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Extension of Strongin’s Global Optimization Algorithm to a Function Continuous on a Compact Interval

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The Lipschitz continuous property has been used for a long time to solve the global optimization problem and continues to be used. Here we can mention the work of Piyavskii, Yevtushenko, Strongin, Shubert, Sergeyev, Kvasov and others. Most papers assume a priori knowledge of the Lipschitz constant, but the derivation of this constant is a separate problem. Further still, we must prove that an objective function is really Lipschitz, and it is a complicated problem too. In the case where the Lipschitz continuity is established, Strongin proposed an algorithm for global optimization of a satisfying Lipschitz condition on a compact interval function without any a priori knowledge of the Lipschitz estimate. The algorithm not only finds a global extremum, but it determines the Lipschitz estimate too. It is known that every function that satisfies the Lipchitz condition on a compact convex set is uniformly continuous, but the reverse is not always true. However, there exist models (Arutyunova, Dulliev, Zabotin) whose study requires a minimization of the continuous but definitely not Lipschitz function. One of the algorithms for solving such a problem was proposed by R. J. Vanderbei. In his work he introduced some generalization of the Lipchitz property named $\varepsilon$-Lipchitz and proved that a function defined on a compact convex set is uniformly continuous if and only if it satisfies the $\varepsilon$-Lipchitz condition. The above-mentioned property allowed him to extend Piyavskii’s method. However, Vanderbei assumed that for a given value of $\varepsilon$ it is possible to obtain an associate Lipschitz $\varepsilon$-constant, which is a very difficult problem. Thus, there is a need to construct, for a function continuous on a compact convex domain, a global optimization algorithm which works in some way like Strongin’s algorithm, i.e., without any a priori knowledge of the Lipschitz $\varepsilon$-constant. In this paper we propose an extension of Strongin’s global optimization algorithm to a function continuous on a compact interval using the $\varepsilon$-Lipchitz conception, prove its convergence and solve some numerical examples using the software that implements the developed method.

Keywords: $\varepsilon$-Lipschitz functions, function minimization, Stronginâ€™s algorithm, algorithm convergence
Citation in English: Zabotin, V.I., Chernyshevskij P.A. Extension of Strongin’s Global Optimization Algorithm to a Function Continuous on a Compact Interval // Computer Research and Modeling, 2019, vol. 11, no. 6, pp. 1111-1119
Citation in English: Zabotin, V.I., Chernyshevskij P.A. Extension of Strongin’s Global Optimization Algorithm to a Function Continuous on a Compact Interval // Computer Research and Modeling, 2019, vol. 11, no. 6, pp. 1111-1119
DOI: 10.20537/2076-7633-2019-11-6-1111-1119

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