Abstract
The paper is concerned with computational research for large scale systems. The focus is on the hierarchical optimization methods that can be successfully applied to large scale optimization problems. A key issue is the possibility of solving several less dimension problems instead of one global high dimension task. Particular emphasis is laid on coarse granularity parallel implementation and its effectiveness. The paper discusses the usage of price coordination for real-life systems optimization. The results of numerical experiments performed for mean-variance portfolio selection using cluster of computers are presented and discussed.
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Niewiadomska-Szynkiewicz, E. (2006). Parallel Hierarchical Methods for Complex Systems Optimization. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751540_57
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DOI: https://doi.org/10.1007/11751540_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34070-6
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