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

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Abstract

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.

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Authors and Affiliations

  1. Institute of Mathematics and Informatics, Vilnius University, Akademijos 4, 08663, Vilnius, Lithuania

    Antanas Žilinskas & Julius Žilinskas

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  1. Antanas Žilinskas
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  2. Julius Žilinskas
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Correspondence to Antanas Žilinskas.

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Žilinskas, A., Žilinskas, J. A hybrid global optimization algorithm for non-linear least squares regression. J Glob Optim 56, 265–277 (2013). https://doi.org/10.1007/s10898-011-9840-9

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