Approximation algorithm for a class of global optimization problems

Marco Locatelli ()

Journal of Global Optimization, 2013, vol. 55, issue 1, 13-25

Abstract: In this paper we develop and derive the computational cost of an $${\varepsilon}$$ -approximation algorithm for a class of global optimization problems, where a suitably defined composition of some ratio functions is minimized over a convex set. The result extends a previous one about a class of Linear Fractional/Multiplicative problems. Copyright Springer Science+Business Media, LLC. 2013

Keywords: Approximation algorithms; Nondecreasing functions; Superhomogeneous functions; Ratio functions (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10898-011-9813-z

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