To find the minimum value into an array of items itsn’t difficult. There are not many options to do that. The most natural approach is to take the first item and to compare its value against the values of all other elements. Once we find a smaller element we continue the comparisons with its value. Finally we find the minimum.

First thing to note is that we pass through the array with n steps and we need exactly n-1 comparisons. It’s clear that this is the optimal solution, because we must check all the elements. For sure we can’t be sure that we’ve found the minimum (maximum) value without checking every single value.

Overview

The algorithm above is very simple and we’re sure that it is optimal. Obviously finding both the minimum and the maximum value is O(n) with n-1 comparisons, but what about combining these tasks into one single pass.

Since they both are O(n) and need n-1 comparisons it’s natural to think that combining the two tasks will be O(n) and 2n – 2 comparisons. However we can reduce the number of comparisons!

Instead of taking only one item from the array and comparing it against the minimum and maximum we can take a pair of items at each step. Thus we can first compare them and then compare the smaller value with the currently smallest value and the greater item with the currently greatest value. This will make only 3 comparisons instead of 4.

Implementation

It’s easy to implement the minimum (maximum) algorithms with a single loop.

The implementation of finding the maximum is practically the same.

Simply merging these two functions will lead us to a O(n) with 2n – 2 comparisons solution.

However we can take a pair of items on each step. First we’ll compare the items from that pair and after that we’ll compare them respectively with the minimum and the maximum value. Because on each iteration we jump by two items, in case the number of array items is even we must check for the array boundaries. This can be overcome by adding a sentinel. Thus the array items are always odd, but this will lead us to a “extra” memory solution.

Sentinel

Without sentinel

Complexity

The complexity of finding both minimum and maximum is O(n). Even after combining the both algorithms in one single pass the complexity remains O(n). However in the second case we can reduce the number of comparisons to 3 * ceil(n/2) instead of 2n – 2!

Application

This algorithm can be applied in various fields of the computer science, since its nature is so basic. However there are two reasons why this approach is so important.

First we can see how by combining two “algorithms” doesn’t mean that we combine their complexities or the number of operations. With a clever trick and with the observation that the two operations are related (minimum and maximum) we can reduce the number of comparisons.

In the other hand we see how using a sentinel can be very handy and can spare us some comparisons, just like the sequential search.

Sorted data can dramatically change the speed of our program, therefore sorting algorithms are something quite special in computer science. For instance searching in a sorted list is faster than searching in an unordered list.

There are two main approaches in sorting – by comparing the elements and without comparing them. A typical algorithm from the first group is insertion sort. This algorithm is very simple and very intuitive to implement, but unfortunately it is not so effective compared to other sorting algorithms as quicksort and merge sort. Indeed insertion sort is useful for small sets of data with no more than about 20 items.

Insertion sort it is very intuitive method of sorting items and we often use it when we play card games. In this case the player often gets an unordered set of playing cards and intuitively starts to sort it. First by taking a card, making some comparisons and then putting the card on the right position.

So let’s say we have an array of data. In the first step the array is unordered, but we can say that it consists of two sub-sets: sorted and unordered, where on the first step the only item in the sorted sub-set is its first item. If the length of the array is n the algorithm is considered completed in n-1 steps. On each step our sorted subset is growing with one item. The thing is that we take the first item from the unordered sub-set and with some comparisons we put it into its place in the sorted sub-set, like on the diagram bellow.

Implementation

Here’s a quick implementation of insertion sort in PHP. The good thing is that it is easy to implement, but there are bad news too – insertion sort is slow and it is ineffective for large data sets.

$data = array(4, 2, 4, 1, 2, 6, 8, 19, 3);

function insertion_sort(&$arr)
{
	$len = count($arr);
	
	for ($i = 1; $i < $len; $i++) {
		$tmp = $arr[$i];
		$j = $i;
		
		while (($j >= 0) && ($arr[$j-1] > $tmp)) {
			$arr[$j] = $arr[$j-1];
			$j--;
		}
		$arr[$j] = $tmp;
	}
}

We can improve this code a little by using a sentinel, just like the sequential search, in order to remove one of the comparisons.

$data = array(4, 2, 4, 1, 2, 6, 8, 19, 3);

function insertion_sort_sentinel(&$arr)
{
	$len = count($arr);
	array_unshift(&$arr, -1);
	
	for ($i = 1; $i < $len+1; $i++) {
		$tmp = $arr[$i];
		$j = $i;
		
		while ($arr[$j-1] > $tmp) {
			$arr[$j] = $arr[$j-1];
			$j--;
		}
		$arr[$j] = $tmp;
	}
	array_shift(&$arr); // remove the sentinel
}

Just because we use searching the right position in an ordered array we can use binary search in order to improve even more the algorithm above. Unfortunately this doesn’t improve so much the general efficiency of this algorithm.

Complexity

As I said this algorithm is not so effective. Its complexity is O(n²) which is far worse than the O(n*log(n)) of quicksort, as you can see on the diagram bellow.

Application

This algorithm is useful for small sets of data and even if it doesn’t look like the most effective sorting algorithm, insertion sort can be useful for some reasons. First of all it is easy to implement, but it also does not require additional memory and it can be fast if the data is almost nearly sorted, which is great.

Relative encoding is another data compression algorithm. While run-length encoding, bitmap encoding and diagram and pattern substitution were trying to reduce repeating data, with relative encoding the goal is a bit different. Indeed run-length encoding was searching for long runs of repeating elements, while pattern substitution and bitmap encoding were trying to “map” where the repetitions happen to occur.

The only problem with these algorithms is that not always the input stream of data is constructed out of repeating elements. It is clear that if the input stream contains many repeating elements there must be some way of reducing them. However that doesn’t mean that we cannot compress data if there are no repetitions. It all depends on the data. Let’s say we have the following stream to compress.

1, 2, 3, 4, 5, 6, 7

We can hardly imagine how this stream of data can be compressed. The same problem may occur when trying to compress the alphabet. Indeed the alphabet letters the very base of the words so it is the minimal part for word construction and it’s hard to compress them.

Fortunately this isn’t true always. An algorithm that tryies to deal with non repeating data is relative encoding. Let’s see the following input stream – years from a given decade (the 90’s).

1991,1991,1999,1998,1991,1993,1992,1992

Here we have 39 characters and we can reduce them. A natural approach is to remove the leading “19” as we humans often do.

91,91,99,98,91,93,92,92

Now we have a shorter string, but we can go even further with keeping only the first year. All other years will as relative to this year.

91,0,8,7,0,2,1,1

Now the volume of transferred data is reduced a lot (from 39 to 16 – more than 50%). However there are some questions we need to answer first, because the stream wont be always formatted in such pretty way. How about the next character stream?

91,94,95,95,98,100,101,102,105,110

We see that the value 100 is somehow in the middle of the interval and it is handy to use it as a base value for the relative encoding. Thus the stream above will become:

-9,-6,-5,-5,-2,100,1,2,5,10

The problem is that we can’t decide which value will be the base value so easily. What if the data was dispersed in a different way.

96,97,98,99,100,101,102,103,999,1000,1001,1002

Now the value of “100” isn’t useful, because compressing the stream will get something like this:

-4,-3,-2,-1,100,1,2,3,899,900,901,902

To group the relative values around “some” base values will be far more handy.

(-4,-3,-2,-1,100,1,2,3)(-1,1000,1,2)

However to decide which value will be the base value isn’t that easy. Also the encoding format is not so trivial. In the other hand this type of encoding can be useful in som specific cases as we can see bellow.

Implementation

The implementation of this algorithm depends on the specific task and the format of the data stream. Assuming that we’ve to transfer the stream of years in JSON from a web server to a browser, here’s a short PHP snippet.

// JSON: [1991,1991,1999,1998,1999,1998,1995,1997,1994,1993]
$years = array(1991,1991,1999,1998,1999,1998,1995,1997,1994,1993);

function relative_encoding($input)
{
	$output = array();
	$inputLength = count($input);
	
	$base = $input[0];
	
	$output[] = $base;
	
	for ($i = 1; $i < $inputLength; $i++) {
		$output[] = $input[$i] - $base;
	}
	
	return $output;
}

// JSON: [1991,0,8,7,8,7,4,6,3,2]
echo json_encode(relative_encoding($years));

Application

This algorithm may be very useful in many cases, but here’s one of them. There are plenty of map applications around the web. Some products as Google Maps, Yahoo! Maps, Bing Maps are quite famous, while there are very useful open source projects as OpenStreetMap. The web sites using these apps are thousands.

A typical use case is to transfer lots of Geo coordinates from web server to a browser using JSON. Indeed any GEO point on Earth is relative to the point (0,0), which is located near the west coast of Africa, however on large zoom levels, when there are tons of markers we can transfer the information with relative encoding.

For instance the following diagram shows San Francisco with some markers on it. Their coordinates are be relative to the point (0,0) on Earth.

Far more useful may be to encode those markers, relative to the center of the city, thus we can save some space.

However this type of compression can be tricky, for example when dragging the map and updating the marker array. In the other hand we must group markers if we have to load more than one city. That’s why we must be careful when implementing it. But in the other hand it can be very useful - for instance on initial load of the map we can reduce data and speed up the load time.

The thing is that with relative encoding we can save only changes to base value (data) - something like version control systems and thus reducing data transfer and load. Here's a graphical example. In the first case on the diagram bellow we can see that each item is stored on its own. It doesn't depend on the adjacent items and it can be completely independent of them.

However we can keep full info only for the first item and any other item will be relative to it, like on the diagram bellow.