The Randomized Selection Method

The Randomized Selection Method is an algorithm used to efficiently find the k-th smallest item in a list. Instead of checking every element, it randomly picks a pivot and partitions the list into elements smaller and larger than that pivot. Depending on the position of the pivot, it decides whether to search the smaller or larger partition. This process repeats recursively, narrowing down the search, until the k-th smallest element is found. The randomness helps improve average performance, making it faster than sorting the entire list, especially with large data sets.