Constructing a linked list is a fairly simple task. Linked lists are a linear structure and the items are located one after another, each pointing to its predecessor and its successor. Almost every operation is easy to code in few lines and doesn’t require advanced skills. Operations like insert, delete, etc. over linked lists are performed in a linear time. Of course on small data sets this works fine, but as the data grows these operations, especially the search operation becomes too slow.

Indeed searching in a linked list has a linear complexity and in the worst case we must go through the entire list in order to find the desired element. The worst case is when the item doesn’t belong to the list and we must check every single item of the list even the last one without success. This approach seems much like the sequential search over arrays. Of course this is bad when we talk about large data sets.

In terms of arrays, we could perform binary search and go directly in the middle of the array, then jump back or forward. That is because we can access array items directly using their index. However as we saw the linked lists unlike arrays can’t benefit of a direct access and we must go item by item.

Because of this natural problem of linked lists searching is slow and obviously we can’t make it better. The only way to improve searching over dynamic data structures is to use different data structure.

The tree is a data structure where each item, except of keeping some data, keeps a reference (pointer) to its children and its parent.

Of course if the item doesn’t have children, they are NIL, then this is considered a leaf in the tree terminology. In the other hand if the item doesn’t have parent item it is considered the root.

If there is no item in the tree the tree is considered empty.

In these terms only the root has no parent, and each item can have as many children as possible. Here are some trees in form of a diagrams.

If we’re looking at the root of the tree we can assume there are two sub-trees – one left and one right. However if we isolate only one of these sub-trees we can again think of it as a tree and assume that it has one left and one right sub-trees and go recursively with this definition.

Overview

A binary tree is a tree where each item can have at most two children.

Binary trees are especially important because they can contain ordered data in a specific manner. Building a binary tree isn’t difficult at all and it’s very similar to building a linked list.
However a binary tree isn’t more successful in searching than any other tree or data structure. If the items aren’t placed in a specific order we must go through the entire tree in order to find the searched item. This isn’t a great optimization, so we must put an order in it to improve the searching process.

Binary Search Tree

The binary search tree is a specific kind of binary tree, where the each item keeps greater elements on the right, while the smaller items are on the left.

Constructing a binary search tree is easy, because we can go for inserting each item only by comparing it with the root and decide where to go (left or right) based on its value.

Implementation

The following code in PHP describes the basic principles of a binary search tree.

class Node
{
	public $parent = null;
	public $left = null;
	public $right = null;
	public $data = null;
	
	public function __construct($data)
	{
		$this->data = $data;
	}
	
	public function __toString()
	{
		return $this->data;
	}
}

class BinaryTree
{
	protected $_root = null;
	
	protected function _insert(&$new, &$node)
	{
		if ($node == null) {
			$node = $new;
			return;
		}
		
		if ($new->data <= $node->data) {
			if ($node->left == null) {
				$node->left = $new;
				$new->parent = $node;
			} else {
				$this->_insert($new, $node->left);
			}
		} else {
			if ($node->right == null) {
				$node->right = $new;
				$new->parent = $node;
			} else {
				$this->_insert($new, $node->right);
			}
		}		
	}
	
	protected function _search(&$target, &$node)
	{
		if ($target == $node) {
			return 1;
		} else if ($target->data > $node->data && isset($node->right)) {
			return $this->_search($target, $node->right);
		} else if ($target->data <= $node->data && isset($node->left)) {
			return $this->_search($target, $node->left);
		}
		
		return 0;
	}
	
	public function insert($node)
	{
		$this->_insert($node, $this->_root);
	}
	
	public function search($item) 
	{
		return $this->_search($item, $this->_root);
	}
}

$a = new Node(3);
$b = new Node(2);
$c = new Node(4);
$d = new Node(7);
$e = new Node(6);

$t = new BinaryTree();

$t->insert($a);
$t->insert($b);
$t->insert($c);
$t->insert($d);
$t->insert($e);

echo $t->search($e);

Search Complexity

Searching in binary search trees is supposed to be faster than searching into linked list. However the searching process in a BST can be very fast, but also can be as slow as on linked list. That is because depending on the input of items they can be placed only on the one side of the root.

That makes the worst-case searching as slow as on linked list which is linear O(n). However if the tree is somehow balanced we can search very quickly with O(log(n)) time.

Further Optimization

We now see how ineffective binary search trees can be, so the only thing we must care is how to keep them balanced, so the search will be faster. The answer is to maintain (during insertion) a balanced binary search tree, which is another very handy data structure.

A balanced binary search tree, or only balanced tree, is a data structure where the height of left and the right sub-trees can vary by one level at most.

Application

Binary search trees are easy to build and maintain. The great thing is that if the data is well balanced they can be very useful for searching. The only problem is that these structures can be ineffective depending on the insertion order. However if we are somehow sure that the items aren’t ordered on the input, we may expect some optimized searching compared to a linked list. Compared to balanced binary search trees, BST require much less time to build and maintain (insert, delete).

Trees are very useful when working with graphs. Actually one of the very common tasks is walking through the entire tree, which can be done in several ways. First we can go to the left sub-tree, then the root and then the right sub-tree. Or right-root-left. Or root-left-right.

However we can go in depth first often called depth-first-search or a breadth-first-search.

These two methods are designed to walk through the items in a specific order, which is very handy for some specific tasks – at least each tree is also a graph.

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.

String matching is something crucial for database development and text processing software. Fortunately every modern programming language and library is full of functions for string processing that help us in our everyday work. However is great to understand their principles.

String algorithms can be mainly divided into several categories. One of these categories is string matching.

When we come to string matching the most basic approach is what is known as brute force, which means just to check every single character from the text to match against the pattern. In general we have a text and a pattern (most commonly shorter than the text). What we need to do is to answer the question whether this pattern appears into the text.

Overview

The principles of brute force string matching are quite simple. We must check for a match between the first characters of the pattern with the first character of the text as on the picture bellow.

In case they match we move forward the second character of the pattern comparing it with the “next” character of the text, as on the picture bellow.

Just because we have found a match between the first character from the pattern with some character of the text, doesn’t mean that the pattern appears in the text. We must move forward to see whether the full pattern is contained into the text.

Implementation

Implementation of brute force string matching is easy and here we can see a short PHP example. The bad news is that naturally this algorithm is quite slow.

function sub_string($pattern, $subject) 
{
	$n = strlen($subject);
	$m = strlen($pattern);
	
	for ($i = 0; i < $n-$m; $i++) {
		$j = 0;
		while ($j < $m && $subject[$i+$j] == $pattern[$j]) {
			$j++;
		}
		if ($j == $m) return $i;
	}
	return -1;
}

echo sub_string('o wo', 'hello world!');

Complexity

As I said this algorithm is slow. Actually every algorithm that contains “brute force” in its name is slow, but to show how slow is string matching I can say that its complexity is O(n.m). Here n is the length of the text, while m is the length of the pattern.

In case we fix the length of the text and test against variable length of the pattern, again we get rapidly growing function.

Application

Brute force string matching can be very ineffective, but it can also be very handy in some cases. Just like the sequential search.

It can be very useful ...

1. Doesn't require pre-processing of the text - Indeed if we search the text only once we don't need to pre-process it. Most of the algorithms for string matching need to build an index of the text in order to search quickly. This is great when you've to search more than once into a text, but if you do only once, perhaps (for short texts) brute force matching is great!
2. Doesn't require additional space - Because brute force matching doesn't need pre-processing it also doesn't require more space, which is one cool feature of this algorithm
3. Can be quite effective for short texts and patterns

It can be ineffective ...

1. If we search more than once the text - As I said in the previous section if you perform the search more than once it's perhaps better to use another string matching algorithm that builds an index and it's faster.
2. It's slow - In general brute force algorithms are slow and brute force matching isn't an exception.

Final Words

String matching is something very special in software development and it is used in various cases, so every developer must be familiar with this topic.

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.

The binary search is perhaps the most famous and best suitable search algorithm for sorted arrays. Indeed when the array is sorted it is useless to check every single item against the desired value. Of course a better approach is to jump straight to the middle item of the array and if the item’s value is greater than the desired one, we can jump back again to the middle of the interval. Thus the new interval is half the size of the initial one.

If the searched value is greater than the one placed at the middle of the sorted array, we can jump forward. Again on each step the considered list is getting half as long as the list on the previous step, as shown on the image bellow.

Implementation

Here’s a sample implementation of this algorithm on PHP. Obviously the nature of this approach is guiding us to a recursive implementation, but as we know, sometimes recursion can be dangerous. That’s why here we can see either the recursive and iterative solution.

Recursive Binary Search

$list = array(0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144);
$x = 55;

function binary_search($x, $list, $left, $right) 
{
	if ($left > $right)
		return -1;
	
	$mid = ($left + $right) >> 1;

	if ($list[$mid] == $x) {
		return $mid;
	} elseif ($list[$mid] > $x) {
		return binary_search($x, $list, $left, $mid-1);
	} elseif ($list[$mid] < $x) {
		return binary_search($x, $list, $mid+1, $right);
	}
}

echo binary_search($x, $list, 0, count($list)-1);

Iterative Binary Search

$list = array(0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144);
$x = 55;

function iterative_binary_search($x, $list) 
{
	$left = 0;
	$right = count($list)-1;
	
	while ($left <= $right) {
		$mid = ($left + $right) >> 1;
		
		if ($list[$mid] == $x) {
			return $mid;
		} elseif ($list[$mid] > $x) {
			$right = $mid - 1;
		} elseif ($list[$mid] < $x) {
			$left = $mid + 1;
		}
	}
	
	return -1;
}

echo iterative_binary_search($x, $list);

Caution: Optimization

Most of the optimization techniques mentioned online recommend to replace the expensive operation of dividing by 2 with its bitwise equivalent (n >> 1) == n/2. That is not always true and it is very dependant from the programming language. Thus in PHP those operations are fairly similar as PHP is written in C. You’ve to be aware of the language specific features when optimizing code.

Fibonacci Search

Every developer has heard of Fibonacci and his sequence. The Fibonacci search algorithm is practically a variation of the binary search algorithm. In fact the only difference is that the binary search algorithm divides the list into two equal parts, while the Fibonacci search divides it in two but not equal parts. In fact sometimes it is faster to search if you divide the list by such non equal sub-lists. However the length of the sub-lists is not random.

It is clear that the ratio of any two consecutive numbers in the Fibonacci sequence is practically forming the golden ratio. This can lead us to another variation of Fibonacci and binary search - the golden section search. The only different thing is that you’ve to divide the length of the list in two parts exactly by the golden ratio.

The complexity both of the Fibonacci and the golden section search algorithm is identical with the complexity of the binary search. However these two algorithms are rarely used in practice. Also it is more difficult to implement these two algorithms than the binary search and their advantage depends on specifically dispersed data.

Complexity

The complexity of the binary search algorithm is intuitively clear - O(log(n)), which makes it far more effective than the sequential search.

Application

It is useless to mention examples of its use. This algorithm is easy to implement and in the same times it is very fast. Yes, indeed, this algorithm is only possible on sorted lists and this is a limitation. Also, as I said, compared to the jump search here we have more than one jump back in most of the cases, which sometimes can be more expensive than jump forward. However is this the fastest search algorithm? I’ll try to answer this question in my next article.

In my previous article I discussed how the sequential (linear) search can be used on an ordered lists, but then we were limited by the specific features of the given task. Obviously the sequential search on an ordered list is ineffective, because we consecutively check every one of its elements. Is there any way we can optimize this approach? Well, because we know that the list is sorted we can check some of its items, but not all of them. Thus when an item is checked, if it is less than the desired value, we can skip some of the following items of the list by jumping ahead and then check again. Now if the checked element is greater than the desired value, we can be sure that the desired value is hiding somewhere between the previously checked element and the currently checked element. If not, again we can jump ahead. Of course a good approach is to use a fixed step. Let’s say the list length is n and the step’s length is k. Basically we check list(0), then list(k-1), list(2k-1) etc. Once we find the interval where the value might be (m*k-1 < x <= (m+1)*k – 1), we can perform a sequential search between the last two checked positions. By choosing this approach we avoid a lot the weaknesses of the sequential search algorithm. Many comparisons from the sequential search here are eliminated.

How to choose the step’s length

We know that it is a good practice to use a fixed size step. Actually when the step is 1, the algorithm is the traditional sequential search. The question is what should be the length of the step and is there any relation between the length of the list (n) and the length of the step (k)? Indeed there is such a relation and often you can see sources directly saying that the best length k = √n. Why is that?

Well, in the worst case, we do n/k jumps and if the last checked value is greater than the desired one, we do at most k-1 comparisons more. This means n/k + k – 1 comparisons. Now the question is for what values of k this function reaches its minimum. For those of you who remember maths classes this can be found with the formula -n/(k^2) + 1 = 0. Now it’s clear that for k = √n the minimum of the function is reached.

Of course you don’t need to prove this every time you use this algorithm. Instead you can directly assign √n to be the step length. However it is good to be familiar with this approach when trying to optimize an algorithm.

Let’s cosider the following list: (0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610). Its length is 16. Jump search will find the value of 55 with the following steps.

Implementation

Let’s see an example of jump search, written in PHP.

$list = array();

for ($i = 0; $i < 1000; $i++) {
	$list[] = $i;
}

// now we have a sorted list: (0, 1, 2, 3, ..., 999)

function jump_search($x, $list)
{
	// calculate the step
	$len = count($list);
	$step = floor(sqrt($len));
	$prev = 0;
	
	while ($list[($step < $len ? $step : $len)] < $x) {
		$prev = $step;
		$step += floor(sqrt($len));
		
		if ($step >= $len) {
			return FALSE;
		}
	}
	
	while ($list[$prev] < $x) {
		$prev++;
		if ($prev == ($step < $len ? $step : $len)) {
			return FALSE;
		}
	}
	
	if ($list[$prev] == $x) {
		return $prev;
	}
	
	return FALSE;
}

echo (int)jump_search(674, $list);

Here we have a sorted list with 1000 elements that looks like this: (0, 1, 2, ..., 999). Obviously with sequential search we'll find the value of 674 with exactly on the 674-th iteration. Here, with jump search we can reach it on the 44-th iteration, and this shows us the advantage of jump search over the sequential search on ordered lists.

Further Optimization

Although all examples here deal with small lists in practice this is not always true. Sometimes the step itself can be a very large number, so once you know the interval where the desired value could be you can perform jump search again.

We saw that the best size of the step is √n, but it is not a good idea to start from the first element of the list just as we didn’t in the example above. A better option is to begin from kth item. Now we can improve the above solution.

Complexity

Obviously the complexity of the algorithm is O(√n), but once we know the interval where the value is we can improve it by applying jump search again. Indeed let’s say the list length is 1,000,000. The jump interval should be: √1000000=1000. As you can see again, you can use jump search with a new step √1000≈31. Every time we find the desired interval we can apply the jump search algorithm with a smaller step. Of course finally the step will be 1. In this case the complexity of the algorithm is no longer O(√n). Now its complexity is approaching logarithmic value. The problem is that the implementation of this approach is considered to be more difficult than the binary search, where the complexity is also O(log(n)).

Application

As almost every algorithm the jump search is very convinient for a certain kind of tasks. Yes, the binary search is easy to implement and its complexity is O(log(n)), but in case of a very large list the direct jump to the middle can be a bad idea. Then we should make a large step back if the searched value is placed at the beginning of the list.

Perhaps every one of us has performed some sort of a primitive jump search in his life without even knowing it. Do you remember cassette recorders? We used the "fast forward" key and periodically checked whether the tape was on our favorite song. Once we stopped at the middle of the song we used the "rewind" button to find exactly the beginning of the song.

This clumsy example can give us the answer of where jump search can be better than binary search. The advantage of jump search is that you need to jump back only once (in case of the basic implementation).

If jumping back takes you significantly more time than jumping forward then you should use this algorithm.

The expression “linear search in sorted lists” itself sounds strange. Why should we use this algorithm for sorted lists when there are lots of other algorithms that are far more effective? As I mentioned in

my previous post the sequential search is very ineffective in most of the cases and it is primary used for unordered lists. Indeed sometimes it is more useful first to sort the data and then use a faster algorithm like the binary search. On the other hand the analysis shows that for lists with less than ten items the linear search is much faster than the binary search. Although, for instance, binary search is more effective on sorted lists, sequential search can be a better solution in some specific cases with minor changes. The problem is that when developers hear the expression “sorted list” they directly choose an algorithm different from the linear search. Perhaps the problem lays in the way we understand what an ordered list is?

What is a sorted list?

We used to think that this list (1, 1, 2, 3, 5, 8, 13) is sorted. Actually we think so because it is … sorted, but the list (3, 13, 1, 3, 3.14, 1.5, -1) is also sorted, except that we don’t know how. Thus we can think that any array is sorted, although it is not always obvious how. There are basically two cases when sequential search can be very useful. First when the list is very short or when we know in advance that there are some values that are very frequently searched. Let’s say we have a very large list, with hundreds of thousands of items, but actually most of the searches in that list always find the same ten values. This additional information tells us that using a binary search will be quite ineffective in this case. A possible approach, of course, is to place those values at the front of the list and to perform a sequential search.

Self-Organization

Self-organization practically means that every time we search and find the desired value, we somehow change the list so the next search will be far more effective. There are basically two approaches to do that.

1. To move the item one position forward to the front of the list;
2. To move the item directly at the front of the list;

Of course it depends on your case which approach you’ll choose, but it is assumed that the second option, the one that we choose to move the item directly at the front of the list, is better. Indeed if we choose the first option and the list is (…, 24, 31) after constantly searching for those two values the array will be changing from (…, 24, 31) to (…, 31, 24) and once again to (…, 24, 31) and so on and so on. Thus a better solution is to move the desired item directly to the front of the list. Now if we look for the value of “5” in the list

(1, 2, 4, …, 5, …, 398) it will become (5, 1, 2, …, 398) after the value is found. By choosing this approach we can be sure that as the number of searches increases, the most frequently searched values are placed at the front of the list. Now the sequential search is quite a good solution! Here’s an example of sequential search from my previous article. The only change is that after we find the desired value we need to move it to the front of the list.

Application

Using sequential search in sorted lists can be very useful and fast, the only thing is that we need to know in advance that there are some values that are frequently searched. A typical example of this case is the contact list on your phone. Perhaps you have lots of names in there, but most of the times you search in it is to find your best friends’ and family phone numbers. That is why most of the cell phone manufacturers add to their phones the ability to predefine shortcut keys for the most frequently dialed numbers. Here’s another use case. Let’s say that we have the same scenario as in my previous

post, where username/name pairs are stored into a CSV file. We can fetch those values in a PHP array.

Every time a user enters the site we search for his name by his username and a welcome message is displayed. We know that some users enter the site very frequently while others do that once per month so we cannot only perform a sequential search but also we can use self-organization for the array and change the CSV file at the end.

The result is:

Hello, Darth Vader 
Found after 5 iterations!
Hello, Darth Vader
Found after 1 iterations!

Now every time Darth Vader tries to sign in, you won’t bother him to wait a lot for sure. However I bet nobody uses CSV files to store such information, but this is only an example.

This is the easiest to implement and the most frequently used search algorithm in practice. Unfortunately the sequential search is also the most ineffective searching algorithm. However, it is so commonly used that it is appropriate to consider several ways to optimize it. In general the sequential search, also called linear search, is the method of consecutively check every value in a list until we find the desired one.

Basic Implementation

The most natural approach is to loop through the list until we find the desired value. Here’s an implementation on PHP using FOR loop, something that can be easily written into any other computer language.

This is really the most ineffective implementation. There are two big mistakes in this code. First of all we calculate the length of the list on every iteration of the array, and secondly after we find the desired element, we don’t break the loop, but continue to loop through the array.

Yes, if the element is repeated without the “break” we can find its last occurrence, but if not the loop will iterate over the end of the array with no practical value.

Optimization of the forward sequential search

… and javascript:

Even with this little optimization the algorithm remains ineffective. As we can see, on every iteration we have two conditional expressions. First we check whether we’ve reached the end of the list, and then we check whether the current element equals to the searched element. So the question is can we reduce the number of the conditional expressions?

Searching in reverse order

Yes, we can reduce the number of comparison instructions from the forward approach of the linear search algorithm by using reverse order searching. Although it seems to be pretty much the same by reversing the order of the search we can discard one of the conditional expressions.

Note that we need to adjust index because of $index—expression.

Indeed here we have only one conditional expression, but the problem is that this implementation is correct ONLY when the element exists in the list, which is not always true. If the element doesn’t appears into the list, then this code can lead to an infinite loop. OK, but how can we stop the loop even when the list doesn’t contain the desired value? The answer is, by adding the searched value to the list.

Sentinel

The above problem can be solved by inserting the desired item as a sentinel value. Thus we’re sure that the list contains the value, so the loop will stop for sure even if at the beginning the value didn’t appear to be part of the list.

This approach can be used to overcome the problem of the reverse linear search approach from the previous section.

Complexity

As I said at the beginning of this post this is one of the most ineffective searching algorithms. Of course the best case is when the searched value is at the very beginning of the list. Thus on the first comparison we can find it. On the other hand the worst case is when the element is located at the very end of the list. Assuming that we don’t know where the element is and the possibility to be anywhere in the list is absolutely equal, then the complexity of this algorithm is O(n).

Different cases

We must remember, however, that the algorithm’s complexity can vary depending on whether the element occurs once.

Is it so ineffective?

Sequential search can be very slow compared to binary search on an ordered list. But actually this is not quite true. Sequential search can be faster than binary search for small arrays, but it is assumed that for n < 8 the sequential search is faster.

Application

The linear search is really very simple to implement and most web developers go to the forward implementation, which is the most ineffective one. On the other hand this algorithm is quite useful when we search in an unordered list. Yes, searching in an ordered list is something that can dramatically change the search algorithm. Actually searching and sorting algorithms are often used together.

A typical case is pulling something from a database, usually in form of a list and then search for some value in it. Unfortunately in most of the cases the database orders the returned result set and yet most of the developers perform a consecutive search over the list. Yet again when the list is ordered it is better to use binary search instead of sequential search.
Let’s say we have a CSV file containing the usernames and the names of our users.

Username,Name
jamesbond007,James Bond
jsmith,John Smith
...

Now we fetch these values into an array.

// work case
$arr = array(
    array('name' => 'James Bond', 'username' => 'jamesbond007'),
    array('name' => 'John Smith', 'username' => 'jsmith')
);

Now using sequential search …

// using a sentinel
$x = 'jsmith';
$arr[] = array('username' => $x, 'name' => '');
$index = 0;

while ($arr[$index++]['username'] != $x);

if ($index < count($arr)) {
    echo "Hello, {$arr[$index-1]['name']}";
} else {
    echo "Hi, guest!";
}