標題: Free lunches on the discrete Lipschitz class
作者: Jiang, Pei
Chen, Ying-ping
資訊工程學系
Department of Computer Science
關鍵字: No-Free-Lunch theorem;Lipschitz continuity;Discrete Lipschitz class;Subthreshold-seeker;Sampling-test scheme
公開日期: 8-Apr-2011
摘要: The No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimizer because all algorithms have the identical performance on average over all functions. However, such a result does not imply that search heuristics or optimization algorithms are futile if we are more cautious with the applicability of these methods and the search space. In this paper, within the No-Free-Lunch framework, we firstly introduce the discrete Lipschitz class by transferring the Lipschitz functions, i.e., functions with bounded slope, as a measure to fulfill the notion of continuity in discrete functions. We then investigate the properties of the discrete Lipschitz class, generalize an algorithm called subthreshold-seeker for optimization, and show that the generalized subthreshold-seeker outperforms random search on this class. Finally, we propose a tractable sampling-test scheme to empirically demonstrate the superiority of the generalized subthreshold-seeker under practical configurations. This study concludes that there exist algorithms outperforming random search on the discrete Lipschitz class in both theoretical and practical aspects and indicates that the effectiveness of search heuristics may not be universal but still general in some broad sense. (C) 2010 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.tcs.2010.12.028
http://hdl.handle.net/11536/9008
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2010.12.028
期刊: THEORETICAL COMPUTER SCIENCE
Volume: 412
Issue: 17
起始頁: 1614
結束頁: 1628
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