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THE LIMITING DISTRIBUTION FOR THE NUMBER OF SYMBOL COMPARISONS USED BY QUICKSORT IS NONDEGENERATE (EXTENDED ABSTRACT)
"... In a continuoustime setting, Fill [2] proved, for a large class of probabilistic sources, that the number of symbol comparisons used by QuickSort, when centered by subtracting the mean and scaled by dividing by time, has a limiting distribution, but proved little about that limiting random variable ..."
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In a continuoustime setting, Fill [2] proved, for a large class of probabilistic sources, that the number of symbol comparisons used by QuickSort, when centered by subtracting the mean and scaled by dividing by time, has a limiting distribution, but proved little about that limiting random variable Y —not even that it is nondegenerate. We establish the nondegeneracy of Y. The proof is perhaps surprisingly difficult. 1. The number of symbol comparisons used by QuickSort: Brief review of a limitingdistribution result In this section we briefly review the main theorem of [2]. An infinite sequence of independent and identically distributed keys is generated; each key is a random word (w1, w2,...) = w1w2 · · · , that is, an infinite sequence, or “string”, of symbols wi drawn from a totally ordered finite alphabet Σ. The common distribution µ of the keys (called a probabilistic source) is allowed to be any distribution over words, i.e., the distribution of any stochastic process with time parameter set {1, 2,...} and state space Σ. We know thanks to Kolmogorov’s consistency criterion (e.g.,
The Limiting Distribution for the Number of Symbol Comparisons Used by QuickSort is
, 2012
"... In a continuoustime setting, Fill (2012) proved, for a large class of probabilistic sources, that the number of symbol comparisons used by QuickSort, when centered by subtracting the mean and scaled by dividing by time, has a limiting distribution, but proved little about that limiting random varia ..."
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In a continuoustime setting, Fill (2012) proved, for a large class of probabilistic sources, that the number of symbol comparisons used by QuickSort, when centered by subtracting the mean and scaled by dividing by time, has a limiting distribution, but proved little about that limiting random variable Y —not even that it is nondegenerate. We establish the nondegeneracy of Y. The proof is perhaps surprisingly difficult.
DISTRIBUTIONAL CONVERGENCE FOR THE NUMBER OF SYMBOL COMPARISONS USED BY QUICKSELECT
"... When the search algorithm QuickSelect compares keys during its execution in order to find a key of target rank, it must operate on the keys ’ representations or internal structures, which were ignored by the previous studies that quantified the execution cost for the algorithm in terms of the number ..."
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When the search algorithm QuickSelect compares keys during its execution in order to find a key of target rank, it must operate on the keys ’ representations or internal structures, which were ignored by the previous studies that quantified the execution cost for the algorithm in terms of the number of required key comparisons. In this paper, we analyze running costs for the algorithm that take into account not only the number of key comparisons but also the cost of each key comparison. We suppose that keys are represented as sequences of symbols generated by various probabilistic sources and that QuickSelect operates on individual symbols in order to find the target key. We identify limiting distributions for the costs and derive integral and series expressions for the expectations of the limiting distributions. These expressions are used to recapture previously obtained results on the number of key comparisons required by the algorithm.
Symbol Comparisons Used by QuickSort
"... Most previous studies of the sorting algorithm QuickSort have used the number of key comparisons as a measure of the cost of executing the algorithm. Here we suppose that the n independent and identically distributed (iid) keys are each represented as a sequence of symbols from a probabilistic sourc ..."
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Most previous studies of the sorting algorithm QuickSort have used the number of key comparisons as a measure of the cost of executing the algorithm. Here we suppose that the n independent and identically distributed (iid) keys are each represented as a sequence of symbols from a probabilistic source and that QuickSort operates on individual symbols, and we measure the execution cost as the number of symbol comparisons. Assuming only a mild “tameness ” condition on the source, we show that there is a limiting distribution for the number of symbol comparisons after normalization: first centering by the mean and then dividing by n. Additionally, under a condition that grows more restrictive as p increases, we have convergence of moments of orders p and smaller. In particular, we have convergence in distribution and convergence of moments of every order whenever the source is memoryless, i.e., whenever each key is generated as an infinite string of iid symbols. This is somewhat surprising: Even for the classical model that each key is an iid string of unbiased (“fair”) bits, the mean exhibits periodic fluctuations of order n.
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"... A general framework for the realistic analysis of sorting and searching algorithms. Application to some popular algorithms ∗ ..."
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A general framework for the realistic analysis of sorting and searching algorithms. Application to some popular algorithms ∗
A Gaussian limit process for optimal FIND algorithms
, 2013
"... We consider versions of the FIND algorithm where the pivot element used is the median of a subset chosen uniformly at random from the data. For the median selection we assume that subsamples of size asymptotic to c · nα are chosen, where 0 < α ≤ 1 2, c> 0 and n is the size of the data set to be spli ..."
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We consider versions of the FIND algorithm where the pivot element used is the median of a subset chosen uniformly at random from the data. For the median selection we assume that subsamples of size asymptotic to c · nα are chosen, where 0 < α ≤ 1 2, c> 0 and n is the size of the data set to be split. We consider the complexity of FIND as a process in the rank to be selected and measured by the number of key comparisons required. After normalization we show weak convergence of the complexity to a centered Gaussian process as n → ∞, which depends on α. The proof relies on a contraction argument for probability distributions on càdlàg functions. We also identify the covariance function of the Gaussian limit process and discuss path and tail properties. AMS 2010 subject classifications. Primary 60F17, 68P10; secondary 60G15, 60C05, 68Q25. Key words. FIND algorithm, Quickselect, complexity, key comparisons, functional limit theorem,