Implementing Bucket Sort Algorithm with Python
Bucket sort is a non-comparison-based sorting algorithm based on the divide-and-conquer principle. It achieves overall order by dividing data into buckets, sorting elements within each bucket, and then merging the results. Core steps: First, divide data into buckets based on distribution characteristics; then sort elements in each bucket using simple sorting methods (e.g., built-in sort); finally, merge all bucket results. Applicable scenarios: When data is uniformly distributed and within a limited range, its efficiency approaches linear time complexity (O(n)). However, non-uniform distribution may degrade it to O(n²), making it less performant than quicksort. Python implementation (taking 0-1 interval floating-point numbers as an example): Create n empty buckets (where n is the length of the data), assign data to corresponding buckets using the formula `int(num * n)`, sort elements within each bucket, and merge all bucket elements. The code is concise but requires adjusting the bucket index calculation according to the data range and optimizing bucket size to avoid extreme value concentration. Summary: Bucket sort is suitable for uniformly distributed data. It leverages divide-and-conquer to reduce complexity but requires attention to data distribution characteristics to avoid performance degradation.
Read MoreImplementing QuickSort Algorithm in Java
QuickSort is based on the divide-and-conquer approach. Its core involves selecting a pivot element to partition the array into elements less than and greater than the pivot, followed by recursively sorting the subarrays. With an average time complexity of O(n log n), it is a commonly used and efficient sorting algorithm. **Basic Steps**: 1. Select a pivot (e.g., the rightmost element). 2. Partition the array based on the pivot. 3. Recursively sort the left and right subarrays. **Partition Logic**: Using the rightmost element as the pivot, define an index `i` to point to the end of the "less than pivot" region. Traverse the array, swapping elements smaller than the pivot into this region. Finally, move the pivot to its correct position. The Java code implements this logic. The time complexity is O(n log n) on average and O(n²) in the worst case, with an average space complexity of O(log n). A notable drawback is that QuickSort is an unstable sort, and its worst-case performance can be poor, so optimizing the pivot selection is crucial to improve performance.
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