new nonmonotone line-search trust-region approach for nonlinear systems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 199-232, July.
    • Handle: RePEc:spr:topjnl:v:27:y:2019:i:2:d:10.1007_s11750-019-00497-2
    DOI: 10.1007/s11750-019-00497-2
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    References listed on IDEAS

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    1. X. J. Tong & L. Qi, 2004. "On the Convergence of a Trust-Region Method for Solving Constrained Nonlinear Equations with Degenerate Solutions," Journal of Optimization Theory and Applications, Springer, vol. 123(1), pages 187-211, October.
    2. Keyvan Amini & Mushtak A. K. Shiker & Morteza Kimiaei, 2016. "A line search trust-region algorithm with nonmonotone adaptive radius for a system of nonlinear equations," 4OR, Springer, vol. 14(2), pages 133-152, June.
    3. Gonglin Yuan & Zehong Meng & Yong Li, 2016. "A Modified Hestenes and Stiefel Conjugate Gradient Algorithm for Large-Scale Nonsmooth Minimizations and Nonlinear Equations," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 129-152, January.
    4. Ju-liang Zhang & Yong Wang, 2003. "A new trust region method for nonlinear equations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 58(2), pages 283-298, November.
    5. Stefania Bellavia & Maria Macconi & Sandra Pieraccini, 2012. "Constrained Dogleg methods for nonlinear systems with simple bounds," Computational Optimization and Applications, Springer, vol. 53(3), pages 771-794, December.
    6. Jinyan Fan & Jianyu Pan, 2011. "An improved trust region algorithm for nonlinear equations," Computational Optimization and Applications, Springer, vol. 48(1), pages 59-70, January.
    7. Hamid Esmaeili & Morteza Kimiaei, 2016. "A trust-region method with improved adaptive radius for systems of nonlinear equations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(1), pages 109-125, February.
    8. Hamid Esmaeili & Morteza Kimiaei, 2016. "A trust-region method with improved adaptive radius for systems of nonlinear equations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(1), pages 109-125, February.
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