The problem of sorting can be viewed as following.
Input: A sequence of n numbers <a1, a2, . . . , an>.
Output: A permutation (reordering) <a‘1, a‘2, . . . , a‘n> of the input sequence such that a‘1 <= a‘2 ….. <= a‘n.
A sorting algorithm is comparison based if it uses comparison operators to find the order between two numbers. Comparison sorts can be viewed abstractly in terms of decision trees. A decision tree is a full binary tree that represents the comparisons between elements that are performed by a particular sorting algorithm operating on an input of a given size. The execution of the sorting algorithm corresponds to tracing a path from the root of the decision tree to a leaf. At each internal node, a comparison ai <= aj is made. The left subtree then dictates subsequent comparisons for ai <= aj, and the right subtree dictates subsequent comparisons for ai > aj. When we come to a leaf, the sorting algorithm has established the ordering. So we can say following about the decison tree.
1) Each of the n! permutations on n elements must appear as one of the leaves of the decision tree for the sorting algorithm to sort properly.
2) Let x be the maximum number of comparisons in a sorting algorithm. The maximum height of the decison tree would be x. A tree with maximum height x has at most 2^x leaves.
[ad type=”banner”]After combining the above two facts, we get following relation.
n! <= 2^x
Taking Log on both sides.
log2(n!) <= x
Since log2(n!) = Θ(nLogn), we can say
x = Ω(nLog2n)
Therefore, any comparison based sorting algorithm must make at least nLog2n comparisons to sort the input array, and Heapsort and merge sort are asymptotically optimal comparison sorts.
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