On the Asymptotic Tractability of Global Optimization

James M. Calvin ()
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James M. Calvin: New Jersey Institute of Technology

A chapter in Advances in Stochastic and Deterministic Global Optimization, 2016, pp 3-12 from Springer

Abstract: Abstract We consider the intrinsic difficulty of global optimization in high dimensional Euclidean space. We adopt an asymptotic analysis, and give a lower bound on the number of function evaluations required to obtain a given error tolerance. This lower bound complements upper bounds provided by recently proposed algorithms.

Keywords: Lower complexity bounds; Tractability; Adaptive algorithms (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-29975-4_1

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DOI: 10.1007/978-3-319-29975-4_1

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