Abstract
This paper continues development of information-statistical approach to minimization of multiextremal functions in the case of non-convex constraints. Proposed approach is called index method. Solving multidimensional problem is reduced to solving equivalent single dimensional one. Dimension reduction is based on Peano curves that allow mapping multidimensional hyper cube onto the segment on real axis. We also use rotating Peano curves that allowed effectively parallelize algorithm to use hundreds of processors. Special attention was paid to mixed local-global strategy for algorithm convergence acceleration.
Supported by grants counsel of President of Russian Federation (grant № МК-1536.2009.9), Supported by federal Agency of Science and Innovations and state contract № 02.740.11.5018.
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Barkalov, K., Ryabov, V., Sidorov, S. (2010). Parallel Scalable Algorithms with Mixed Local-Global Strategy for Global Optimization Problems. In: Hsu, CH., Malyshkin, V. (eds) Methods and Tools of Parallel Programming Multicomputers. MTPP 2010. Lecture Notes in Computer Science, vol 6083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14822-4_26
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DOI: https://doi.org/10.1007/978-3-642-14822-4_26
Publisher Name: Springer, Berlin, Heidelberg
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