This document discusses different sorting techniques and their worst case, average case, and best case time complexities. It represents the running time complexities on the y-axis and number of elements on the x-axis for various sorting algorithms, distinguishing between stable and unstable algorithms. The analysis can be used to select the sorting algorithm with the lowest running time complexity based on the data scenario and amount of data.

1. Sorting Techniques
Santosh Kumar Verma
Assistant Professor-CSE,
JK Lakshmipat University, Jaipur

2. Sorting Techniques
This project represents the three different views in terms of running
time complexity (Worst Case, Average Case, and Best Case) on Y-axis
with respect to number of elements on X-axis.
Name of sorting algorithms are colored according to their nature of
stable sorting or not and the curves represents all possible running
time complexities of sorting algorithms.
Now, one can pick an algorithm with least running time complexity by
analyzing the data scenario (best/average/worst case) and the data
count.

3. Bubble
Selection
Insertion
Quick
Odd-Even
Cocktail Shaker
Bogo Sort
Merge
Heap
Tim
Tree
Introsort
Tournament
Shell Sort
Radix Sort, for d=1
No. of Elements 
RunningTimeComplexity
WORSTCASEANALYSIS
Stable Sorting Algorithm
Unstable Sorting Algorithm
Stability depends on Parameter
Where k=1

4. Bubble
Selection
Insertion
Odd-Even
Cocktail Shaker
Bogo Sort
Merge
Heap
Tim
Tree
Quick
Introsort
Tournament
Shell Sort
Radix Sort, for d=1
No. of Elements 
RunningTimeComplexity
AVERAGECASEANALYSIS
Stable Sorting Algorithm
Unstable Sorting Algorithm
Stability depends on Parameter
Where k=1

5. Selection
Merge
Heap
Quick
Tree
Introsort
Tournament
Radix Sort, for d=1
Bubble
Insertion
Odd-Even
Cocktail Shaker
Shell Sort
Bogo Sort
Tim
No. of Elements 
RunningTimeComplexity
BESTCASEANALYSIS
Stable Sorting Algorithm
Unstable Sorting Algorithm
Stability depends on Parameter
Where k=1