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Complexity: $O(nk)$ for $n$ keys which have $k$ digits Best Use Case: Large datasets where keys are integers or can be represented as integer sequences (like strings).

Complexity: Always $O(n^2)$ Best Use Case: Small datasets where simplicity is important and memory usage is a concern.

Complexity: $O(n+k)$ where $k$ is the range of the non-negative key values Best Use Case: Small integer range datasets and when counting the occurrences of each unique element is possible.

Complexity: Average-case $O(n + k)$, Worst-case $O(n^2)$ Best Use Case: Datasets that are uniformly distributed across the range and when there is a way to evenly distribute data into buckets.

Complexity: Average and worst-case $O(n^2)$, Best-case $O(n)$ Best Use Case: Small datasets or when the data is nearly sorted.

Complexity: Always $O(n \log n)$ Best Use Case: Large datasets where stable sort and predictable time are important, with enough space to handle the copies of the array.

Complexity: Average-case $O(n \log n)$, Worst-case $O(n^2)$ Best Use Case: Large datasets with high-performance requirements, especially when in-place sorting is needed.

Complexity: Always $O(n \log n)$ Best Use Case: Sorting large datasets where an in-place sort and memory usage are important factors.

Complexity: Average and worst-case $O(n^2)$, Best-case $O(n)$ Best Use Case: Small datasets, nearly sorted data, or when simplicity is valued.