Little o notation is not commonly used to measure the time complexity of an algorithm. It is used to describe the asymptotic behavior of a function as its input approaches a specific value.

A linear search algorithm takes O(n) time because it needs to examine each element of the array in the worst case.

Merge Sort has the best time complexity for sorting an array of size n, which is O(n log n). It uses the divide-and-conquer approach to sort the array efficiently.

The space complexity of an algorithm that stores the entire input array in memory is O(n) because it requires n units of space to store each element of the array.

Bubble Sort has the worst-case time complexity of O(n²) because it compares each element of the array with every other element, resulting in a total of n * (n - 1) / 2 comparisons.

Binary search has a time complexity of O(n log n) because it repeatedly divides the search space in half, reducing the number of elements to be searched by a factor of 2 in each iteration.

Linear search has the best space complexity for finding the minimum value in an array of size n because it only requires O(1) space to store the current minimum value.

The time complexity of depth-first search on a graph is O(V + E) because it visits each vertex and edge once in the worst case.

Bubble Sort has the worst-case space complexity of O(n²) because it creates a temporary array of size n to store the sorted elements.

The time complexity of breadth-first search on a graph is O(V + E) because it visits each vertex and edge once in the worst case.

Linear search has the best time complexity for finding the maximum value in an array of size n because it only requires O(1) space to store the current maximum value.

The space complexity of storing the path from the root node to the target node in a binary search tree is O(n) because the worst-case scenario is when the target node is located at the deepest level of the tree.

Quick Select has the best time complexity for finding the median of an array of size n because it uses a randomized selection algorithm that has an expected time complexity of O(n).

The time complexity of topological sort on a DAG is O(V + E) because it visits each vertex and edge once in the worst case.