Sorting

For a quick wordy comparison, take a look at: http://bit.ly/sortsCompared

A Visualization is worth a 1000 tables

http://j.mp/mergeVsQuick

A Table is Worth a 1000 pictures

https://en.wikipedia.org/wiki/Sorting_algorithm#Comparison_of_algorithms

Classifier	Selection	Insertion	Merge	Quick
Type	Simple	Simple	Efficient	Efficient
Extra Space	Not required	Not required	Required	Required
Constructs	Two for loops	Two for loops	Terminal cases and Recursive calls	Terminal case and Recursive calls
Stable	Yes	No	Yes	No
Strategy	Only Comparison	Only Comparison	Divide and Conquer	Divide and Conquer
Most preferable for	small size lists	small size lists	bigger lists	bigger lists
Parallelism	Not applicable	Not applicable	Most suitable	Suitable
Complexity O(n)	n**2	n**2	nlogn - worst case	n**2 - worst case
Used by		Python's timsort	Python's timsort	Linux's sort

Pictures, pictures galore

"A picture is worth a 1000 words." The pictures help convey the contrasting dynamics that occur between the four sorts that need to covered in the lab.

[TOC]

Modified Bubble Sort Approach

• Typically O(n**2) and therefore good only for small data sets.

• Most likely they are stable sorts - meaning they maintain the order in which equal elements appear in the list

• http://interactivepython.org/runestone/static/pythonds/SortSearch/TheSelectionSort.html
• http://interactivepython.org/runestone/static/pythonds/SortSearch/TheInsertionSort.html

Selection Sort

selection

Insertion Sort

insertion

Divide and Conquer Approach

• Typically O(n*logn) and therefore suitably for bigger data sets.

• As in the case of Merge Sort, it requires additional space (so for real large sets, this can be a disadvantage). For Quick Sort, additional nlog(n) space is required.

• Typically, as in the case of Quick Sort, they are also not stable sorts - the order of equal elements may not be maintained after the sort is complete

• http://interactivepython.org/runestone/static/pythonds/SortSearch/TheMergeSort.html
• http://interactivepython.org/runestone/static/pythonds/SortSearch/TheQuickSort.html

Merge Sort

Merge

Quick Sort

split

partition1

partition2

Clarifying Questions

1. Which of the following sort algorithms are guaranteed to be O(n log n) even in the worst case?

2. (A) Selection Sort

3. (B) Quick Sort

4. (C) Merge Sort

5. (D) Insertion Sort

6. Which takes the least amount of extra space for sorting to happen? Which takes the most amount of extra space?

7. A. Selection Sort, Merge Sort

8. B. Insertion Sort, Quick Sort

9. C. Insertion Sort, Merge Sort

10. D. None of the above combinations

11. What is the correct order in terms of increased efficiency of sorting?

12. A. Selection, Insertion, Quick and Merge

13. B. Insertion, Selection, Merge and Quick

14. C. Selection, Insertion, Merge and Quick

15. D. None of the above