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Randomized algorithms in interval global optimization

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Abstract

This paper is a critical survey of the interval optimization methods aimed at computing global optima for multivariable functions. To overcome some drawbacks of traditional deterministic interval techniques, we outline some ways of constructing stochastic (randomized) algorithms in interval global optimization, in particular, those based on the ideas of random search and simulated annealing.

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Authors and Affiliations

  1. Institute of Computational Technologies, Siberian Branch, Russian Academy of Sciences, pr. Akad. Lavrent’eva 6, Novosibirsk, 630090, Russia

    S. P. Shary

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  1. S. P. Shary
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Correspondence to S. P. Shary.

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Original Russian Text © S.P. Shary, 2008, published in Sibirskii Zhurnal Vychislitel’noi Matematiki, 2008, Vol. 11, No. 4, pp. 457–474.

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Shary, S.P. Randomized algorithms in interval global optimization. Numer. Analys. Appl. 1, 376–389 (2008). https://doi.org/10.1134/S1995423908040083

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