### Basic Sorting

When we have a few objects be it of any kind, and we want to sort them this means that we want to rearrange them somehow so that they follow some order, an order that we define. We can take for example one of the fields, for example if we have sheep, and each sheep has a name, and we want to order the sheep by name then we could just take any sheep and first place the sheep named Avida, and then take the sheep named Guliantra and so on, lexicographically.

Respectively we could sort the sheep that we have by their age, and in this case Guliantra could end up before Avida.

In computer algorithms, sorting is the like the basic algorithms, and you will find that to solve many other problems you would start by first sorting and then progress to actually solve the problem. So sorting could be the first step of many other problem solutions.

While there are a few types of sorting algorithms we have a few shared metrics to evaluate them all. So when you refer me to a specific algorithm we could ask, is this sorting algorithm stable? Is it sorting in place? What is the average worst and best case running time of this sorting algorithm? Which type of data is this sorting algorithm best suited for, how much memory does this sorting algorithm require and does it need actually extra memory?

Let's go through these terms of stable, in place, running time, memory terms.

### Stable Sort

Once you sort a few sheep let's say by year age, it might be the case that a few sheep have the same yearly age.

The question then is this, if the sheep are all lined up but not sorted by yearly age, and you sort them out by their yearly ages, if two sheep have the same age would they have different relative order after the sorting?

Note that we didn't say absolute order, because it might be well the case that the two oldest sheep with the same age are lined up at first on the beginning of the row, but at the end of the sort you line them up at the end of the row.

The question is just if when you sort and line up these oldest sheets at the end of the line if they preserve their original ordering, if they always do when sorted by your sorting algorithm, then we say that your sorting algorithm is relative order preserving for equal items or in other words a stable sort.

Stable sorts include: Merge sort, insertion sort, radix sort, tim sort, bubble sort.

Unstable sorts include: Heap sort, quick sort.

### In Place

When we ask if a sorting algorithm is in place or not we ask about how much extra memory it's using. As long as it's using a constant amount of memory relative to the list that you sort then we say it's in place. You can use additional memory for your sorting, that is all fine, but the question is whether the extra memory is of constant space meaning, it's not growing relatively to the number of items you sort.

It does not matter the relation between the additional memory use to the number of items in list, any such relation means that it's not in place. In order for a sorting algorithm to be in place it must use O(1) extra amount of memory, meaning no extra relation to the original items that you sort, you can sort as many items as you wish your sorting algorithm must only use extract constant amount of memory in order to be called in place.

### Bubble Sort

Bubble sort is a stable sort, it's also in place, it's best time complexity is O(n), it's average time complexity is O(n^2), it's worst time complexity is O(n^2), and it's space complexity is O(1).

In bubble sort we compare each successive part of elements in some given input list, and we invert each such pare if they are not in order.

After we finish one iteration of scanning the whole list, comparing each two items and replace each such two if they are inverted then if we started this comparison from the beginning of the list then this would mean we bubble up the largest element to the n-1 place the end of the list.

Bubble sort is also known as sinking sort because it's repeatedly comparing each two items and swaps if they are at the wrong order, so the items are like sinking to one end of the list.

After one iteration of comparing all the pairs you are sure to have one item sorted, the largest item if you start from the beginning of the list, and after two such iterations of the whole array you have the two largest items ordered at the end of the list.

We said that the best running time of bubble sort is O(n) that is because if we scan the list once, and we see we need not swap any two items we can shortcut and end the sorting here, as we saw the list is sorted already.

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