SORTING Michael Tsai 2017328 2 Sorting 3 Applications

SORTING Michael Tsai 2017/3/28

2 Sorting •

3 Applications of Sorting •

4 Applications of Sorting •

5 Categories of Sorting Algo. • Internal Sort: • Place all data in the memory • External Sort: • The data is too large to fit it entirely in the memory. • Some need to be temporarily placed onto other (slower) storage, e. g. , hard drive, flash disk, network storage, etc. • In this lecture, we will only discuss internal sort. • Storage is cheap nowadays. In most cases, only internal sort is needed.

6 Some terms related to sorting •

7 How fast can we sort? • Assumption: compare and swap • Compare: compare two items in the list • Swap: Swap the locations of these two items • How much time do we need in the worst case? [1, 2, 3] Yes [1, 2, 3] No [1, 3, 2] [3, 1, 2] [2, 1, 3] stop [1, 2, 3] stop [2, 1, 3] stop [2, 3, 1] stop [3, 1, 2] stop [3, 2, 1]

8 Decision tree for sorting [1, 2, 3] [2, 1, 3] [1, 3, 2] [2, 1, 3] stop [1, 2, 3] stop [1, 3, 2] • stop [2, 3, 1] stop [3, 1, 2] stop [3, 2, 1]

9 How fast can we sort? •

10 Review: Selection Sort • Select the smallest, move it to the first position. • Select the second smallest, move it to the second position. • …. • The last item will automatically be placed at the last position. ㄅ ㄆ 1 1 ㄆ 2 ㄅ 1 2 ㄆ ㄅ

11 Review: Selection Sort •

12 Insertion Sort • In each iteration, add one item to a sorted list of i item. • Turning it into a sorted list of (i+1) item 2 3 6 5 1 4 2 3 5 6 1 4 1 2 3 5 6 4 1 2 3 4 5 6

13 Pseudo code

14 Insertion Sort •

15 What’s good about insertion sort •

16 Merge Sort • Use Divide-and-Conquer strategy • Divide-and-Conquer: • Divide: Split the big problem into small problems • Conquer: Solve the small problems • Combine: Combine the solutions to the small problems into the solution of the big problems. • Merge sort: • Divide: Split the n numbers into two sub-sequences of n/2 numbers • Conquer: Sort the two sub-sequences (use recursive calls to delegate to the clones) • Combine: Combine the two sorted sub-sequences into the one sorted sequence

17 Merge Sort Divide Conquer x 2 Combine

19 Merge Sort: Example

20 How to combine (merge)? Temporary storage j i 1 Original array 4 1 5 2 8 3 2 4 5 3 6 6 8 9 9

21 Implementation: Merge

22 Merge sort •

23 Quick Sort • Find a pivot(支點), manipulate the locations of the items so that: • (1) all items to its left is smaller or equal (unsorted), • (2) all items to its right is larger • Recursively call itself to sort the left and right sub-sequences. 26 5 37 1 61 11 59 15 48 19 26 5 19 1 61 11 59 15 48 37 26 5 19 1 15 11 59 61 48 37 11 5 19 1 15 26 59 61 48 37

24 Pseudo Code Divide Conquer x 2 No Combine!

25 Quick Sort 11 5 19 1 15 26 59 61 48 37 1 5 11 19 15 26 59 61 48 37 1 5 11 15 19 26 48 37 59 61 1 5 11 15 19 26 37 48 59 61

26 Quick Sort: Worst & Best case •

27 Randomized Quick Sort • Randomly select a pivot 26 5 37 1 61 Swap in advance 11 59 15 48 19

28 Average running time • Time needed for the “ 9/10 subsequence” Time needed for partitioning Time needed for the “ 1/10 subsequence”

29 Average running time

30 Average running time Case 1: Worst case for the first level partition, but best-case for second level. Case 2: Best case for the first level partition Same! (Case 1 has larger constant) The better-partitioned level would “absorb” the extra running time for worse-partitioned level.

31 比較四大金剛 Worst Not covered today! Average Additional Space? Insertion sort O(1) Merge sort O(n) Quick sort O(1) Heap sort O(1) • Insertion sort: quick with small input size n. (small constant) • Quick sort: Best average performance (fairly small constant) • Merge sort: Best worst-case performance • Heap sort: Good worst-case performance, no additional space needed. • Real-world strategy: a hybrid of insertion sort + others. Use input size n to determine the algorithm to use.