Determine the time and space complexities of a linear search.
Time complexity :O(log(n)) Space complexity O(n)
Time complexity :O(n) Space complexity O(log(n))
Time complexity :O(n) Space complexity O(n)
Time complexity O(log(n)) Space complexity O(log(n))
The correct time and space complexities of a linear search algorithm are:
Time complexity: O(n)
Space complexity: O(1)
In a linear search, we iterate through each element in the input sequentially until we find the desired element or reach the end of the list. In the worst case scenario, where the desired element is at the end of the list or does not exist in the list at all, we would have to iterate through all n elements of the list, making the time complexity O(n).
The space complexity is O(1) because we only need a constant amount of space to store the index or pointer to the current element we are checking. No additional space is required as the size of the input increases.
The correct answer is:
Time complexity: O(n)
Space complexity: O(1)
The correct answer is:
Time complexity: O(n)
Space complexity: O(1)
A linear search, also known as sequential search, is a simple searching algorithm that checks each element in a list or array until the target element is found or until the end of the list/array is reached.
The time complexity of a linear search is O(n), where n is the number of elements in the list/array. In the worst-case scenario, where the target element is located at the end of the list/array or is not present at all, the algorithm will have to iterate through all n elements.
The space complexity of a linear search is O(1), which means that the amount of additional memory required by the algorithm does not depend on the size of the input. In a linear search, only a few variables are needed to keep track of the current position and store the target element, resulting in constant space consumption.