Abstract
This paper presents a new scheme for parallel computations on cluster systems for time consuming problems of globally optimal decision making. This uniform scheme (without any centralized control processor) is based on the idea of multidimensional problem reduction. Using same new multiple mappings (of the Peano curve type), a multidimensional problem is reduced to a family of univariate problems which can be solved in parallel in such a way that each of these processors shares the information obtained by the other processors.
Supported in part by the Intel Research Grant “Parallel Computing on Multiprocessor and Multi-computer Systems for Globally Optimal Decision Making"
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References
Kushner, H.J.: A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise. Transactions of ASME, Ser. D. Journal of Basic Engineering 86(1), 97–106 (1964)
Piyavskij, S.A.: An Algorithm for Finding the Absolute Minimum of a Function. Optimum Decision Theory 2, Inst. of Cybern. of the Acad. of Sci. of the Ukr. SSR, Kiev, 13–24 (1967) (in Russian)
Pfister, G.P.: In Search of Clusters. Prentice Hall PTR, Upper Saddle River (1998)
Strongin, R.G.: Numerical Methods in Multi-Extremal Problems (Information-Statistical Algorithms). Nauka, Moscow (1978) (in Russian)
Strongin, R.G.: Algorithms for Multi-extremal Mathematical Programming Problems Employing the Set of Joint Space-Filling Curves. Journal of Global Optimization 2, 357–378 (1992)
Strongin, R.G., Markin, D.L.: Minimization of Multiextremal Functions with Nonconvex Constraints. Cybernetics 16(4), 64–69 (1986) (in Russian)
Strongin, R.G., Gergel, V.P.: On Realization of the Generalized Multidimensional Global Search Algorithm on a Computer. In: Problems of Cybernetics. Stochastic Search in Optimization Problems. Scientific Council of Academy of Sciences of USSR for Cybernetics, Moscow, pp. 59–66 (1978) (in Russian)
Strongin, R.G., Sergeyev, Y.D.: Global Multidimensional Optimization on Parallel Computer. Parallel Computing 13, 1259–1273 (1992)
Strongin, R.G., Sergeyev, Y.D.: Global Optimization with Non-convex Constraints: Sequential and Parallel Algorithms. Kluwer Academic Publishers, Dordrecht (2000)
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Gergel, V.P., Strongin, R.G. (2003). Parallel Computing for Globally Optimal Decision Making. In: Malyshkin, V.E. (eds) Parallel Computing Technologies. PaCT 2003. Lecture Notes in Computer Science, vol 2763. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45145-7_7
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DOI: https://doi.org/10.1007/978-3-540-45145-7_7
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