1. CS 206 Introduction to Computer Science II 12 / 08 / 2008 Instructor: Michael Eckmann

2. Michael Eckmann - Skidmore College - CS 206 - Fall 2008 Today’s Topics Questions/comments? Quicksort Selection problem

3. Quicksort algorithm 1) if size of list, L is 0 or 1, return 2) pick some element in list as a pivot element 3) divide the remaining elements (minus the pivot) of L into two groups, L 1, those with elements less than the pivot, and L 2, those with elements greater than or equal to the pivot 4) return (Quicksort(L 1 ) followed by pivot, followed by Quicksort(L 2 ))‏ Depending on which is the pivot element, the sizes of the two sides could differ greatly. Compared to mergeSort, Quicksort does not guarantee equal size portions to sort (which is bad.) But, the divide stage can be done in-place (without any additional space like another array.)‏ Quicksort

4. To pick some element in list as a pivot element we can either –pick the first (bad if list is almost sorted, why?)‏ –pick a random one (random # generation is time consuming)‏ –a good way is to pick the pivot is the median of 3 elements (say the median of the first, middle and last element) not much extra work the almost sorted case isn't a problem for this Divide strategy – how to divide our list into two sublists of less than pivot and greater than pivot (assume all elements distinct for now)‏ The strategy about to be described gives good results. Quicksort

5. Divide strategy 1) swap the pivot with the last in the list 2) start index i pointing to first in list and index j to next to last element 3) while (element at i < pivot)‏ increment i 4) while (element at j >= pivot)‏ decrement j 5) if (i pivot and element at j is < pivot so, we swap them and repeat from step 3. 6) when i > j, we swap the pivot that is in the last place with the element at i. Quicksort

6. Notice that in the best case, if Quicksort could divide the list in equal portions at each level then we would have the fewest recursion levels O(log 2 n)‏ The work to be done on each level is on the order of n. So in the best case quicksort is O(n log 2 n)‏ Any ideas on what it'd be in the worst case? Quicksort

7. Let's write Quicksort –We can make quicksort be a recursive method that takes in an array the starting index of the data to be sorted (why do we need this?)‏ the number of elements of the data to be sorted (why do we need this?)‏ –quicksort will call a method to partition the elements find a pivot and divide a (portion of a) list into elements less than pivot, followed by pivot, followed by elements greater than pivot This method will take in –an array –the starting index of the data to be partitioned –the number of elements of the data to be partitioned and it will return the pivot index and alter the order of the elements of a subset of the array passed in Quicksort

8. A typical speedup for Quicksort is to do the following: –when we get down to some small number of elements (say 10) in our list, instead of using quicksort on them, we do insertion sort. –Let's use that xSort applet to visualize insertion sort. How would we alter the code we just wrote to do insertion sort when the number of elements to sort is small? Quicksort

9. Let's discuss a similar algorithm that solves the Selection problem. The selection problem is the desire to find the kth smallest item in an unsorted list. (e.g. in a list indexed from 0 to 99, if we wanted to find the 3 rd smallest item, k=3 and if our list was sorted (which it isn't) this item would live at index 2)‏ We can use the partition ideas from quicksort. If the pivot happens to live at index k-1, we're done. If not, then focus only on the side of the pivot that k is on. That is, if pivot happens to be at index j after partitioning, then if k-1 j then only work on the right list and if k-1==j, then we're done. Let's see if we can code this now. Selection problem