Interval Sorting

  • Rosa M. Jiménez
  • Conrado Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6198)


In the interval sort problem, we are given an array A of n items and p ranges of ranks I 1 = [ℓ1,u 1], ..., I p  = [ℓ p ,u p ]. The goal is to rearrange the array so that A[ℓ t ..u t ] contains the ℓ t -th, ..., u t -th smallest elements of A in nondecreasing order, for all t, 1 ≤ t ≤ p, and A[u t  + 1..ℓ t + 1− 1] contains the (u t  + 1), ..., (ℓ t + 1− 1) smallest elements of A, for all t, 0 ≤ t ≤ p. That is, the array is sorted by blocks, with sorted and unsorted blocks alternating. One of the most interesting aspects of this research is the unification of several important and related problems (sorting, selection, multiple selection, partial sorting) under a single framework. Results on interval sorting generalize the results for any of these particular—and fundamental—problems.

We propose a divide-and-conquer algorithm, owing to quicksort and quickselect, named chunksort, to solve the problem. We give an exact expression for the average number of comparisons made by the basic variant of chunksort. Then we consider what is the expected optimal number of comparisons needed to solve an interval sort instance and we design a variant of chunksort that achieves near optimal expected performance, up to n + o(n) comparisons. In fact, we conjecture that the algorithm that we propose has actually optimal expected performance up to o(n) terms and provide some evidence for this conjecture.


Internal Node Small Element Lower Order Term Recursive Call Binary Search Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rosa M. Jiménez
    • 1
  • Conrado Martínez
    • 1
  1. 1.Departament de Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

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