# An Introduction To Binary Search Algorithm

New to search algorithms? Don’t know where to start? Lucky for you, algo.monster has a good explanation on search algorithms such as binary search, linear search, and other algorithms. In today’s share, it will introduce this practical data searching technique. Check out this article to find out the detailed information.

**What is binary search?**

Binary search is a useful tool for finding a particular element in a sorted dataset. It is a very efficient algorithm for searching large amounts of values. The size of an array can be divided in half using a special method called arithmetic algebra.

In addition, the time required to complete the operation is logarithmic. In other words, the worst-case scenario for an array of n elements is guaranteed to be in less than log(n). Hence, binary search is a great tool for reducing search complexity.

**What can a binary search algorithm do?**

The binary search is a good choice for large arrays. By using this method, you can easily find a name in a database among millions of names. Assume that the names are alphabetically arranged in advance. Moreover, you can also use it for smaller data. Whether you’re searching for a name or a number, you can always be sure that binary search is an excellent choice for the job. So, don’t wait any longer! This method is the best option.

**How does binary search work?**

A binary search is a quick way to locate a target value. It compares sorted data on an ordering principle. Using it, the median value is the first element. And the target is the second element. The next element is the last element. Usually, the two elements are related in descending order. When a value is found in an array, it will be the first element to be compared.

In other words, it compares the middle-most element of the array with the target value. The middle-most item must be greater than the item to return a valid result. Then, it searches the items left and right of the middle-most one until the sub-array becomes zero. This process is repeated until all elements in the array are equal. Then, binary search is an excellent choice for complex data analysis.

**When can we use binary search?**

The most common situation when you would use binary search is to find an item in an array of numbers. If the list has many entries, using this algorithm is a better option than trying to sort each one by hand. This approach is faster and more accurate, but it also requires a single sort. However, it may not always be the best option. In this case, you could choose to use the other method, which is known as linear search.

Then, binary search comes into play. If the query is a yes-or-no answer, it will return the first answer with the second result. Generally, it is used for lists and arrays that are sorted. The time complexity of binary search is O(log N), which makes it a fast sorting algorithm.

**Why is this method fast?**

In binary, the elements are compared using a divide-and-conquer approach. If there is a match between the two elements, the result will be the same. Unlike equality comparisons, a binary search is faster and more accurate than the average algorithm.

Binary search is extremely efficient because it reduces the complexity of the search by a factor of two. Also, it reduces the time to search a large array to O(log n).

Basically, during the process of searching, this method downsizes the array by half each time. So, the number of elements you need to compare changes like this: 1 then 1/2, then 1/4, and so on. Thus, it saves much time and effort. That is why it is often fast in searching a certain element.

For example, it can locate an item within 1 million elements with just 20 comparisons. Also, unlike other types of search approaches, the binary method is more accurate and has a wider range of uses.

**Simple examples of using a binary search algorithm**

Using a paper dictionary to find a new word:

You will open the dictionary approximately in the middle part. Then compare the first letter of the word with the page you open. For example, your word begins with the letter K. And where you open lies letter L. So, you will know you have to keep searching in the left half.

The game of guessing a number:

Say from 1 to 100, guess what number is written down. Obviously, starting the guess with 50 is going to save much time. Then, you know to move to the higher half or to the lower numbers.

**Binary search tree (BST)**

Using binary search, you can view a binary tree. The top element represents the root node. The bottom half represents the left child node. Then, the middle element of the upper half is the right child node. The rest of the tree is built the same way. Depending on the target value, the subtrees are traversed to the left or the right. This allows the code to be optimized by hiding the long computation in another function.

How does the tree work? It compares the size of the item to the size of the root element. If the item is smaller than the root, it moves to the left subtree. If not, it moves to the right subtree. This process is repeated until the item is found. Otherwise, it returns NULL. This algorithm is generally efficient, but sometimes can be slow. However, it is still very useful for a variety of tasks.

**Conclusion **

Binary search is an efficient algorithm for finding information. Basically, the algorithm looks for the first element in the middle. Often, the algorithm compares two elements and returns the one that matches the first. If the elements of the data structure are close, binary search is faster. In addition, it allows you to search for duplicates in a database. So, a better choice for large datasets would be a fast recursive function.

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