Quicksort: Difference between revisions

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When the input is a random permutation, the pivot has a random rank, and so it is not guaranteed to be in the middle 50 percent. However, when starting from a random permutation, each recursive call's pivot has a random rank in its list, and therefore is in the middle 50 percent approximately half the time. Imagine that a coin is flipped: heads means that the rank of the pivot is in the middle 50 percent, tail means that it isn't. Now imagine that the coin is flipped over and over until it gets {{mvar|k}} heads. Although this could take a long time, on average only {{math|2''k''}} flips are required, and the chance that the coin won't get {{mvar|k}} heads after {{math|100''k''}} flips is highly improbable (this can be made rigorous using [[Chernoff bound]]s). By the same argument, Quicksort's recursion will terminate on average at a call depth of only <math>2 \log_{4/3} n</math>. But if its average call depth is {{math|''O''(log ''n'')}}, and each level of the call tree processes at most {{mvar|n}} elements, the total amount of work done on average is the product, {{math|''O''(''n'' log ''n'')}}. The algorithm does not have to verify that the pivot is in the middle half as long as it is a consistent amount of times.

Using more careful arguments, it is possible to extend this proof, for the version of Quicksort where the pivot is randomnly chosen,

Using more sophisticated probabilistic tools, it is possible to proveshow a strongertime propertybound forthat theholds version''with ofhigh Quicksortprobability'': where the pivot is randomnly chosen:specifically, for any give <math>a\ge 4</math>, let <math>c=(a-4)/2</math>, then with probability at least <math>1-\frac{1}{n^c}</math>, the number of comparisons will not exceed <math>2an\log_{4/3}n</math>.<ref>{{cite book |last1=Motwani |first1= Rajeev |last2= Raghavan|first2= Prabhakar |date= |title= Randomized Algorithms|url= |___location= |publisher= Cambridge University Press|page= |isbn=9780521474658 |access-date=}}</ref>

==== Using recurrences ====

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: <math>\operatorname{E}[C] = \sum_i \sum_{j<i} \frac{2}{j+1} = O\left(\sum_i \log i\right)=O(n \log n).</math>

===Time bound with high probability===

Using more sophisticated probabilistic tools, it is possible to prove a stronger property for the version of Quicksort where the pivot is randomnly chosen: for any give <math>a\ge 4</math>, let <math>c=(a-4)/2</math>, then with probability at least <math>1-\frac{1}{n^c}</math>, the number of comparisons will not exceed <math>2an\log_{4/3}n</math>.<ref>{{cite book |last1=Motwani |first1= Rajeev |last2= Raghavan|first2= Prabhakar |date= |title= Randomized Algorithms|url= |___location= |publisher= Cambridge University Press|page= |isbn=9780521474658 |access-date=}}</ref>

=== Space complexity ===

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of the sorted records, in order to exchange records, and a constant number of integer variables used as indices).<ref>{{cite conference |last= Ďurian|first= Branislav|date= |title=Quicksort without a stack |url= |work= |book-title= Mathematical Foundations of Computer Science 1986: Proceedings of the 12th Symposium |conference=MFCS 1986 |___location= Bratislava, Czechoslovakia|publisher= Springer Berlin Heidelberg|access-date=}}</ref>

Another, less common, not-in-place, version of quicksort{{citation needed|date=July 2025}} uses {{math|''O''(''n'')}} space for working storage and can implement a stable sort. The working storage allows the input array to be easily partitioned in a stable manner and then copied back to the input array for successive recursive calls. Sedgewick's optimization is still appropriate.

== Relation to other algorithms ==