Efficiency of an algorithm depends on two parameters:
1. Time Complexity
2. Space Complexity
Time Complexity: Time Complexity is defined as the number of times a particular instruction set is executed rather than the total time is taken. It is because the total time taken also depends on some external factors like the compiler used, processor’s speed, etc.
Space Complexity: Space Complexity is the total memory space required by the program for its execution.
Both are calculated as the function of input size(n).
One important thing here is that in spite of these parameters the efficiency of an algorithm also depends upon the nature and size of the input.
Following is a quick revision sheet that you may refer at last minute
| Algorithm | Time Complexity | |||
|---|---|---|---|---|
| Best | Average | Worst | ||
| Selection Sort | Ω(n^2) | θ(n^2) | O(n^2) | |
| Bubble Sort | Ω(n) | θ(n^2) | O(n^2) | |
| Insertion Sort | Ω(n) | θ(n^2) | O(n^2) | |
| Heap Sort | Ω(n log(n)) | θ(n log(n)) | O(n log(n)) | |
| Quick Sort | Ω(n log(n)) | θ(n log(n)) | O(n^2) | |
| Merge Sort | Ω(n log(n)) | θ(n log(n)) | O(n log(n)) | |
| Bucket Sort | Ω(n+k) | θ(n+k) | O(n^2) | |
| Radix Sort | Ω(nk) | θ(nk) | O(nk) | |
Also, see:
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above
Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the DSA Self Paced Course at a student-friendly price and become industry ready.
