Question

Determine the complexity (O (?) ) of the algorithms that could

be used to perform the following tasks (assuming the list size is
large towards infinity). Type your answer in
( )

Answer 1

The complexity of an algorithm determines how its performance scales with the size of the input. It is typically denoted as O(?) where ? represents the complexity class. Common complexities include O(1), O(log n), O(n), and O(n^2). To determine the complexity for specific tasks, we need more information about the algorithms used.

Step-by-step explanation:

The complexity of an algorithm determines how its performance scales with the size of the input. It is typically denoted as O(?) where ? represents the complexity class.

Some common complexities include:

• O(1) - constant time complexity, where the algorithm takes the same amount of time regardless of the input size (e.g., accessing an element in an array)
• O(log n) - logarithmic time complexity, where the algorithm's runtime increases logarithmically with the input size (e.g., binary search)
• O(n) - linear time complexity, where the algorithm's runtime increases linearly with the input size (e.g., iterating through an array)
• O(n^2) - quadratic time complexity, where the algorithm's runtime increases quadratically with the input size (e.g., nested loops)

For specific tasks, the complexity would depend on the algorithm used. Going through each task individually:

Task 1:

To determine the complexity of an algorithm for this task, we would need more information about what the task involves.

Task 2:

Similarly, without knowing the details of the task, we cannot determine the complexity.

Task 3:

Without information about the specific algorithms used for performing the tasks, we cannot determine their complexities.

Overall, to assess the complexity of an algorithm, we need to understand the specific task and the algorithm being used.