Question

finding the upper bound for each code snippet. complete the upper bound column for space and time complexities. s/n snippet time space

Answer 1

The question involves calculating the time complexity and space complexity of given code snippets using Big O notation, which indicates the upper bound for the algorithm's run time and memory usage.

Step-by-step explanation:

The question refers to analyzing the time complexity and space complexity of code snippets, aiming to determine the upper bound of each. In computer science, time complexity refers to the computational complexity that describes the amount of time it takes to run an algorithm. Space complexity, on the other hand, refers to the amount of memory space needed by the algorithm to process data.

To find the upper bounds, one would typically use Big O notation, which expresses the worst-case scenario or the maximum amount of time (for time complexity) and space (for space complexity) an algorithm can possibly take to complete.

For example, let's consider a simple for loop that iterates 'n' times over an array to sum its elements:

int sum = 0;
for (int i = 0; i < n; i++) {
sum += arr[i];
}

The time complexity would be O(n) because the loop runs 'n' times. The space complexity would be O(1) because there is a constant amount of space used, no matter the size of 'n'.