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QuickHeapsort: Modifications and Improved Analysis

DOI:10.1007/s00224-015-9656-y
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Volker Diekert at Universität Stuttgart
Volker Diekert
Armin Weiß at Universität Stuttgart
Armin Weiß
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Abstract and Figures

QuickHeapsort is a combination of Quicksort and Heapsort. We show that the expected number of comparisons for QuickHeapsort is always better than for Quicksort if a usual median-of-constant strategy is used for choosing pivot elements. In order to obtain the result we present a new analysis for QuickHeapsort splitting it into the analysis of the partition-phases and the analysis of the heap-phases. This enables us to consider samples of non-constant size for the pivot selection and leads to better theoretical bounds for the algorithm. Furthermore, we introduce some modifications of QuickHeapsort. We show that for every input the expected number of comparisons is at most nlog2n−0.03n+o(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$n\log _{2}n - 0.03n + o(n)$\end{document} for the in-place variant. If we allow n extra bits, then we can lower the bound to nlog2n−0.997n+o(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$ n\log _{2} n -0.997 n+ o (n)$\end{document}. Thus, spending n extra bits we can save more that 0.96n comparisons if n is large enough. Both estimates improve the previously known results. Moreover, our non-in-place variant does essentially use the same number of comparisons as index based Heapsort variants and Relaxed-Weak-Heapsort which use nlog2n−0.9n+o(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$ n\log _{2}n -0.9 n+ o (n)$\end{document} comparisons in the worst case. However, index based Heapsort variants and Relaxed-Weak-Heapsort require Θ(nlogn)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}${\Theta }(n\log n)$\end{document} extra bits whereas we need n bits only. Our theoretical results are upper bounds and valid for every input. Our computer experiments show that the gap between our bounds and the actual values on random inputs is small. Moreover, the computer experiments establish QuickHeapsort as competitive with Quicksort in terms of running time.
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Theory Comput Syst (2016) 59:209–230
DOI 10.1007/s00224-015-9656-y
QuickHeapsort: Modifications and Improved Analysis
Volker Diekert1·Armin Weiß1
Published online: 15 September 2015
© Springer Science+Business Media New York 2015
Abstract QuickHeapsort is a combination of Quicksort and Heapsort. We show that
the expected number of comparisons for QuickHeapsort is always better than for
Quicksort if a usual median-of-constant strategy is used for choosing pivot elements.
In order to obtain the result we present a new analysis for QuickHeapsort splitting
it into the analysis of the partition-phases and the analysis of the heap-phases. This
enables us to consider samples of non-constant size for the pivot selection and leads
to better theoretical bounds for the algorithm. Furthermore, we introduce some mod-
ifications of QuickHeapsort. We show that for every input the expected number of
comparisons is at most nlog2n−0.03n+o(n) for the in-place variant. If we allow
nextra bits, then we can lower the bound to nlog2n−0.997n+o(n). Thus, spend-
ing nextra bits we can save more that 0.96ncomparisons if nis large enough. Both
estimates improve the previously known results. Moreover, our non-in-place variant
does essentially use the same number of comparisons as index based Heapsort vari-
ants and Relaxed-Weak-Heapsort which use nlog2n−0.9n+o(n) comparisons in
the worst case. However, index based Heapsort variants and Relaxed-Weak-Heapsort
require (n log n) extra bits whereas we need nbits only. Our theoretical results are
upper bounds and valid for every input. Our computer experiments show that the
gap between our bounds and the actual values on random inputs is small. Moreover,
the computer experiments establish QuickHeapsort as competitive with Quicksort in
terms of running time.
Keywords In-place sorting ·Heapsort ·Quicksort ·Analysis of algorithms
Armin Weiß
armin.weiss@fmi.uni-stuttgart.de
Volker Diekert
diekert@fmi.uni-stuttgart.de
1FMI, Universit¨
at Stuttgart, Universit¨
atsstr. 38, D-70569 Stuttgart, Germany
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... UltimateHeapsort is inferior to QuickHeapsort in terms of the average case number of comparisons, although, unlike QuickHeapsort, it allows an n lg n + O(n) bound for the worst case number of comparisons. Diekert and Weiß [4] analyzed QuickHeapsort more thoroughly and described some improvements requiring less than n lg n − 0.99n + o(n) comparisons on average (choosing the pivot as median of √ n elements). However, both the original analysis of Cantone and Cincotti and the improved analysis could not give tight bounds for the average case of median-of-k QuickHeapsort. ...
... 6 without proofs). In [52], the third author analyzed QuickMergesort with constant-size pivot sampling (see Sect. 4 ...
... 2. Under reasonable assumptions, sample sizes of Θ( √ n) are optimal among all polynomial size sample sizes. 3. The probability that median-of-√ n QuickXsort needs more than x wc (n) + 6n comparisons decreases exponentially in 4 √ n (Proposition 4.5). Here, x wc (n) is the worst-case cost of X. 4. We introduce median-of-medians fallback pivot selection (a trick similar to Introsort [39]) which guarantees n lg n + O(n) comparisons in the worst case while altering the average case only by o(n)-terms (Theorem 4.7). 5. Let k be a fixed constant and let X be a sorting method that needs a buffer of αn elements for some constant α ∈ [0, 1] to sort n elements and requires on average x(n) = n lg n + bn ± o(n) comparisons to do so. ...
QuickXsort: A Fast Sorting Scheme in Theory and Practice
Article
Full-text available
QuickXsort is a highly efficient in-place sequential sorting scheme that mixes Hoare’s Quicksort algorithm with X, where X can be chosen from a wider range of other known sorting algorithms, like Heapsort, Insertionsort and Mergesort. Its major advantage is that QuickXsort can be in-place even if X is not. In this work we provide general transfer theorems expressing the number of comparisons of QuickXsort in terms of the number of comparisons of X. More specifically, if pivots are chosen as medians of (not too fast) growing size samples, the average number of comparisons of QuickXsort and X differ only by o(n)-terms. For median-of-k pivot selection for some constant k, the difference is a linear term whose coefficient we compute precisely. For instance, median-of-three QuickMergesort uses at most nlgn-0.8358n+O(logn) comparisons. Furthermore, we examine the possibility of sorting base cases with some other algorithm using even less comparisons. By doing so the average-case number of comparisons can be reduced down to nlgn-1.4112n+o(n) for a remaining gap of only 0.0315n comparisons to the known lower bound (while using only O(logn) additional space and O(nlogn) time overall). Implementations of these sorting strategies show that the algorithms challenge well-established library implementations like Musser’s Introsort.
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... UltimateHeapsort is inferior to QuickHeapsort in terms of the average case number of comparisons, although, unlike QuickHeapsort, it allows an n lg n + O(n) bound for the worst case number of comparisons. Diekert and Weiß [4] analyzed QuickHeapsort more thoroughly and described some improvements requiring less than n lg n − 0.99n + o(n) comparisons on average (choosing the pivot as median of √ n elements). However, both the original analysis of Cantone and Cincotti and the improved analysis could not give tight bounds for the average case of median-of-k QuickMergesort. ...
... We consider both the case where k is a fixed constant and where k = k(n) is an increasing function of the (sub)problem size. Previous results in [4,35] for Quicksort suggest that sample sizes k(n) = Θ( √ n) are likely to be optimal asymptotically, but most of the relative savings for the expected case are already realized for k ≤ 10. It is quite natural to expect similar behavior in QuickXsort, and it will be one goal of this article to precisely quantify these statements. ...
... We use a symmetric variant (with a min-oriented heap) if the left segment shall be sorted by X. For detailed code for the above procedure, we refer to [3] or [4]. ...
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QuickXsort is a highly efficient in-place sequential sorting scheme that mixes Hoare's Quicksort algorithm with X, where X can be chosen from a wider range of other known sorting algorithms, like Heapsort, Insertionsort and Mergesort. Its major advantage is that QuickXsort can be in-place even if X is not. In this work we provide general transfer theorems expressing the number of comparisons of QuickXsort in terms of the number of comparisons of X. More specifically, if pivots are chosen as medians of (not too fast) growing size samples, the average number of comparisons of QuickXsort and X differ only by $o(n)$-terms. For median-of-$k$ pivot selection for some constant $k$, the difference is a linear term whose coefficient we compute precisely. For instance, median-of-three QuickMergesort uses at most $n \lg n - 0.8358n + O(\log n)$ comparisons. Furthermore, we examine the possibility of sorting base cases with some other algorithm using even less comparisons. By doing so the average-case number of comparisons can be reduced down to $n \lg n- 1.4106n + o(n)$ for a remaining gap of only $0.0321n$ comparisons to the known lower bound (while using only $O(\log n)$ additional space and $O(n \log n)$ time overall). Implementations of these sorting strategies show that the algorithms challenge well-established library implementations like Musser's Introsort.
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