## How to Approach:

Understanding the common sorting algorithms is incredibly valuable, as many sorting or searching solutions require tweaks of known sorting algorithms. A good approach when you are given a question like this is to run through the different sorting algorithms and see if one applies particularly well.

Example: You have a very large array of ‘Person’ objects. Sort the people in increasing order of age. We’re given two interesting bits of knowledge here: (1) It’s a large array, so efficiency is very important. (2) We are sorting based on ages, so we know the values are in a small range. By scanning through the various sorting algorithms, we might notice that bucket sort would be a perfect candidate for this algorithm. In fact, we can make the buckets small (just 1 year each) and get O(n) running time.

## Bubble Sort:

Start at the beginning of an array and swap the first two elements if the first is bigger than the second. Go to the next pair, etc, continuously making sweeps of the array until sorted. O(n^2).

## Selection Sort:

Find the smallest element using a linear scan and move it to the front. Then, find the second smallest and move it, again doing a linear scan. Continue doing this until all the elements are in place. O(n^2).

## Merge Sort:

Sort each pair of elements. Then, sort every four elements by merging every two pairs. Then, sort every 8 elements, etc. O(n log n) expected and worst case.

## Quick Sort:

Pick a random element and partition the array, such that all numbers that are less than it come before all elements that are greater than it. Then do that for each half, then each quar ter, etc. O(n log n) expected, O(n^2) worst case.

## Bucket Sort:

Partition the array into a finite number of buckets, and then sort each bucket individually. This gives a time of O(n + m), where n is the number of items and m is the number of distinct items.