End of Term Review 2.0

End of Term Review 2.0 - Merge Sort Merge two sorted lists...

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Unformatted text preview: Merge Sort Merge two sorted lists into one 25,31,52,88,98 14,23,25,30,31,52,62,79,88,98 14,23,30,62,79 CSE 2011 Prof. J. Elder - 46 - Last Updated: 4/1/10 2:37 PM Analysis of Merge-Sort The height h of the merge-sort tree is O(log n) at each recursive call we divide in half the sequence, The overall amount or work done at the nodes of depth i is O(n) we partition and merge 2i sequences of size n/2i we make 2i+1 recursive calls Thus, the total running time of merge-sort is O(n log n) depth #seqs size 0 1 n 1 2 n/2 i 2i n/2i … … T (n) = 2T (n / 2) + O(n) … CSE 2011 Prof. J. Elder - 47 - Last Updated: 4/1/10 2:37 PM Heap-Sort Algorithm Build an array-based (max) heap Iteratively call removeMax() to extract the keys in descending order Store the keys as they are extracted in the unused tail portion of the array CSE 2011 Prof. J. Elder - 48 - Last Updated: 4/1/10 2:37 PM Heap-Sort Running Time The heap can be built bottom-up in O(n) time Extraction of the ith element takes O(log(n - i+1)) time (for downheaping) Thus total run time is T (n) = O(n) + n log(n i + 1) i =1 = O(n) + n log i i =1 O(n) + n log n i =1 = O(n log n) CSE 2011 Prof. J. Elder - 49 - Last Updated: 4/1/10 2:37 PM Quick-Sort Quick-sort is a divide-andconquer algorithm: x Divide: pick a random element x (called a pivot) and partition S into L elements less than x x E elements equal to x G elements greater than x L E G Recur: Quick-sort L and G Conquer: join L, E and G x CSE 2011 Prof. J. Elder - 50 - Last Updated: 4/1/10 2:37 PM ...
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This note was uploaded on 02/14/2012 for the course CSE 2011Z taught by Professor Elder during the Fall '11 term at York University.

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End of Term Review 2.0 - Merge Sort Merge two sorted lists...

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