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Fragile Complexity of Adaptive Algorithms

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Algorithms and Complexity (CIAC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12701))

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Abstract

The fragile complexity of a comparison-based algorithm is f(n) if each input element participates in O(f(n)) comparisons. In this paper, we explore the fragile complexity of algorithms adaptive to various restrictions on the input, i.e., algorithms with a fragile complexity parameterized by a quantity other than the input size n. We show that searching for the predecessor in a sorted array has fragile complexity \(\varTheta (\log k)\), where k is the rank of the query element, both in a randomized and a deterministic setting. For predecessor searches, we also show how to optimally reduce the amortized fragile complexity of the elements in the array. We also prove the following results: Selecting the kth smallest element has expected fragile complexity \(O(\log \log k)\) for the element selected. Deterministically finding the minimum element has fragile complexity \(\varTheta (\log (\mathrm {Inv}))\) and \(\varTheta (\log (\mathrm {Runs}))\), where \(\mathrm {Inv}\) is the number of inversions in a sequence and \(\mathrm {Runs}\) is the number of increasing runs in a sequence. Deterministically finding the median has fragile complexity \(O(\log (\mathrm {Runs}) + \log \log n)\) and \(\varTheta (\log (\mathrm {Inv}))\). Deterministic sorting has fragile complexity \(\varTheta (\log (\mathrm {Inv}))\) but it has fragile complexity \(\varTheta (\log n)\) regardless of the number of runs.

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Notes

  1. 1.

    For simplicity of exposition, we assume the rank is close to one, but the result clearly holds for rank distance to other positions in A.

  2. 2.

    The measure \(\mathrm {Inv}\) is defined as the total number of inversions in the input, where each of the \({n\atopwithdelims ()2}\) pairs of elements constitute an inversion if the elements of the pair appear in the wrong order. The measure \(\mathrm {Runs}\) is defined as the number of runs in the input, where a run is a maximal consecutive ascending subsequence.

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Acknowledgements

This material is based upon work performed while attending AlgoPARC Workshop on Parallel Algorithms and Data Structures at the University of Hawaii at Manoa, in part supported by the National Science Foundation under Grant No. CCF-1930579. We thank Timothy Chan and Qizheng He for their ideas improving the randomized selection algorithm.

P.B was partially supported by NSERC. P.C and J.I. were supported by F.R.S.-FNRS under Grant no MISU F 6001 1. R.F. was partially supported by the Independent Research Fund Denmark, Natural Sciences, grant DFF-7014-00041. J.I. was supported by NSF grant CCF-1533564. S.L. is Directeur de Recherches du F.R.S.-FNRS.

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Authors and Affiliations

  1. School of Computer Science, Carleton University, Ottawa, Canada

    Prosenjit Bose

  2. Université libre de Bruxelles, Brussels, Belgium

    Pilar Cano, John Iacono & Stefan Langerman

  3. University of Southern Denmark, Odense, Denmark

    Rolf Fagerberg

  4. New York University, New York, USA

    John Iacono

  5. IT University of Copenhagen, Copenhagen, Denmark

    Riko Jacob

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  1. Prosenjit Bose
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Correspondence to Pilar Cano .

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  1. Sapienza University of Rome, Rome, Italy

    Tiziana Calamoneri

  2. Sapienza University of Rome, Rome, Italy

    Federico Corò

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Bose, P., Cano, P., Fagerberg, R., Iacono, J., Jacob, R., Langerman, S. (2021). Fragile Complexity of Adaptive Algorithms. In: Calamoneri, T., Corò, F. (eds) Algorithms and Complexity. CIAC 2021. Lecture Notes in Computer Science(), vol 12701. Springer, Cham. https://doi.org/10.1007/978-3-030-75242-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-75242-2_10

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