Sorting Algorithms Questions

Pre-sortedness of the data

Distribution of the data values within a range

Frequency of occurrence of data elements

Order statistics of the data

It is only efficient for datasets with an even number of elements.

It cannot sort datasets containing duplicate values.

Its space complexity can be significant if the range of input values is large.

It is not suitable for sorting strings or objects.

Yes

No

The partitioning process is performed entirely within the original array, without requiring the allocation of substantial additional memory proportional to the input size.

The partitioning step always selects the first element of the subarray as the pivot.

The algorithm sorts the array by recursively dividing it into smaller subarrays and then merging them back together.

In-place partitioning is only applicable when the input array is already sorted in reverse order.

Quick Sort can be easily modified to be stable

Quick Sort is inherently unstable

Quick Sort is inherently stable

The stability of Quick Sort depends on the input data

Yes, counting sort is always stable.

No, counting sort is inherently unstable.

Counting sort can be made stable with modifications to the algorithm.

The stability of counting sort depends on the input data.

Building a binary search tree from the input data

Using a hash table to store and retrieve sorted elements

Recursive division of the input array into smaller subarrays

Iterative comparison of adjacent elements

A poorly chosen pivot can lead to the worst-case time complexity of O(n^2)

Selecting a random pivot always guarantees the best performance

Using the first element as the pivot is generally the most efficient approach

The choice of pivot has no impact on the performance of Quick Sort

Heap Sort typically has the same space complexity

Heap Sort has a lower space complexity

Heap Sort has a higher space complexity

Heap Sort's space complexity depends on the input data

Heap-balance

Heapify

Sift-down

Sift-up