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Which of the following changes to typical QuickSort improves its performance on average and are generally done in practice.
1) Randomly picking up to make worst case less likely to occur.
2) Calling insertion sort for small sized arrays to reduce recursive calls.
3) QuickSort is tail recursive, so tail call optimizations can be done.
4) A linear time median searching algorithm is used to pick the median, so that the worst case time reduces to O(nLogn)
  • a)
    1 and 2
  • b)
    2, 3, and 4
  • c)
    1, 2 and 3
  • d)
    2, 3 and 4
Correct answer is option 'C'. Can you explain this answer?
Verified Answer
Which of the following changes to typicalQuickSortimproves its perform...
The 4th optimization is generally not used, it reduces the worst case time complexity to O(nLogn), but the hidden constants are very high.
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Which of the following changes to typicalQuickSortimproves its perform...
Improvements in typical QuickSort

Random picking and Insertion sort

One of the major drawbacks of QuickSort is its worst-case complexity O(n^2) when the input array is already sorted or nearly sorted. To avoid this, we can randomly pick a pivot element, making it less likely to occur. Also, calling Insertion Sort for small-sized arrays can reduce the number of recursive calls, improving the performance.

Tail call optimization

Another improvement that can be done is tail call optimization. Since QuickSort is tail-recursive, the compiler can optimize the tail calls, reducing memory usage and improving the performance.

Linear time median searching

Using a linear time median searching algorithm to pick the pivot element can reduce the time complexity of QuickSort to O(nLogn) even in the worst case scenario. This is because the median is guaranteed to split the array into two halves, reducing the number of recursive calls.

Conclusion

In conclusion, the changes mentioned above are commonly done in practice to improve the performance of typical QuickSort. Random picking and calling Insertion Sort for small-sized arrays reduce the likelihood of worst-case complexity, while tail call optimization reduces memory usage. Using a linear time median searching algorithm to pick the pivot element can reduce the time complexity of QuickSort to O(nLogn) even in the worst-case scenario.
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Which of the following changes to typicalQuickSortimproves its performance on average and are generally done in practice.1) Randomly picking up to make worst case less likely to occur.2) Calling insertion sort for small sized arrays to reduce recursive calls.3) QuickSort is tail recursive, so tail call optimizations can be done.4) A linear time median searching algorithm is used to pick the median, so that the worst case time reduces to O(nLogn)a)1 and 2b)2, 3, and 4c)1, 2 and 3d)2, 3 and 4Correct answer is option 'C'. Can you explain this answer?
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