Is on log n the best we can do yes if we use binary

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Unformatted text preview: tion ! Is O(n log n) the best we can do? – Yes, if we use binary comparison on the key values 19 Theoretic Theoretic Lower Bound n elements can have n! permutations ! A sort decision tree is a binary tree that represents a sorting method based on comparisons ! Each internal node compares two elements ! – Left branch implies first element smaller than second – Right branch otherwise ! Each leaf nodes corresponds to one of the n! permutations 20 Sort Sort Decision Tree: An example Sort 3 items: Never Never makes a redundant comparison comparison x1,x2 x2,x3 x1<x2<x3 x1,x3 x1,x3 x2<x1<x3 x1<x3<x2 x3<x1<x2 x2,x3 x2<x3<x1 x3<x2<x1 # leaf nodes = n! leaf 21 Theoretic Theoretic Lower Bound A sort decision tree has (2n! – 1) nodes ! Height of tree is at least log n! ! n! ≥ (n/2)(n/2) ⇒ log n! is Ω(n log n) ! Sorting algorithms that use comparisons must make Ω(n log n) comparisons ! 22 Quick, Quick, Merge, or Heap? ! Quicksort is the fastest sorting algorithm in practice, but – Can perform as badly as O(n2) – Requires O(log n) stack space ! Mergesort is guaranteed to run in O(n log n), but – Slower than quicksort on average – Requires O(n) additional temporary storage ! Heapsort is guaranteed to run in O(n log n), in-place, and requires no recursions, but – Many real world tests show that heapsort is slower than quicksort (and mergesort) on average – Useful for really large problems 23...
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This document was uploaded on 03/06/2014 for the course ECE 368 at Purdue.

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