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Decoupling linear and nonlinear regimes: an evaluation of efficiency for nonlinear multidimensional optimization

Author

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  • Christopher M. Cotnoir

    (Old Dominion University)

  • Balša Terzić

    (Old Dominion University
    Old Dominion University)

Abstract

Solving a large subset of multidimensional nonlinear optimization problems can be significantly improved by decoupling their intrinsically linear and nonlinear parts. This effectively decreases the dimensionality of the problem, reduces the search space and improves the efficiency of the optimization. This decoupled approach is generalized with mathematical formalism and its superiority over standard methods empirically verified and quantified on a couple of examples involving $$\chi ^2$$ χ 2 curve fitting to data.

Suggested Citation

  • Christopher M. Cotnoir & Balša Terzić, 2017. "Decoupling linear and nonlinear regimes: an evaluation of efficiency for nonlinear multidimensional optimization," Journal of Global Optimization, Springer, vol. 68(3), pages 663-675, July.
    • Handle: RePEc:spr:jglopt:v:68:y:2017:i:3:d:10.1007_s10898-016-0480-y
    DOI: 10.1007/s10898-016-0480-y
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    References listed on IDEAS

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    1. Remigijus Paulavičius & Julius Žilinskas, 2014. "Simplicial Lipschitz optimization without the Lipschitz constant," Journal of Global Optimization, Springer, vol. 59(1), pages 23-40, May.
    2. Antanas Žilinskas & Julius Žilinskas, 2013. "A hybrid global optimization algorithm for non-linear least squares regression," Journal of Global Optimization, Springer, vol. 56(2), pages 265-277, June.
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    Cited by:

    1. Sanpeng Zheng & Renzhong Feng & Aitong Huang, 2020. "The Optimal Shape Parameter for the Least Squares Approximation Based on the Radial Basis Function," Mathematics, MDPI, vol. 8(11), pages 1-20, November.

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