1. Sorting Suppose you wanted to write a computer game like Doom 4: The Caverns of Calvin… How do you render those nice (lurid) pictures of Calvin College torture chambers, with hidden surfaces removed? Given a collection of polygons (points, tests, values), how do you sort them? My favorite sort: What are your favorite sorts? Read 6.1-6.5, omit rest of chapter 6.

2. Simple (and slow) algorithms Bubble sort: Selection Sort: Insertion Sort: Which is best? important factors: comparisons, data movement

3. Sorting out Sorting A collection or file of items with keys Sorting may be on items or pointers Sorting may be internal or external Sorting may or may not be stable Simple algorithms: easy to implement slow (on big sets of data) show the basic approaches, concepts May be used to improve fancier algorithms

4. Sorting Utilities We’d like our sorting algorithms to work with all data types… template void exch(Item &A, Item &B) {Item t=A; A=B; B=t; } template void compexch(Item &A, Item &B) {if (B<A) exch(A, B); }

5. Bubble Sort The first sort most students learn And the worst… template void bubble(Item a[], int l, int r) { for (int i=l; i<r; i++) for (int j=r; j>i; j--) compexch(a[j-1], a[j]); } comparisons?something like n 2 /2 date movements?something like n 2 /2

6. Selection Sort Find smallest element Exchange with first Recursively sort rest template void selection(Item a[], int l, int r) { for (int i=1; i<r; i++) { int min=i; for (int j=i+1; j<=r; j++) if (a[j]<a[min]) min=j; exch(a[i], a[min]); } comparisons?n 2 /2 swaps?n

7. Insertion Sort Like sorting cards Put next one in place template void insertion(Item a[], int l, int r) { int i; for (i=r; i>l; i--) compexch(a[i-1],a[i]); for (i=l+2; i<=r; i++) { int j=i; Item v=a[i]; while (v<a[j-1]) { a[j] = a[j-1]; j--; } a[j] = v; } comparisons?n 2 /4 data moves? n 2 /4

8. Which one to use? Selection: few data movements Insertion: few comparisons Bubble: blows But all of these are  (n 2 ), which, as you know, is TERRIBLE for large n Can we do better than  (n 2 )?

9. Merge Sort The quintessential divide-and-conquer algorithm Divide the list in half Sort each half recursively Merge the results. Base case: left as an exercise to the reader

10. Merge Sort Analysis Recall runtime recurrence: T(1)=0; T(n) = 2T(n/2) + cn  (n log n) runtime in the worst case Much better than the simple sorts on big data files – and easy to implement! Can implement in-place and bottom-up to avoid some data movement and recursion overhead Still, empirically, it’s slower than Quicksort, which we’ll study next.

11. Quicksort Pick a pivot; pivot list; sort halves recursively. The most widely used algorithm A heavily studied algorithm with many variations and improvements (“it seems to invite tinkering”) A carefully tuned quicksort is usually fastest (e.g. unix’s qsort standard library function) but not stable, and in some situations slooow…

12. Quicksort template void qsort(Item a[], int l, int r) { if (r<=l) return; int i=partition(a, l, r); qsort(a, l, i-1); qsort(a, i+1, r); } partition: pick an item as pivot, p (last item?) rearrange list into items smaller, equal, and greater than p

13. Partitioning template int partition(Item a[], int l, int r) { int i=l-1, j=r; Item v=a[r]; for (;;) { while (a[++i] < v) ; while (v<a[--j]) if (j==l) break; if (i >= j) break; exch(a[i], a[j]); } exch(a[i], a[r]); return i; }

14. Quicksort Analysis What is the runtime for Quicksort? Recurrence relation? Worst case:  (n 2 ) Best, Average case:  (n log n) When does the worst case arise?  when the list is (nearly) sorted! oops… Recursive algorithms also have lots of overhead. How to reduce the recursion overhead?

15. Quick Hacks: Cutoff How to improve the recursion overhead? Don’t sort lists of size <= 10 (e.g.) At the end, run a pass of insertion sort. In practice, this speeds up the algorithm

16. Quick Hacks: Picking a Pivot How to prevent that nasty worst-case behavior? Be smarter about picking a pivot E.g. pick three random elements and take their median Again, this yields an improvement in empirical performance: the worst case is much more rare (what would have to happen to get the worst case?)

17. Median, Order Statistics Quicksort improvement idea: use the median as pivot Give me an algorithm to: find the smallest element of a list find the 4 th smallest element find the k th smallest element Algorithm idea: sort, then pick the middle element.   (n log n) worst, average case.  This won’t help for quicksort!  Can we do better?

18. Quicksort-based selection Pick a pivot; partition list. Let i be location of pivot. If i>k search left part; if i<k search right part template void select(Item a[], int l, int r, int k) { if (r <= l) return a[r]; int i = partition(a, l, r); if (i > k) return select(a, l, i-1, k); if (i < k) return select(a, i+1, r, k); } Worst-case runtime? O(n 2 ) Expected runtime? O(n)

19. Lower Bound on Sorting Do you think that there will always be improvements in sorting algorithms? better than  (n)? better than  (n log n)? how to prove that no comparison sort is better than  (n log n) in the worst case?  consider all algorithms!? Few non-trivial lower bounds are known. Hard! But, we can say that the runtime for any comparison sort is  (n log n).

20. Comparison sort lower bound How many comparisons are needed to sort? decision tree: each leaf a permutation; each node a comparison: a < b? A sort of a particular list: a path from root to leaf. How many leaves? n! Shortest possible decision tree?  (log n!) Stirling’s formula (p. 43): lg n! is about n lg n – n lg e + lg(sqrt(2 pi n))  (n log n)! There is no comparison sort better than  (n log n) (but are there other approaches to sorting?)