1. Lecture 25 Selection sort, reviewed Insertion sort, reviewed Merge sort Running time of merge sort, 2 ways to look at it Quicksort Course evaluations

2. Selection sort for k = 1 to n-1 –find kth smallest item –swap it with the one in the kth position number of comparisons –n-1 for k=1 –n-2 for k=2 –…–… –1 for k=n-1 total: n(n-1)/2

3. Insertion sort for k=2 to n –move kth item into sorted order with respect to the first k-1 items which already sorted –involves moving sorted items over: can do this at the same time as finding where kth item has to go number of comparisons: worst case –1 for k=2 –2 for k=3 –…–… –n-1 for k=n total: n(n-1)/2 on average, maybe n(n-1)/4, but involves more moving of data than selection sort

4. Bubble sort Pass through data, swapping neighbors that are out of order After first pass, largest element has “sunk” to bottom Repeat until no more swaps are needed, considering one less pair each time Cost: n-1 comparisons for first pass, n-2 for 2 nd pass, etc. May need n-1 passes, so also “O(n 2 )”

5. Merge sort Recursive If only one item, return Sort first half by merge sort Sort second half by merge sort Merge the results

6. Keys to efficiency Recursion: reduce problem to two problems of half the size Also known as: divide and conquer Merge step: only requires n comparisons, where n is number of items to be merged Need two arrays, but no more Do not construct arrays inside the recursive call!!! (The comprehensive edition of Liang does this) How should we implement this in Java? public static sort(what parameters?) We cannot pass “half an array”, so what to do?

7. Details for sort public static void sort(int left, int right, double[] x, double[] temp){ if(left == right){ return; } int mid = (left+right)/2; sort(left, mid, x, temp); // sort first half of x using temp sort(mid+1, right, x, temp); // sort second half of x using temp merge(left, mid, right, x, temp); // merge them into temp copy(left, right, temp, x); // copy temp back to x }

8. Details for merge(left, mid, right, x, temp) Keep 3 ints: i,j,k i initialized to left j initialized to mid+1 k initialized to left advance until i > mid or j > right copy the rest of one of the other half cute trick: 2 additional while loops another trick: do nothing if x[mid] <= x[mid+1] for this reason, better move the copy inside merge

9. Number of comparisons Let C(n) = number of comparisons to sort array of length n by merge sort Clearly, C(n) = 2 C(n/2) + (n-1), if n is even, > 0 or a little less, if we program merge efficiently C(1) = 0 C(2) = 1 C(4) = 5 C(8) = 17 C(16) = 49 Hard to see a pattern, but when we double n, the number of comparisons is also doubled, + an extra n-1 comparisons

10. Let’s simplify this slightly Count the merge as n comparisons, instead of n-1 C(n) = 2 C(n/2) + n, if n is even, > 0 C(1) = 0 C(2) = 2 C(4) = 8 C(8) = 24 C(16) = 64 Do you see a pattern now? Assume n is a power of 2, say 2 k By inspection, C(n) = k n In other words, C(n) = n log 2 (n)

11. Proof by induction on k Base case: C(1) = 1 log 2 (1) = 0 Inductive hypothesis: suppose true for k-1: C(2 k-1 ) = (k-1) 2 k-1 Just like: assume the recursive magic works Want to prove true for n=k C(2 k ) = 2 C(2 k-1 ) + 2 k = 2 (k-1) 2 k-1 + 2 k = (k-1) 2 k + 2 k = k 2 k

12. Proof that all horses have same color proof by induction on number of horses if n=1, true inductive hypothesis: suppose true for n-1: if you have n-1 horses, they are all same color given n horses in a corral, take 1 out: rest must have same color put it back and take a different one out: rest must have same color therefore they are all the same color what is wrong with this?

13. An easier way to think about merge sort 1 call to sort array of length n 2 calls to sort array of length n/2 4 calls to sort array of length n/4 8 calls to sort array of length n/8 … n calls to sort array of length 1 at each level, the merge operations take a total of at most n comparisons since the number of levels is log 2 n the total number of comparisons is at most n log 2 n

14. What if n is not a power of 2? Does not really matter: mid = (left + right)/2 rounds down which is fine Number of comparisons is still approximately n log 2 n (which is not an integer) We say running time is O(n log 2 n) Base of logarithm doesn’t matter much, because log 10 n = log 2 n/log 2 10

15. Quicksort Similar recursive idea, but avoids the need for 2 arrays Chooses a pivot element, then divides array into two parts, one with elements ≤ pivot, and one with elements > pivot Possible ways to choose pivot: see next page To partition the array, loop from left to find first element > pivot, and loop from right to find first element <= pivot Swap them and repeat Place pivot in right place Make two recursive calls

16. How to choose the pivot Goal: want to choose pivot to divide array approximately in half, but want to do this fast Ideas? First position middle position average value median value randomly chosen median of items in first, middle and last positions

17. Number of comparisons for quicksort Assuming arrays divided approximately in half each time, same as merge sort Advantage: only one array Disadvantage: items with equal values may end up interchanged from their original value Doesn’t matter for primitive types, but may matter for objects For this reason the Arrays.sort methods use quicksort for primitive types and merge sort for Comparable objects

18. Other sorts Heapsort: slightly slower than quicksort on average but number of comparisons is n log 2 n even in worst case, like merge sort Radix sort

19. Big O Notation We say a function f(n) is O(g(n)) if there is a constant c so that f(n) ≤ c g(n) for all n Thus we say the number of comparisons for merge sort is O(n log n) We don’t need to write the base Which of these functions are O of the others: log n, 2 n, 10 n, n 2, n 3, n, n log n