Difference Between Sorting Algorithms
Differentiate Between Sorting Algorithms:-
60% + Problem solved with the help of DSA ( Sorting Algorithms)
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Let's discuss these below Sorting Algorithms and it's time and Space complexity...
Sorting algorithms are a fundamental concept in computer science and programming. They are used to arrange a collection of data, such as a list or an array, into a specific order (e.g., numerical, alphabetical, or by some other criteria).
Here are some of the most commonly used sorting algorithms:
✅Bubble Sort
Repeatedly swaps adjacent elements if they are in the wrong order.
Time complexity: O(n^2) in the average and worst cases.
Simple to implement but not very efficient for large datasets.
✅Selection Sort
Finds the minimum element from the unsorted part of the list and swaps it with the first element of the unsorted part.
Time complexity: O(n^2) in the average and worst cases.
Also simple to implement but not efficient for large datasets.
✅Insertion Sort
Iterates through the list, removing one element at a time and inserting it into the correct position in the sorted portion.
Time complexity: O(n^2) in the average and worst cases.
Efficient for small or nearly-sorted datasets.
✅Merge Sort
Divides the list into two halves, recursively sorts each half, and then merges the sorted halves.
Time complexity: O(n log n) in the average and worst cases.
Efficient for large datasets and good for external sorting (when data doesn't fit in memory).
✅Quicksort
Selects a 'pivot' element from the list and partitions the other elements into two sub-lists, according to whether they are less than or greater than the pivot.
Time complexity: O(n log n) in the average case, but O(n^2) in the worst case (when the list is already sorted or reverse-sorted).
Widely used and efficient for large datasets, but can have poor performance in the worst-case scenario.
✅Heap Sort
Builds a binary heap data structure from the input list and then repeatedly extracts the maximum element.
Time complexity: O(n log n) in the average and worst cases.
Efficient for large datasets and uses less memory than Merge Sort.
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