The appropriate Big O notation for the complexity of a sorting algorithm generally depends on the specific algorithm being considered. However, here are some common complexities for different sorting algorithms:
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O(n²): This complexity is applicable to simple sorting algorithms like Bubble Sort and Insertion Sort, which have a worst-case time complexity of O(n²).
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O(n log n): Many efficient sorting algorithms, like Merge Sort and Quick Sort, have an average and worst-case time complexity of O(n log n).
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O(n): This is possible in specific cases with counting sort or radix sort, but it's not typical for comparison-based sorting.
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O(log n) and O(1) are not relevant complexities for sorting algorithms.
Based on common sorting algorithms, O(n²) is one of the simplest and more traditional complexities for sorting (like Bubble Sort, Insertion Sort, etc.), but it's important to note that more efficient algorithms typically have a complexity of O(n log n). If you are looking for a single response from your options provided, O(n²) would be an appropriate choice for general cases but is not necessarily the best overall.
If the question requires identifying just one, the closest answer would still be O(n²) given the options.
So the answer is: O(n²) (upper O left parenthesis n squared right parenthesis).
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