Solution

Solution

Algorithm analysis determines the computational complexity of an algorithm, which includes time complexity and space complexity. These help predict performance and select the best algorithm for a task.

Key factors:

• Time complexity: How the running time increases as the size of the input grows. Big O notation gives an upper bound, such as $O(1)$, $O(n)$, $O(n^{2})$, or $O(\log n)$.
• Space complexity: How the amount of memory an algorithm uses changes as the input size increases.
• Scalability: Whether the algorithm remains practical as the input grows (tractable versus intractable).
• Correctness and suitability: Whether the algorithm actually solves the problem (for example, Binary Search requires a sorted list).