Arjun Suresh (talk | contribs) |
Arjun Suresh (talk | contribs) |
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Line 31: | Line 31: | ||
|Quick | |Quick | ||
|$\theta(n^{2})$ (when array is sorted, and pivot taken from one end) | |$\theta(n^{2})$ (when array is sorted, and pivot taken from one end) | ||
− | |$\theta(n\ | + | |$\theta(n\lg n)$ |
|$\theta(nlgn)$ | |$\theta(nlgn)$ | ||
|0 (when array is already sorted) | |0 (when array is already sorted) |
Algorithm | Worst Case | Average Case | Best Case | Min. no. of swaps | Max. no. of swaps |
---|---|---|---|---|---|
Bubble | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ |
Selection | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ | 0 (when array is already sorted) | $\theta(n^{2})$ |
Insertion | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n)$ (when array is sorted) | 0 (when array is already sorted) | $\theta(n^{2})$ |
Quick | $\theta(n^{2})$ (when array is sorted, and pivot taken from one end) | $\theta(n\lg n)$ | $\theta(nlgn)$ | 0 (when array is already sorted) | $\theta(n^{2})$ |
Merge | $\theta(nlgn)$ | $\theta(nlgn)$ | $\theta(nlgn)$ | Is not in-place sorting | Is not in-place sorting |
Heap | $\theta(nlgn)$ | $\theta(nlgn)$ | $\theta(nlgn)$ | $O(nlgn)$ | $\theta(nlgn)$ |
Algorithm | Worst Case | Average Case | Best Case | Min. no. of swaps | Max. no. of swaps |
---|---|---|---|---|---|
Bubble | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ |
Selection | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n^{2})$ | 0 (when array is already sorted) | $\theta(n^{2})$ |
Insertion | $\theta(n^{2})$ | $\theta(n^{2})$ | $\theta(n)$ (when array is sorted) | 0 (when array is already sorted) | $\theta(n^{2})$ |
Quick | $\theta(n^{2})$ (when array is sorted, and pivot taken from one end) | $\theta(n\lgn)$ | $\theta(nlgn)$ | 0 (when array is already sorted) | $\theta(n^{2})$ |
Merge | $\theta(nlgn)$ | $\theta(nlgn)$ | $\theta(nlgn)$ | Is not in-place sorting | Is not in-place sorting |
Heap | $\theta(nlgn)$ | $\theta(nlgn)$ | $\theta(nlgn)$ | $O(nlgn)$ | $\theta(nlgn)$ |