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Unlocking Efficiency: Understanding the Linear Search Algorithm

Unlocking Efficiency: Understanding the Linear Search Algorithm

The linear search algorithm might seem like a basic walk in the park but don’t be fooled. There’s more to it than meets the eye. Read the blog as we explore the hidden depths of linear search, its implementation, and its surprising limitations.

Table of Contents

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What is Linear Search Algorithm?

Linear search, also known as sequential search, is a straightforward method for finding a target value within a list or an array. It involves iterating through each element in the data structure, starting from the beginning and comparing each element with the target value until the match is found or the entire list is traversed. 

This search technique is simple to implement but becomes less efficient with larger datasets since it checks every element individually, resulting in a time complexity of O(n) in the worst-case scenario, where ‘n’ represents the number of elements in the list. Despite its simplicity, linear search is useful for smaller datasets or when the elements are not sorted, as it doesn’t require the list to be arranged in any specific order for successful execution. 

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How Does the Linear Search Algorithm Work?

The linear search algorithm works in a very efficient way. The steps of the working of the algorithm are given below: 

Step 1: Start: Begin with the first element (index 0) of the list/array.

Step 2: Compare: Check if the current element matches the target value you are searching for.

Step 3: Match Found: If there’s a match, the search ends, and the index where the match occurred is noted. If not, proceed to the next.

Step 4: Iterate: Move to the next element in the list and repeat steps 2 and 3 until the target value is found or until the end of the list is reached.

Step 5: End: If the target value is found, the search concludes by displaying the index where the match occurred. If the entire list is traversed and the value is not found, it indicates that the element is not present in the list.

Here’s a graphical illustration of the working of the linear search algorithm:

how does linear search algorithm work?

In this graphical illustration, we initially start with an input array and identify the key element we wish to find, which in this case is 41 denoted as ‘K’. As we traverse the array, we aim to locate the value 41. The first element in the list is 54; upon inspection, it does not match the target value, so we proceed to the next element. During the second iteration, after traversing, we encounter element 7, which does not equal 41, prompting us to move forward in the list.

This process continues until we find a match between the key value and an element in the array. After six passes, on the seventh iteration, we find that the key value matches an element present in the array. Consequently, no further traversal is required, as we’ve successfully located our key value within the array.

The practical implementation of the linear search algorithm in C programming language is as follows:

#include <stdio.h>
int linear_search(int array[], int n, int x) {
  // Going through array sequentially
  for (int i = 0; i < n; i++)
    if (array[i] == x)
      return i;
  return -1;
int main() {
  int array[] = {54, 7, 1, 69, 72, 83, 41, 93, 23};
  int x = 41;
  int n = sizeof(array) / sizeof(array[0]);
  int result = linear_search(array, n, x);
  (result == -1) ? printf("Element not found") : printf("Element found at index: %d", result);

Output: Element found at index: 6

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Complexity in data structures refers to the efficiency and performance of organizing and manipulating data. It consists of two key aspects: time complexity and space complexity. The goal is to design data structures and algorithms that optimize time and space, ensuring operations are executed swiftly and with minimal memory overhead. A well-designed data structure strikes a balance between these complexities, offering efficient storage, retrieval, and manipulation of data, regardless of the dataset’s size.

Time Complexity: Time complexity is a measure in computer science that evaluates the efficiency of an algorithm concerning the amount of time it takes to run based on the input size. It offers a glimpse into how an algorithm scales with larger inputs and provides an understanding of the resources required. 

The time complexity of the linear search is as follows: 

Best CaseO(1)
Average CaseO(n)
Worst CaseO(n)

Space Complexity: It is the measure of the amount of memory an algorithm requires to solve a problem, relative to the input size. It focuses on understanding how memory consumption grows as the input scales up. This analysis considers variables, data structures, auxiliary space, and other memory requirements used by the algorithm during its execution. 

The space complexity of linear search is as follows:

Space ComplexityO(1)

Understanding the advantages and disadvantages of algorithms is crucial for several reasons. It helps in choosing the most appropriate algorithm for a specific task or problem. By knowing their strengths and weaknesses, you can select an algorithm that aligns with the requirements of the problem, optimizing efficiency and performance.

Here are some of the pros and cons of linear search:


  • Simple implementation
  • Works on unsorted data
  • Applicable to a small list


  • Inefficiency with large datasets
  • Not suitable for sorted data
  • Limited application in complex or large-scale scenarios


In conclusion, while the linear search algorithm is straightforward to implement, its efficiency diminishes with larger data sets. Its simplicity makes it suitable for small lists or unsorted arrays, but for larger collections, more efficient algorithms like binary search are performed due to their quicker search times. Understanding the tradeoff helps in choosing the most appropriate algorithm based on the specific requirements of the task at hand. 

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When should I use a linear search?

Linear search is suitable for relatively small lists or instances where the data is unordered. It’s a handy algorithm for situations where the list isn’t too large and when efficiency isn’t a primary concern.

What are the limitations of linear search?

Its main drawback is its time complexity. In larger lists, especially those sorted, linear search can be slower compared to other search algorithms like binary search.

Is linear search efficient for large datasets?

Linear search becomes less efficient as the dataset grows larger. For significantly large or sorted datasets, algorithms with better time complexity, such as binary search, are preferable.

Can linear search work with different data types?

Yes, linear search can be applied to various data types, like integers, characters, strings, and more. It’s adaptable and can search for any comparable element within a list.

What is the worst-case time complexity of a linear search?

The worst-case time complexity of linear search is O(n), where ‘n’ is the number of elements in the list. This indicates that, in the worst scenario, the algorithm might need to traverse the entire list to find the target element.

How do I implement linear search in code?

Implementing linear search involves a simple loop that iterates through each element in the list, comparing it with the desired value, until a match is found or the end of the list is reached. You can use languages like Python, Java, C++, etc., to write this algorithm efficiently.

Can linear search be applied to both sorted and unsorted lists?

Yes, linear search can be used on both sorted and unsorted lists. However, its efficiency is more noticeable on unsorted data, where it simply checks each element one by one until a match is found.

Is linear search recursive or iterative?

Linear search is typically implemented iteratively, using loops to traverse through the list. While it can be done recursively, it’s less common due to the simplicity of the iterative approach.

Does linear search always start searching from the beginning of the list?

Yes, linear search begins its search from the first element of the list and proceeds sequentially until it either finds the target element or reaches the end of the list.

What happens if the element is not found in the list during a linear search?

If the element being searched for is not present in the list, the linear search will traverse through all the elements and reach the end of the list without finding a match. It will return a signal, often indicating that the element is not present in the list.

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About the Author

Senior Consultant Analytics & Data Science

Presenting Sahil Mattoo, a Senior Consultant Analytics & Data Science at Eli Lilly and Company is an accomplished professional with 14 years of experience across data science, analytics, and technical leadership domains, demonstrates a remarkable ability to drive business insights. Sahil holds a Post Graduate Program in Business Analytics and Business Intelligence from Great Lakes Institute of Management.