lecture_06

# lecture_06 - Introduction to Algorithms 6.046J/18.401J...

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Introduction to Algorithms 6.046J/18.401J Lecture 6 Prof. Piotr Indyk

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Introduction to Algorithms September 27, 2004 L6.2 © Charles E. Leiserson and Piotr Indyk Today: sorting Show that Θ ( n lg n ) is the best possible running time for a sorting algorithm. Design an algorithm that sorts in O(n) time. Hint: different models ?
Introduction to Algorithms September 27, 2004 L6.3 © Charles E. Leiserson and Piotr Indyk Comparison sort All the sorting algorithms we have seen so far are comparison sorts : only use comparisons to determine the relative order of elements. E.g ., insertion sort, merge sort, quicksort, heapsort.

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Introduction to Algorithms September 27, 2004 L6.4 © Charles E. Leiserson and Piotr Indyk x Partitioning subroutine P ARTITION ( A , p , r ) A [ p . . r ] x A [ p ] pivot = A [ p ] i p for j p + 1 to r do if A[ j] x then i i + 1 exchange A [ i ] A [ j ] exchange A [ p ] A [ i ] return i x x ? p i r j Invariant:
Introduction to Algorithms September 27, 2004 L6.5 © Charles E. Leiserson and Piotr Indyk Comparison sort All of our algorithms used comparisons All of our algorithms have running time (n lg n) Is it the best that we can do using just comparisons ? Answer: YES, via decision trees

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Introduction to Algorithms September 27, 2004 L6.6 © Charles E. Leiserson and Piotr Indyk Decision-tree example 1:2 1:2 2:3 2:3 123 1:3 1:3 132 312 1:3 1:3 213 2:3 2:3 231 321 Each internal node is labeled i : j for i , j {1, 2,…, n } . The left subtree shows subsequent comparisons if a i a j . The right subtree shows subsequent comparisons if a i a j . Sort a 1 , a 2 , …, a n (n=3)
Introduction to Algorithms September 27, 2004 L6.7 © Charles E. Leiserson and Piotr Indyk Decision-tree example 1:2 1:2 2:3 2:3 123 1:3 1:3 132 312 1:3 1:3 213 2:3 2:3 231 321 Each internal node is labeled i : j for i , j {1, 2,…, n } . The left subtree shows subsequent comparisons if a i a j . The right subtree shows subsequent comparisons if a i a j . Sort a 1 , a 2 , a 3 = ⟨ 9, 4, 6 :

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Introduction to Algorithms September 27, 2004 L6.8 © Charles E. Leiserson and Piotr Indyk Decision-tree example 1:2 1:2 2:3 2:3 123 1:3 1:3 132 312 1:3 1:3 213 2:3 2:3 231 321 Each internal node is labeled i : j for i , j {1, 2,…, n } . The left subtree shows subsequent comparisons if a i a j . The right subtree shows subsequent comparisons if a i a j . 9 4 Sort a 1 , a 2 , a 3 = ⟨ 9, 4, 6 :
Introduction to Algorithms September 27, 2004 L6.9 © Charles E. Leiserson and Piotr Indyk Decision-tree example 1:2 1:2 2:3 2:3 123 1:3 1:3 132 312 1:3 1:3 213 2:3 2:3 231 321 Each internal node is labeled i : j for i , j {1, 2,…, n } . The left subtree shows subsequent comparisons if a i a j . The right subtree shows subsequent comparisons if a i a j . 9 6 Sort a 1 , a 2 , a 3 = ⟨ 9, 4, 6 :

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Introduction to Algorithms September 27, 2004 L6.10 © Charles E. Leiserson and Piotr Indyk Decision-tree example 1:2 1:2 2:3 2:3 123 1:3 1:3 132 312 1:3 1:3 213 2:3 2:3 231 321 Each internal node is labeled i : j for i , j {1, 2,…, n } .
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