In computer science, the space complexity of an algorithm refers to the amount of memory that the algorithm requires to execute. Space complexity is often analyzed in terms of the amount of additional memory used by an algorithm as a function of the input size.

Linear search is a simple and straightforward algorithm for searching for an element in a list or array. It works by sequentially checking each element in the list until the target element is found or until the end of the list is reached.

Let’s analyze the space complexity of the linear search algorithm.

Space Complexity of Linear Search

The space complexity of linear search can be considered as constant space complexity, denoted as O(1). This means that the amount of additional memory required by the algorithm does not depend on the size of the input.

In linear search, the algorithm does not require any extra memory proportional to the input size. It only uses a constant amount of memory to store the variables required for the algorithm execution, such as the loop index and the target value.

Conclusion

In conclusion, the space complexity of linear search is constant, which means it does not grow with the size of the input data. This makes linear search a space-efficient algorithm for searching for an element in a list or array.

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