What is Linear search?

Linear Search searches the list progressively and returns the position if the key element to be searched is found in the list; else, -1 is returned. Linear Search begins at the beginning of an array and progresses to the finish, searching for a match at each item.

Before the search element is traversed, the elements before it are traversed. In other words, if the element to be searched is in position 10, all items in positions 1-9 are examined before 10.

Linear search algorithm implementation

bool linear_search ( int *list, int size, int key, int* rec )

{

     bool found = false;

     int i;

     for ( i = 0; i< size; i++ )

           {

                if ( key == list[i] )

                      break;

            }

              if ( i< size )

                 {

                      found = true;

                      rec = &list[i];

                 }

               return found;

}

The code looks for the element in a loop that runs from 0 to n. There are two ways for the loop to end. The loop condition fails if the index variable i reaches the end of the list. The loop is ended early using a break statement if the current item in the list matches the key. The method then checks the index variable to determine if it is smaller than that size (in which case the loop is ended early and the item is discovered) or not (in which case the item is not found).

Assume that element 45 is being sought from a list of sorted elements 12, 18, 25, 36, 45, 48, 50. Since the value to be searched is not 12 (value 45), the next element 18 is compared and is also not 45, and so on until the index is 5, at which point the element 45 is compared with the search value and is equal, indicating that the element has been found and the element position is 5.

Linear Search Algorithm Complexity

You will encounter three distinct complications when running the Linear Search Algorithm, which are as follows.

Best Case, Worst Case, and Average Case

Complexity in the Best-Case Scenario

The element under consideration might be discovered in the first place.
The search in this example concludes with a single successful comparison.
As a result, the linear search method executes O(1) operations in the best-case scenario.

Complexity in the Worst-Case Scenario

The element being sought may be near the end of the array or not at all.
The search succeeds in 'n' comparisons in the first example.
The search fails after 'n' comparisons in the following scenario.
As a result, the linear search method performs O(n) operations in the worst-case situation.

Complexity in Average Case Scenario

When the element to be sought is in the center of the array, the average case of the Linear Search Algorithm is O(n).

Linear Search Algorithm Space Complexity

The linear search technique takes up no extra space; for an array of n entries, its space complexity is O(n).

Linear Search Algorithm Application

The linear search algorithm is useful in the following situations:

Linear search may be used on single-dimensional as well as multi-dimensional arrays.
When the array has only a few entries, linear search is simple to implement and effective.
Linear Search is also efficient when used to retrieve a single search in an unordered-List.

Linear Search Properties

Time Complexity: The worst-case linear search time complexity is o(n), where n is the size of the array.
Linear search takes an equal volume of memory to operate.
Efficiency: The linear search method is effective for tiny databases but ineffective for large datasets. Linear search is frequently used as a subpart in more complicated algorithms.
Linear search may be simply accomplished using a loop with every iteration comparing the goal value to the current array entry.

Advantages of Linear Search

Simple to grasp: Linear search is a basic algorithm that is straightforward to comprehend and apply, making it an excellent choice for novices. It works by checking each member in a list progressively until the required element is discovered, making it a simple operation.
There is no need for a particular data structure: Linear search is a versatile choice since it may be used to any data structure. It does not require any particular data structures, such as a balanced tree or a hash table, which makes it easier to construct.
Can be used on unsorted data: Linear search can be performed on unsorted data, which can be useful in some instances. In contrast, some search methods, such as binary search, need the data to be sorted.
No additional memory required: Linear search does not require any more memory to be used, making it a more memory-efficient choice. In contrast, other search methods, such as hash tables, require more memory to hold the indexes.
Not influenced by data size: Linear search is unaffected by data size and may be used to find entries in big lists. This is because the algorithm searches for an element in the same amount of time regardless of the size of the list.

Disadvantages of Linear Search

Linear search may betime-consuming, particularly when working with huge lists. When looking for an element in a big list, it checks each element one by one, which might take a long time. As a result, it is less efficient than other search algorithms, such as binary search.
Linear search is not ideal for huge data sets since it is less efficient than other search algorithms when dealing with enormous data sets. Searching for an element in a huge list might take a long time, which can be a significant disadvantage when working with massive data.
Not appropriate for ordered data: Linear search is not appropriate for ordered data. When looking for an element in ordered data, other search techniques, such as binary search, are more efficient.
Not appropriate for huge data sets: Not ideal for repetitive jobs - When utilized for repetitive tasks, linear search may be time-consuming and inefficient. It is not appropriate for activities that need many searches since searching for the same element several times would take longer.
Linear search is not ideal for real-time applications since it might take a long time to find an element. This might be a significant drawback in applications that demand a rapid response time.

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