Sorting algorithms represent foundational knowledge that every computer scientist and IT professional should at least know at a basic level. And it turns out to be a great way of learning about why arrays are important well beyond mathematics.

In this section, we’re going to take a look at a number of well-known sorting algorithms
with the hope of sensitizing you to the notion of *performance*–a topic that is covered
in greater detail in courses such as algorithms and data structures.

This is not intended to be a comprehensive reference at all. The idea is to learn how these classic algorithms are coded in the teaching language for this course, C#, and to understand the essentials of analyzing their performance, both theoretically and experimentally. For a full theoretical treatment, we recommend the outstanding textbook by Niklaus Wirth [WirthADP], who invented the Pascal language. (We have also adapted some examples from Thomas W. Christopher’s [TCSortingJava] animated sorting algorithms page.

We’ll begin by introducing you to a simple method, whose only purpose in life is to
swap two data values at positions `m` and `n` in a given integer array:

1 2 3 4 5 6 7 8 | ```
public static void exchange (int[] data, int m, int n)
{
int temporary;
temporary = data [m];
data [m] = data [n];
data [n] = temporary;
}
``` |

In general, swapping two values in an array is no different than swapping any two integers.
Suppose we have the following integers `a` and `b`:

```
int a, b;
int t;
a = 25;
b = 35;
t = a;
a = b;
b = t;
```

After this code does its job, the value of `a` would be 35 and the value of `b` would be 25.

So in the `exchange()` function above,
if we have two different array elements at positions `m` and `n`,
we are basically getting each value at these positions,
e.g. `data[m]` and `data[n]` and treating them
as if they were `a` and `b` in the above code.

You might find it helpful at this time to verify that the above code does what we’re saying it does, and a good way is to type it directly into the C# interpreter (csharp) so you can see it for yourself.

The `exchange()` function is vital to all of the sorting algorithms in the following way.
It is used whenever two items are found to be out of order.
When this occurs, they will be *swapped*. This doesn’t mean
that the item comes to its final resting place in the array.
It just means that for the moment, the items
have been reordered so we’ll get closer to having a sorted array.

Let’s now take a look at the various sorting algorithms.

The Bubble Sort algorithm works by repeatedly scanning
through the array exchanging adjacent elements that are out of order. Watching
this work with a strategically-placed `Console.WriteLine()` in the outer
loop, you will see that the sorted array grows right to left. Each
sweep picks up the largest remaining element and moves to the right as
far as it can go. It is therefore not necessary to scan through the
entire array each sweep, but only to the beginning of the sorted
portion.

We define the number of *inversions* as the number of element pairs that
are out of order. They needn’t be adjacent. If `data[7] > data[16]`,
that’s an inversion. Every time an inversion is required,
we also say that there is corresponding data *movement*. If you look at the
`exchange()` code, you’ll observe that a swap requires three movements
to take place, which happens very quickly on most processors but still amounts
to a significant cost.

There can be at most \(N \cdot \frac{N-1}{2}\) inversions in the array of length \(N\). The maximum number of inversions occurs when the array is sorted in reverse order and has no equal elements.

Each exchange in Bubble Sort removes precisely one inversion; therefore, Bubble Sort requires \(O(N^2)\) exchanges.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
public static void IntArrayBubbleSort (int[] data)
{
int i, j;
int N = data.Length;
for (j=N-1; j>0; j--) {
for (i=0; i<j; i++) {
if (data [i] > data [i + 1])
exchange (data, i, i + 1);
}
}
}
``` |

The Selection Sort algorithm works to minimize the amount of data movement,
hence the number of `exchange()` calls.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
public static int IntArrayMin (int[] data, int start)
{
int minPos = start;
for (int pos=start+1; pos < data.Length; pos++)
if (data [pos] < data [minPos])
minPos = pos;
return minPos;
}
public static void IntArraySelectionSort (int[] data)
{
int i;
int N = data.Length;
for (i=0; i < N-1; i++) {
int k = IntArrayMin (data, i);
if (i != k)
exchange (data, i, k);
}
}
``` |

It’s a remarkably simple algorithm to explain. As shown in the code, the
actual sorting is done by a function, `IntArraySelectionSort()`, which
takes an array of data as its only parameter, like Bubble sort.
The way Selection Sort works is as follows:

- An outer loop visits each item in the array to find out whether it is the minimum of all the elements after it. If it is not the minimum, it is going to be swapped with whatever item in the rest of the array is the minimum.
- We use a helper function,
`IntArrayMin()`to find the position of the minimum value in the rest of the array. This function has a parameter,`start`to indicate where we wish to begin the search. So as you can see from the loop in`IntArraySelectionSort()`, when we are looking at position`i`, we are searching for the minimum from position`i + 1`to the end of the array.

As a concrete example, if you have an array of 10 elements, this means that
`i` goes from 0 to 9. When we are looking at position 0, we check to find the
position of the minimum element in
positions 1..9. If the minimum is not already at position `i`, we swap the minimum into
place. Then
we consider `i=1` and look at positions 2..9. And so on.

We won’t do the full algorithmic analysis here. Selection Sort is interesting because
it does most of its work through *comparisons*, which is always the same regardless
of how the data are ordered, \(N \cdot \frac{N-1}{2}\), which is
\(O(N^2)\) The
number of *exchanges* is O(N ). The comparisons are a non-trivial cost, however, and do show
in our own performance experiments with randomly-generated data.

In the Insertion Sort algorithm, we build a sorted
list from the bottom of the array. We repeatedly insert the next element
into the sorted part of the array by sliding it down (using our familiar
`exchange()` method) to its proper position.

This will require as many exchanges as Bubble Sort, since only one inversion is removed per exchange. So Insertion Sort also requires \(O(N^2)\) exchanges. On average Insertion Sort requires only half as many comparisons as Bubble Sort, since the average distance an element must move for random input is one-half the length of the sorted portion.

1 2 3 4 5 6 7 8 9 10 11 | ```
public static void IntArrayInsertionSort (int[] data)
{
int i, j;
int N = data.Length;
for (j=1; j<N; j++) {
for (i=j; i>0 && data[i] < data[i-1]; i--) {
exchange (data, i, i - 1);
}
}
}
``` |

Shell Sort is basically a trick to make Insertion Sort run faster. If
you take a quick glance at the code and look beyond the presence of
two additional *outer loops*, you’ll notice that the code looks very similar.

Since Insertion Sort removes one inversion per exchange, it cannot run faster than the number of inversions in the data, which in worst case is \(O(N^2)\). Of course, it can’t run faster than N, either, because it must look at each element, whether or not the element is out of position. We can’t do any thing about the lower bound O(N), but we can do something about the number of steps to remove inversions.

The trick in Shell Sort is to start off swapping elements that are further apart. While this may remove only one inversion sometimes, often many more inversions are removed with intervening elements. Shell Sort considers the subsequences of elements spaced k elements apart. There are k such sequences starting at positions 0 through k-1 in the array. In these sorts, elements k positions apart are exchanged, removing between 1 and 2(k-1)+1 inversions.

Swapping elements far apart is not sufficient, generally, so a Shell Sort will do several passes with decreasing values of k, ending with k=1. The following examples experiment with different series of values of k.

In this first example, we sort all subsequences of elements 8 apart,
then 4, 2, and 1. Please note that these intervals are to show how the
method works–not how the method works *best*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
public static void IntArrayShellSort (int[] data, int[] intervals)
{
int i, j, k, m;
int N = data.Length;
// The intervals for the shell sort must be sorted, ascending
for (k=intervals.Length-1; k>=0; k--) {
int interval = intervals [k];
for (m=0; m<interval; m++) {
for (j=m+interval; j<N; j+=interval) {
for (i=j; i>=interval && data[i]<data[i-interval]; i-=interval) {
exchange (data, i, i - interval);
}
}
}
}
}
``` |

1 2 3 4 5 | ```
public static void IntArrayShellSortNaive (int[] data)
{
int[] intervals = { 1, 2, 4, 8 };
IntArrayShellSort (data, intervals);
}
``` |

In general, shell sort with sequences of jump sizes that
are powers of one another doesn’t
do as well as one where most jump sizes are not multiples of others,
mixing up the data more.
In addition, the number of intervals must be increased as the size of the array to
be sorted increases, which explains why we allow an *arbitrary* array of intervals
to be specified.

Without too much explanation, we show how you can choose the intervals differently
in an *improved* shell sort, where the intervals have been chosen so as not to be
multiples of one another.

Donald Knuth has suggested a couple of methods for computing the intervals:

\[h_0 = 1\]\[h_{k+1} = 3 h_k + 1\]\[t = \lfloor log_3 n \rfloor - 1\]

Here we are using notation for the *floor* function
\(\lfloor x \rfloor\) means the largest integer \(\le x\).

This results in a sequence 1, 4, 13, 40, 121.... You stop computing values in the sequence when \(t = log_3 n - 1\). (So for n=50,000, you should have about 9-10 intervals.)

For completeness, we note that \(log_3 n\) must be sufficiently large (and > 2)
for this method to work. Our code ensures this by taking the *maximum* of
\(log_3 n\) and 1.

Knuth also suggests:

\[h_0 = 1\]\[h_{k+1} = 2 h_k + 1\]\[t = \lfloor log_2 n \rfloor - 1\]

This results in a sequence 1, 3, 7, 15, 31....

Here is the improvement to our naive method that dynamically calculates the intervals based on the first suggestion of Knuth:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
static int[] GenerateIntervals (int n)
{
if (n < 2) { // no sorting will be needed
return new int[0];
}
int t = Math.Max (1, (int)Math.Log (n, 3) - 1);
int[] intervals = new int[t];
intervals [0] = 1;
for (int i=1; i < t; i++)
intervals [i] = 3 * intervals [i - 1] + 1;
return intervals;
}
public static void IntArrayShellSortBetter (int[] data)
{
int[] intervals = GenerateIntervals (data.Length);
IntArrayShellSort (data, intervals);
}
``` |

Shell sort is a complex sorting algorithm to make “work well”, which is why it is not seen often in practice. It is, however, making a bit of a comeback in embedded systems.

We nevertheless think it is a very cool algorithm to have heard of as a computer science student and think it has promise in a number of situations, especially in systems where there are limits on available memory (e.g. embedded systems).

This sort is a more advanced example that uses *recursion*. We’re going to explain it
elsewhere in our notes/book.

Quicksort is a rather interesting case. It is often perceived to be one of the best sorting algorithms but, in practice, has a worst case performance also on the order \(O(N ^2)\). When the data are randomly sorted (as in our experiments) it does better at \(O(N \log N)\).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
public static void IntArrayQuickSort (int[] data, int l, int r)
{
int i, j;
int x;
i = l;
j = r;
x = data [(l + r) / 2]; /* find pivot item */
while (true) {
while (data[i] < x)
i++;
while (x < data[j])
j--;
if (i <= j) {
exchange (data, i, j);
i++;
j--;
}
if (i > j)
break;
}
if (l < j)
IntArrayQuickSort (data, l, j);
if (i < r)
IntArrayQuickSort (data, i, r);
}
public static void IntArrayQuickSort (int[] data)
{
IntArrayQuickSort (data, 0, data.Length - 1);
}
``` |

We’ll have a bit more to say about this algorithm in our discussion of recursion.

Now it is time to talk about how we are going to check the performance in a real-world situation. We’re going to start by modeling the situation here the data are in random order.

The following code generates a random array:

1 2 3 4 5 6 | ```
public static void IntArrayGenerate (int[] data, int randomSeed)
{
Random r = new Random (randomSeed);
for (int i=0; i < data.Length; i++)
data [i] = r.Next ();
}
``` |

There are a few things to note in this code:

- We use the random number generator option to include a
*seed*. Random numbers aren’t truly random. The particular sequence is just determined by a seed. The simplest way to create a Random object uses a seed taken from the system clock. - Because the sorting algorithms
*modify*the data that are passed to it, we need to have a way of regenerating the sequence. (We could also copy the data, but it is kind of a waste of memory.) - In order to regenerate a particular example, we actually need the random sequence to be consistent, so we know that each of the sorting algorithms is being tested using the same random data. Hence we specify the same seed each time.

In this code, we are actually beginning to make use of *classes* that are part of the
.Net framework/library.

We need the ability to time the various sorting algorithms. Luckily, .Net gives us a
way of doing so through its `Stopwatch` class. This class supports methods that you
would expect if you’ve ever used a stopwatch (the kind found in sports):

- Reset: Resets the elapsed time to zero. We need this so we can use the same Stopwatch for each sorting algorithm.
- Start: Starts the stopwatch. Will keep recording time until stopped.
- Stop: Stops the stopwatch.
- ElapsedMilliseconds: Not really a method but a property (like a variable). We’ll use this to get the total time that has elapsed between pairs of Start/Stop events in milliseconds.

So let’s take a look at how we compare the sorting algorithms by looking at the `Main()`
method’s code. As this code is fairly lengthy, we’re going to look at parts of it. The
`Main()` method should be thought of as an *experiment* that tests the performance
of each of the sorting algorithms.

1 2 3 4 | ```
int arraySize;
int randomSeed;
Stopwatch watch = new Stopwatch ();
double elapsedTime; // time in second, accurate to about millseconds
``` |

The variables declared here are to set up the apparatus:

`arraySize`: The size of the array where we wish to test the performance. We will use this to create an array with`arraySize`random values.`randomSeed`: This allows the user to vary the seed that is used to create the random array. We often want to do this to determine whether our performance results are stable when run a large number of times with different distributions. We won’t go into too much detail here but consider it an important part of building good performance benchmarks.`watch`: The stopwatch object we’re using to do the timings of all experiments.

1 2 3 4 5 6 7 8 | ```
if (args.Length < 2) {
arraySize = Input.InputInt("Please enter desired array size: ");
randomSeed = Input.InputInt(
"Please enter an initial random seed value: ");
} else {
arraySize = int.Parse (args [0]);
randomSeed = int.Parse (args [1]);
}
``` |

This code is designed so we can accept the parameters `arraySize` and `randomSeed`
from the command line or by prompting the user. When programmers design benchmarks, they
often try to make it possible to run them with minimal user interaction. For the purposes
of teaching, we wanted to make it possible to run it with or without command-line parameters.

1 2 3 4 5 6 7 | ```
IntArrayGenerate (data, randomSeed);
watch.Reset ();
watch.Start ();
IntArrayBubbleSort (data); // the other experiments call a different method
watch.Stop ();
elapsedTime = watch.ElapsedMilliseconds/1000.0;
Console.WriteLine ("Bubble Sort: {0:F3}", elapsedTime);
``` |

This code fragment is actually replicated a few times in the actual `Main()` method (to
run each of the different sorting algorithms). Essentially, we do the following for each
of the sorting algorithms we want to benchmark:

- Create the random array of data.
- Reset the Stopwatch object to zero.
- Start the Stopwatch.
- Run the sorting algorithm of interest (here
`IntArrayBubbleSort()`). In the rest of the`Main()`code, we change this line to call the function for each of the other sorting algorithms. - Stop the Stopwatch and get the elapsed time (watch.Elapsed).
- Print the performance results.

When you get `watch.ElapsedMilliseconds`, this gives you an integer (long) number of
milliseconds (thousandths of a second).

If you already have performed a checkout of our entire project at
Bitbucket, you can find this code in the `projects/Arrays/Sorting`
folder (and open the solution `Sorting.sln` in MonoDevelop or Visual
Studio).

You can also view the full source code in our [SortingFolder].

Here’s the output of a trial run on one of our computers. The results will vary depending on your computer’s CPU, among other factors.

```
bin/Debug$ mono Sorting.exe 1000 12
Quick Sort: 0.000
Naive Shell Sort: 0.000
Better Shell Sort: 0.000
Insertion Sort: 0.001
Selection Sort: 0.002
Bubble Sort: 0.003
bin/Debug$ mono Sorting.exe 1000 55
Quick Sort: 0.000
Naive Shell Sort: 0.000
Better Shell Sort: 0.000
Insertion Sort: 0.001
Selection Sort: 0.002
Bubble Sort: 0.003
bin/Debug$ mono Sorting.exe 10000 2
Quick Sort: 0.001
Naive Shell Sort: 0.019
Better Shell Sort: 0.002
Insertion Sort: 0.134
Selection Sort: 0.174
Bubble Sort: 0.321
bin/Debug$ mono Sorting.exe 50000 2
Quick Sort: 0.006
Naive Shell Sort: 0.441
Better Shell Sort: 0.015
Insertion Sort: 3.239
Selection Sort: 4.172
Bubble Sort: 8.028
bin/Debug$ mono Sorting.exe 100000 2
Quick Sort: 0.014
Naive Shell Sort: 1.794
Better Shell Sort: 0.034
Insertion Sort: 13.158
Selection Sort: 16.736
Bubble Sort: 31.334
```

At least based on randomly-generated arrays, the performance can be summarized as follows:

- Bubble Sort is rather unimpressive as expected. In fact, this algorithm is never used in practice but is of historical interest. Like the brute-force style of searching, it does way too much work to come up with the right answer!
- Selection Sort and Insertion Sort are also rather unimpressive on their own. Even though Selection Sort can in theory do a lot less data movement, it must make a large number of comparisons to find the minimum value to be moved. Again it is way too much work. Insertion Sort, while unimpressive, fares a bit better and turns out to be a nice building block (if modified) for the Shell Sort. Varying the interval size drastically reduces the amount of data movement (and the distance it has to move).
- Shell Sort does rather well, especially when we pick the right intervals. In practice, the intervals also need to be adjusted based on the size of the array, which is what we do as larger array sizes are considered. This is no trivial task but a great deal of work has already been done in the past to determine functions that generate good intervals.
- The Quicksort is generally fastest. It is by far the
most commonly used sorting algorithm. Yet there are signs that Shell sort
is making a comeback in embedded systems, because it concise to code
and is still quite fast. See
[WikipediaShellSort], where it is mentioned that
the [uClibc] library makes use of Shell sort in its
`qsort()`implementation, rather than implementing the library sort with the more common quicksort.

[WirthADP] | Niklaus Wirth, Algorithms + Data Structures = Programs, Prentice Hall, 1976. |

[WikipediaShellSort] | http://en.wikipedia.org/wiki/Shellsort |

[uClibc] | http://en.wikipedia.org/wiki/UClibc |

[TCSortingJava] | http://tools-of-computing.com/tc/CS/Sorts/SortAlgorithms.htm |

[SortingFolder] | https://bitbucket.org/gkthiruvathukal/introcs-csharp/src/d82c38851f6a/projects/Arrays/Sorting/Main.cs |