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A hybrid global optimization algorithm for non-linear least squares regression

Listed author(s):
  • Antanas Žilinskas


  • Julius Žilinskas


Registered author(s):

    A hybrid global optimization algorithm is proposed aimed at the class of objective functions with properties typical of the problems of non-linear least squares regression. Three components of hybridization are considered: simplicial partition of the feasible region, indicating and excluding vicinities of the main local minimizers from global search, and computing the indicated local minima by means of an efficient local descent algorithm. The performance of the algorithm is tested using a collection of non-linear least squares problems evaluated by other authors as difficult global optimization problems. Copyright Springer Science+Business Media, LLC. 2013

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    Article provided by Springer in its journal Journal of Global Optimization.

    Volume (Year): 56 (2013)
    Issue (Month): 2 (June)
    Pages: 265-277

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    Handle: RePEc:spr:jglopt:v:56:y:2013:i:2:p:265-277
    DOI: 10.1007/s10898-011-9840-9
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    1. Tvrdik, Josef & Krivy, Ivan & Misik, Ladislav, 2007. "Adaptive population-based search: Application to estimation of nonlinear regression parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 713-724, October.
    2. Demidenko, Eugene, 2006. "Criteria for global minimum of sum of squares in nonlinear regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1739-1753, December.
    3. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    4. Antanas Žilinskas & Julius Žilinskas, 2010. "P-algorithm based on a simplicial statistical model of multimodal functions," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 396-412, December.
    5. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
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