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Journal of Global Optimization

, Volume 73, Issue 1, pp 113–151 | Cite as

Convergence-order analysis for differential-inequalities-based bounds and relaxations of the solutions of ODEs

  • Spencer D. Schaber
  • Joseph K. Scott
  • Paul I. Barton
Article
  • 99 Downloads

Abstract

For the performance of global optimization algorithms, the rate of convergence of convex relaxations to the objective and constraint functions is critical. We extend results from Bompadre and Mitsos (J Glob Optim 52(1):1–28, 2012) to characterize the convergence rate of parametric bounds and relaxations of the solutions of ordinary differential equations (ODEs). Such bounds and relaxations are used for global dynamic optimization and are computed using auxiliary ODE systems that use interval arithmetic and McCormick relaxations. Two ODE relaxation methods (Scott et al. in Optim Control Appl Methods 34(2):145–163, 2013; Scott and Barton in J Glob Optim 57:143–176, 2013) are shown to give second-order convergence, yet they can behave very differently from each other in practice. As time progresses, the prefactor in the convergence-order bound tends to grow much more slowly for one of these methods, and can even decrease over time, yielding global optimization procedures that require significantly less computation time.

Keywords

Deterministic global optimization Convergence-order analysis Nonconvex optimization Dynamic optimization 

Mathematics Subject Classification

34A40 49M20 49M37 65L05 65L20 90C26 

Notes

Acknowledgements

We gratefully acknowledge funding from Novartis Pharmaceuticals and helpful comments from the anonymous reviewers.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Spencer D. Schaber
    • 1
  • Joseph K. Scott
    • 2
  • Paul I. Barton
    • 3
  1. 1.Cargill, Inc.PlymouthUSA
  2. 2.Clemson UniversityClemsonUSA
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA

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