# The Merge Sort¶

We now turn our attention to using a divide and conquer strategy as a
way to improve the performance of sorting algorithms. The first
algorithm we will study is the **merge sort**. Merge sort is a recursive
algorithm that continually splits a list in half. If the list is empty
or has one item, it is sorted by definition (the base case). If the list
has more than one item, we split the list and recursively invoke a merge
sort on both halves. Once the two halves are sorted, the fundamental
operation, called a **merge**, is performed. Merging is the process of
taking two smaller sorted lists and combining them together into a
single, sorted, new list. Figure 10 shows our familiar example
list as it is being split by `mergeSort`

. Figure 11 shows
the simple lists, now sorted, as they are merged back together.

The `mergeSort`

function shown in ActiveCode 1 begins by asking the
base case question. If the length of the list is less than or equal to
one, then we already have a sorted list and no more processing is
necessary. If, on the other hand, the length is greater than one, then
we use the Python `slice`

operation to extract the left and right
halves. It is important to note that the list may not have an even
number of items. That does not matter, as the lengths will differ by at
most one.

Once the `mergeSort`

function is invoked on the left half and the
right half (lines 8–9), it is assumed they are sorted. The rest of the
function (lines 11–31) is responsible for merging the two smaller sorted
lists into a larger sorted list. Notice that the merge operation places
the items back into the original list (`alist`

) one at a time by
repeatedly taking the smallest item from the sorted lists.

The `mergeSort`

function has been augmented with a `print`

statement
(line 2) to show the contents of the list being sorted at the start of
each invocation. There is also a `print`

statement (line 32) to show
the merging process. The transcript shows the result of executing the
function on our example list. Note that the list with 44, 55, and 20
will not divide evenly. The first split gives [44] and the second gives
[55,20]. It is easy to see how the splitting process eventually yields a
list that can be immediately merged with other sorted lists.

In order to analyze the `mergeSort`

function, we need to consider the
two distinct processes that make up its implementation. First, the list
is split into halves. We already computed (in a binary search) that we
can divide a list in half \(\log n\) times where *n* is the
length of the list. The second process is the merge. Each item in the
list will eventually be processed and placed on the sorted list. So the
merge operation which results in a list of size *n* requires *n*
operations. The result of this analysis is that \(\log n\) splits,
each of which costs \(n\) for a total of \(n\log n\)
operations. A merge sort is an \(O(n\log n)\) algorithm.

Recall that the slicing operator is \(O(k)\) where k is the size
of the slice. In order to guarantee that `mergeSort`

will be
\(O(n\log n)\) we will need to remove the slice operator. Again,
this is possible if we simply pass the starting and ending indices along
with the list when we make the recursive call. We leave this as an
exercise.

It is important to notice that the `mergeSort`

function requires extra
space to hold the two halves as they are extracted with the slicing
operations. This additional space can be a critical factor if the list
is large and can make this sort problematic when working on large data
sets.

Self Check