- Divide and Conquer Algorithm: - Breaks down a large array into smaller sub-arrays. - Sorts the sub-arrays recursively. - Combines the sorted sub-arrays to achieve the final sorted array.
Key Steps:
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Choose a Pivot:
- Select an element from the array to serve as a reference point (pivot).
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Partition:
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Rearrange elements:
- Elements less than the pivot move to its left.
- Elements greater than the pivot move to its right.
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Pivot ends up in its correct sorted position.
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Recursively Sort Sub-Arrays:
- Recursively apply Quicksort to the left and right sub-arrays (excluding the pivot).
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Base Case:
- If a sub-array has one or no elements, it’s already sorted (recursion stops).
- Efficiency:
- Average-Case Time Complexity: O(n log n)
- Worst-Case Time Complexity: O(n^2) (rare, often mitigated with pivot selection strategies)
- Space Complexity: O(log n) due to recursion
- Advantages:
- Fast in practice for most real-world data sets.
- In-place sorting, requiring little extra memory.
- Considerations:
- Pivot selection can significantly impact performance.
- Can be less efficient for small arrays or already sorted data.
Additional Notes:
- Often used in standard sorting libraries due to its overall efficiency.
- Can be modified to handle various data types and sorting needs.