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14.1 14.2 14.3 14.4 Why is the bubble sort so slow The bubble sort makes n(n 1)/2 comparisons to sort n elements. How does it follow that its complexity function is O(n2) Why are the O(n) sorting algorithms (radix sort and bucket sort) slower than the O(n lg n) sorting algorithms (merge sort, quick sort, and heap sort) The merge sort applies the general method, known as divide and conquer, to sort an array. It divides the array into pieces and applies itself recursively to each piece. What other sorting algorithm(s) use this method Which sorting algorithms work as well on linked lists as on arrays Which sorting algorithms have a different worst case complexity than their average case Which sorting algorithms have a different best case complexity than their average case Why is the nonrecursive version of a recursive sorting algorithm usually more efficient How is the quick sort like the merge sort

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Algorithm 14.11 The Bucket Sort (Precondition: s = {s0 . . . sn 1} is a sequence of n ordinal values with known minimum value min and maximum value max.) (Postcondition: the sequence s is sorted.) 1. Initialize an array of n buckets (collections). 2. Repeat step 3 for each si in the sequence. 3. Insert si into bucket j, where j = rn , r = (si min)/(max + 1 min). 4. Sort each bucket. 5. Repeat step 6 for j from 0 to n 1. 6. Add the elements of bucket j sequentially back into s. EXAMPLE 14.14 Sorting U.S. Social Security Numbers with the Bucket Sort.

Therefore, Using Parseval's theorem,

14.5 14.6 14.7 14.8 14.9

= 720,'

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Suppose you have 1000 nine-digit identification numbers. Set up 1000 arrays of type int and then distribute the numbers using the formula j = rn , r = (si min)/(max + 1 min) = (si 0)/(109 + 1 0) si /109. So, for example, the identification number 666666666 would be inserted into bucket number j where j = rn = (666666666/109)(103) = 666.666666 = 666. Similarly, identification number 123456789 would be inserted into bucket number 123, and identification number 666543210 would be inserted into bucket 666. (See Figure 14.6.) Then each bucket would be sorted. Note that the number of elements in each bucket will average 1, so the choice of sorting algorithm will not affect the running time. Finally, the elements are copied back into s, starting with bucket number 0.

Consider the following cascade of two first-order all-pole filters:

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14.10 Under what circumstances would the merge sort be preferred over the other two O(n lg n) sorting algorithms 14.11 Under what circumstances is the quick sort like the selection sort 14.12 Under what circumstances would the quick sort be preferred over the other two O(n lg n) sorting algorithms 14.13 How is the heap sort similar to the selection sort and the insertion sort 14.14 Which algorithm does the Java API use to implement its java.util.Arrays.sort() methods 14.15 A sorting algorithm is said to be stable if it preserves the order of equal elements. Which of the sorting algorithms are not stable 14.16 Which of the nine sorting algorithms outlined in this chapter require extra array space 14.17 Which of the nine sorting algorithms outlined in this chapter would work best on an external file of records 14.18 The merge sort is parallelizable. This means that parts of it can be performed simultaneously, independent of each other, provided that the computer has multiple processors that can run in parallel. This works for the merge sort because several different parts of the array can be subdivided or merged independently of other parts. Which of the other sorting algorithms described in this chapter are parallelizable

CHAP. 14]

(a) Find the variance of the round-off noise at the output of the cascade for an 8-bit processor with

(a) A model for the round-off noise is shown in the following figure:

CHAP. 14]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

H(z) =

Note that because e l ( n ) is filtered by h ( n ) , and e2(n) is only filtered by the second filter in the cascade, which has a unit sample response

14.19 Imagine a Web site that has a Java applet for each sorting algorithm that shows how the algorithm works by displaying an animation of a test run on an array a[] of 256 random numbers in the range 0.0 to 1.0. The animation shows on each iteration of the algorithm s main loop a two-dimensional plot of 256 points (x, y), one for each element in the array, where x = i+1 and y = a[i]. Each plot in Figure 14.7 shows the progress halfway through the sort for one of the following six algorithms: selection sort insertion sort merge sort quick sort heap sort radix sort Match each plot with the sorting algorithm that produced it:

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