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Derivative-Free Optimization for Population Dynamic Models

  • Ute SchaarschmidtEmail author
  • Trond Steihaug
  • Sam Subbey
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 359)

Abstract

Quantifying populations in changing environments involves fitting highly non-linear and non-convex population dynamic models to distorted observations. Derivatives of the objective function with respect to parameters might be expensive to obtain, unreliable or unavailable.

The aim of this paper is to illustrate the use of derivative-free optimization for estimating parameters in continuous population dynamic models described by ordinary differential equations. A set of non-linear least squares problems is used to compare several solvers in terms of accuracy, computational costs and robustness. We also investigate criteria for a good optimization method which are specific to the type of objective function considered here. We see larger variations in the performances of the derivative-free methods when applied for parameter estimation in population dynamic models than observed for standard noisy benchmark problems.

Keywords

Parameter Estimation Problem Direct Search Method Population Dynamic Model Mesh Adaptive Direct Search Pattern Search Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of BergenBergenNorway
  2. 2.Institute of Marine ResearchBergenNorway

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