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# lec7 - EE 608 Computational Models and Methods Lecture 7...

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EE 608: Computational Models and Methods Lecture 7: Linear Sorts Read Chapter 8 of Introduction to Algorithms

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Comparison Sort Algorithms How fast can we sort? Much depends on the assumptions one makes about how the sorting is accomplished. So far we have considered only algorithms that sort by comparing pairs of elements (i.e., Insertion-Sort , Merge-Sort , HeapSort , and Quick- Sort ). They are called comparison sorts . Any sort requires at least n - 1 comparisons, or it won’t have examined the input. However, all comparison sorts require Ω( n lg n ) comparisons to sort in the worst case. Can we achieve a sort that is faster than Ω( n lg n )? We assume that all elements are distinct while we are discussing the lower bound on comparison sorts. In this case, a i < a j , a i > a j , a i a j , a i a j provide equivalent information relative to ordering; hence, we use a i a j .
Decision Trees A comparison sort can be viewed in terms of a decision tree , which represents the comparisons performed by the algorithm operating on an input of a certain size. a1:a2 > <= <= a1:a3 a2:a3 > <3,2,1> <2,1,3> <= a2:a3 a1:a3 <1,2,3> <1,3,2> <3,1,2> <2,3,1> > > > <= <=

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Decision Trees continued There is a comparison tree for each input of size n . The leaves represent all possible permutations of the input array; hence, there are n ! leaves in the tree. Internal nodes represent comparisons between pairs of elements. The path from the root to a leaf shows the comparisons for arriving at the leaf’s permutation (an execution trace). The length of the longest path in the tree indicates its worst-case running time. Theorem 9.1: Any decision tree that sorts n elements has height Ω( n lg n ), (i.e., at least one path is that length).
Complexity of Comparison Sorts Proof: (We ignore data movement, bookkeeping operations, etc.) The number of leaves in the decision tree is at least n !, or two permutations go to the same leaf. The number of leaves in a binary tree is 2 h , and: 2 h n ! h lg( n !) Recall Stirling’s approximation: n ! = 2 πn ( n e ) n (1 + Θ(1 /n )) > ( n e ) n , hence: h lg( n e ) n = n lg n - n lg e h = Ω( n lg n )

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Decision Tree Questions 1. In a comparison sort decision tree, what do the leaves of the tree represent? How many leaves must there be in a decision tree sorting n elements? 2. In a comparison sort decision tree, what do the non-leaf nodes of the tree represent? 3. In a comparison sort decision tree, what do the edges of the tree represent?
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