1. CS 361 – Chapters 8-9 Sorting algorithms –Selection, insertion, bubble, “swap” –Merge, quick, stooge –Counting, bucket, radix How to select the n-th largest/smallest element without sorting.

2. The problem Arrange items in a sequence so that their keys are in ascending or descending order. Long history of research. Many new algorithms are tweaks of famous sorting algorithms. Some methods do better based on how values distributed, size of data, nature of underlying hardware (parallel processing, memory hierarchy), etc. Some implementations given on the class Web site and also: http://cg.scs.carleton.ca/~morin/misc/sortalg

3. Some methods Selection sort: Find the largest value and swap it into first position, find 2 nd largest value and put it 2 nd, etc. Bubble sort: Scan the list and see which consecutive values are out of order, and swap them. Multiple passes are required. Insertion sort: Place the next element in the correct place by shifting other ones over to make room. We maintain a boundary between the sorted and unsorted parts of the list. Merge sort: Split list in half until just 1-2 elements. Merge adjacent lists by collating them.

4. Analysis What is the best case (  ) time for any sorting algorithm? Selection, bubble, and insertion sort all run in O(n 2 ) time. –Why? –Among the three: which is the best, which is the worst? Merge sort runs in O(n log 2 n) time. –If we imagine the tree of recursive calls, the nested calls go about log 2 n deep. At each level, we must do O(1) work at each of the n values. –Later, we will use a more systematic approach to compute the complexity of recursive algorithms.

5. Quick sort Like merge sort, it’s a recursive algorithm based on divide and conquer. Call quickSort initially with parameters (array, 0, n – 1). quickSort(a, p, r): if p < r: q = partition(a, p, r) quickSort(a, p, q) quickSort(a, q+1, r) What makes quick sort distinctive is its partitioning. See handout.

6. QS partitioning Given a sub-array spanning indices p..r Let x = value of first element here, i.e. a[p] We want to put smaller values on left side and larger values on right side of this array slice. We return the location of the boundary between the low and high regions. Practice with handout!

7. Counting sort Designed to run in linear time Works well when range of values is not large Find how many values are less than x = a[i]. That will tell you where x belongs in output sorted array. for i = 1 to k: C[i] = 0 for i = 1 to n:// Let C[x] = #elements == x ++ C[A[i]] for i = 2 to k:// Let C[x] = #elements <= x C[i] += C[i – 1] for i = n downto 1:// Put sorted values into B. B[C[A[i]]] = A[i] -- C[A[i]]// try: 3,6,4,1,3,4,1,4

8. Bucket sort Assume array’s values are (more or less) evenly distributed over some range. Create n buckets, each covering 1/n of the range. Insert each a[i] into the appropriate bucket. If a bucket winds up with 2+ values, use any method to sort them. Ex. { 63, 42, 87, 37, 60, 58, 95, 75, 97, 3 } –We can define buckets by tens

9. Radix sort Old and simple method. Sort values based on their ones’ digit. –In other words, write down all numbers ending with 0, followed by all numbers ending with 1, etc. Continue: Sort by the tens’ digit. Then by the hundreds’ digit, etc. Can easily be modified to alphabetize words. Technique also useful for sorting records by several fields.

10. Stooge sort Designed to show that divide & conquer does not automatically mean a faster algorithm. Soon we will learn how to mathematically determine the exact O(g(n)) runtime. stoogeSort(A, i, j): if A[i] > A[j]: swap A[i] and A[j] if i+1 >= j: return k = (j – i + 1) / 3 stoogeSort(A, i, j – k) // how much of A? stoogeSort(A, i + k, j) stoogeSort(A, i, j – k)

12. Selection The “selection problem” is: given a sequence of values, return the k-th smallest value, for some k. –If k = 1 or n, problem is simple. –It would be easy to write a O(n log n) algorithm by sorting all values first. But this does unnecessary work. A randomized method with expected runtime of O(n). –Based on randomized quick sort: choose any value to be the pivot –So it’s called “randomized quick select” –Algorithm takes as input S and k, where 1  k  n. (Indices count from 1)

13. Pseudocode quickSelect(S,k): if n = 1 return S[1] x = random element from S L = [ all elements < x ] E = [ all elements == x ] G = [ all elements > x ] if k <= |L| return quickSelect(L, k) else if k <= |L| + |E| return x else return quickSelect(G, k – |L| - |E|) // e.g. 12 th out of 20 = 2 nd out of 10

14. Analysis To find O(g(n)), we are going to find an upper bound on the “expected” execution time. Expected, as in expected value – the long term average if you repeated the random experiment many times. Book has some preliminary notes… –You can add expected values, but not probabilities. –Consider rolling 1 vs. 2 dice. P 1 (rolling 4) = 1/6 but P 2 (rolling 8) = 5/36 So the probabilities don’t add! You can only add probability if it’s 2 alternatives of the same experiment, e.g. rolling a 4 or a 5 on one die: 1/6+1/6. –Exp(1 die) = 3.5Exp(2 dice) = 7 These values can add.

15. Selection proof We want to show that the selection algorithm is O(n). The algorithm is based on partitioning S. –Define a “good” partition = where x is in the middle half of the distribution of values (not in the middle half of locations). –Probability = ½, and sizes of L and of G used for the next recursive call have sizes .75n. How many recursive calls until we have a “good” partition? Same as asking how many times a coin flips until we get heads: we would expect 2. Overhead in doing one function invocation. –We need a loop, so this is O(n). Say, bn for some constant b. T(n) = expected time of algorithm T(n)  T(.75n) + 2bn

16. Work out recurrence T(n)  T(.75n) + 2bn Let’s expand T(.75n)  T(.75 2 n) + 2b(.75n) Substitute: T(n)  T(.75 2 n) + 2b(.75n) + 2bn Expand: T(.75 2 n)  T(.75 3 n) + 2b(.75 2 n) Substitute: T(n)  T(.75 3 n) + 2b(.75 2 n) + 2b(.75n) + 2bn We can keep going and eventually the argument of T on the right side becomes at most 1. (The O(1) base case.) When does that occur? Solve for k: (.75 k n)  1 (3/4) k  1/n  (4/3) k  n  k  log 4/3 n  k = ceil(log 4/3 n) So, T(n)  T(1) + k terms of 2bn multiplied by.75 i (for i = 0 to k) (This sum is at most 4.) T(n)  O(1) + 2bn (4) = O(n)

17. Merge sort We can use the same technique to analyze merge sort. (p. 248). Let’s look at cost of recursive case: T(n) = 2 T(n/2) + cn Expand recursive case: T(n/2) = 2 T(n/4) + c(n/2) Substitute: T(n) = 2 T(n/2) + cn = 2 [ 2 T(n/4) + c(n/2) ] + cn = 4 T(n/4) + 2cn Expand: T(n/4) = 2 T(n/8) + c(n/4) Substitute: T(n) = 4 T(n/4) + cn = 4 [ 2 T(n/8) + c(n/4) ] + 2cn = 8 T(n/8) + 3cn. See a pattern? T(n) = 2 k T(n/2 k ) + kcn At some point, n/2 k = 1  n = 2 k  k = log 2 n. T(n) = 2 log 2 n T(1) + (log 2 n) cn = O(n log 2 n).

18. Stooge sort Yes, even Stooge sort can be analyzed a similar way! T(n) = 3 T((2/3)n) + cn Expand: T((2/3)n) = 3 T((4/9) n) + c((2/3)n) Substitute: T(n) = 3 T((2/3)n) + cn = 3 [ 3 T((4/9) n) + c((2/3)n) ] + cn = 9 T((4/9) n) + 3cn Expand: T((4/9) n) = 3 T((8/27) n) + c((4/9)n) Substitute: T(n) = 9 T((4/9) n) + 3cn = 9 [3 T((8/27) n) + c((4/9)n) ] + 3cn = 27 T((8/27) n) + 7cn Continuing, we observe: T(n) = 3 k T((2/3) k n) + (2 k – 1)cn

19. Stooge sort (2) At some point, the recursive argument reaches (or goes below) 1. ((2/3) k n) = 1  (2/3) k = 1/n  (3/2) k = n  k = log 3/2 n So T(n) = 3 log 3/2 n T(1) + (2 log 3/2 n ) – 1) cn = O(3 log 3/2 n ) + O(n 2 log 3/2 n ) Is this exponential complexity? No – let’s simplify: 3 log 3/2 n = ((3/2) log 3/2 3 ) log 3/2 n = ((3/2) log 3/2 n ) log 3/2 3 = n log 3/2 3 The other term can be simplified similarly and we have n 1 + log 3/2 2 which turns out to be the same order. T(n) = O(n log 3/2 3 ).