1. Chapter 8: Sorting

2. Contents Algorithms for Sorting List (both for contiguous lists and linked lists) –Insertion Sort –Selection Sort –Bubble / Quick Sort –Merge Sort –Heap Sort Analysis of Sorting Algorithms

3. Introduction  Internal Sorting (*)  All records to be sorted are kept in memory.  Performance of Sorting Algorithms  Consider both the number of comparisons of keys and the number of times entries must be moved inside a list.  Both the worst-case and the average performance of a sorting algorithm are of interest.

4. Records and Keys  Every Record has an associated key of type Key.  A Record can be implicitly converted to the corresponding Key.  The keys(hence also the records)can be compared Under the operations ‘ ’, ‘ >= ’, ‘ <= ’, ‘ = = ’ and ‘ ! = ’.

5. Insertion Sort

6. Example: Ordered insertion in Contiguous List

7. Trace of sorting a list of six words Examples of Insertion Sorting (Contiguous Version)

8. template void Sortable list :: insertion_sort( ) { int first_unsorted; //position of first unsorted entry int position; //searches sorted part of list Record current; //holds the entry temporarily removed from list for (first_unsorted = 1; first_unsorted < count; first_unsorted++) if (entry[first_unsorted] < entry[first_unsorted - 1]) { position = first_unsorted; current = entry[first_unsorted]; do { //move entry[position] = entry[position - 1]; position--; } while (position > 0 && entry[position - 1] > current); entry[position] = current; //insert } Contiguous Insertion Sorting

10. Analysis of Insertion Sorting  Insertion sort is suitable for both contiguous list and linked list.

11. Insertion Sorting (Linked Version) Linked version algorithm see page 324

12. Selection Sort

13. Motivation Major disadvantage of insertion sort – Even after most entries have been sorted properly into the first part of the list, the insertion of a later entry may require that many of them be moved. – 移动数据 Selection sorting method solves it.

14. Example of Selection Sort Sort a list of six words alphabetically

15. The General Steps of Selection Sort

16. Contiguous Implementation of Selection Sort template void Sortable list :: selection_sort( ) { for (int position = count - 1; position > 0; position--) { int max = max_key(0, position); swap(max, position); }

17. Contiguous Implementation of Selection Sort (cont’) template int Sortable list :: max_key(int low, int high){ int largest, current; largest = low; for (current = low + 1; current <= high; current++) if (entry[largest] < entry[current]) largest = current; return largest; } template void Sortable list :: swap(int low, int high) { Record temp; temp = entry[low]; entry[low] = entry[high]; entry[high] = temp; }

18. Analysis of Selection Sort Less move operations Selection sort is suitable for both contiguous list and linked list.

19. Bubble Sorting void BubbleSort(List &L ){ for (i = 1; i < L.count; ++i ) //count –1 pass for (j = 0; j < L.count – i; ++j) if (L.entry[j] > L.entry[j+1]){ //swap data = L.entry[j]; L.r[j] = L.r[j+1]; L.r[j+1] = data; } O(n 2 )

20. Divide-and-Conquer Sorting Quicksort

21. Motivation It is much easier to sort short lists than long ones. Divide and conquer: dividing a problem into smaller but similar sub-problems. Thus, Divide-and-Conquer Sorting divides the list into two sub-lists and sort them separately.

22. Outline of Divide-and-Conquer Sorting void Sortable_list :: sort( ) { if (the list has length greater than 1) { partition the list into lowlist, highlist; lowlist.sort( ); highlist.sort( ); combine(lowlist, highlist); }

23. Quicksort  Steps of Quicksort 1. First choose a key named pivot, about half the keys will come before it and half after. The simplest rule is to choose the first number in a list as the pivot. 2. Partition the items so that all those with keys less than the pivot come in one sub-list, and all those with greater keys come in another. 3. Sort the two sub-lists separately 4. Put the sub-lists together.

24. Trace and Recursion tree, Quicksort of 7 numbers Pivot is the first key Sort (26, 33, 35, 29, 19, 12, 22)

25. Quicksort for Contiguous Lists template void Sortable_list :: quick_sort( ) { recursive_quick_sort(0, count - 1); } template void Sortable_list :: recursive_quick_sort(int low, int high) { int pivot_position; if (low < high) { pivot_position = partition(low, high); // …. recursive_quick_sort(low, pivot_ position - 1); recursive_quick_sort(pivot_position + 1, high); }

26. Choice of pivot First or last entry –Worst case appears for a list already sorted or in reverse order. Central entry –Poor cases appear only for unusual orders. Random entry –Poor cases are very unlikely to occur. Middle of three — first, last, central entry

27. template void Sortable_list :: int Partition(int low, int high){ Record pivot; int i, Last_small; //position of the last key less than pivot swap(low, (low + high)/2); //interchanges entries in two positions pivot = entry[low]; // First entry is now pivot Last_small = low; for (i = low + 1; i <= high; i++) if (entry[i] < pivot) { Last_small = last_small + 1; swap(last_small, i); // Move large entry to right and small to left } swap(low, last_small); // Put the pivot into its proper position. return last_small; } Partition with Central Entry Pivot

28. {26, 33, 35, 29, 19, 12, 22}, pivot = 29 {29, 33, 35, 26, 19, 12, 22}, i = 3, last_small=1, {29, 26, 35, 33, 19, 12, 22}, i = 4, last_small=2, {29, 26, 19, 33, 35, 12, 22}, i = 5, last_small=3, {29, 26, 19, 12, 35, 33, 22}, i = 6, last_small=4, {29, 26, 19, 12, 22, 33, 35}, low=0, last_small=4, {22, 26, 19, 12, 29, 33, 35} Tracking of Partition

29. Analysis of Quicksort Worst-case analysis: –If the pivot is chosen poorly, one of the partitioned sublists may be empty and the other reduced by only one entry. In this case, quicksort is slower than either insertion sort or selection sort. Average-case analysis: Quicksort is suitable only for contiguous list.

30. Summary of Sorting Methods Use of space –Most sorting methods use little extra space –Quick need some space for tracing recursion execution. Use of computer time –Insertion and selection sort is O(n 2 ) –quick sort is fastest, O(nlogn) //average case Programming effort –Simple sorting methods for small list –Sophisticated sorting methods for large list