Abstract
In the theory and practice of optimization it often happens that the objective function has a very low degree of regularity or that it is defined only empirically. Another critical point in optimization is that many algorithms deliver only local convergence. So for these two reasons it is advise-able to analyze methods of direct search like random and quasi-random search techniques. In this paper we consider error estimates for deterministic analogues of random search.
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© 1985 Springer-Verlag Berlin Heidelberg
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Bayrhamer, W. (1985). Some Remarks on Quasi-Random Optimization. In: Demyanov, V.F., Pallaschke, D. (eds) Nondifferentiable Optimization: Motivations and Applications. Lecture Notes in Economics and Mathematical Systems, vol 255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-12603-5_28
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DOI: https://doi.org/10.1007/978-3-662-12603-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-15979-7
Online ISBN: 978-3-662-12603-5
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