MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Time and space complexity are the two main measures for calculating algorithm efficiency, determining how many resources are needed on a machine to process it. Where time measures how long it takes to process the algorithm, space measures how much memory is used.

Detailed explanation-2: -One way to measure the efficiency of an algorithm is to count how many operations it needs in order to find the answer across different input sizes.

Detailed explanation-3: -Time complexity is a function that describes how long an algorithm takes in terms of the quantity of input it receives. Space complexity is a function that describes how much memory (space) an algorithm requires to the quantity of input to the method.

Detailed explanation-4: -Time efficiency-a measure of amount of time for an algorithm to execute. Space efficiency-a measure of the amount of memory needed for an algorithm to execute. Asymptotic dominance-comparison of cost functions when n is large. That is, g asymptotically dominates f if g dominates f for all “large” values of n.

Detailed explanation-5: -To express the time complexity of an algorithm, we use something called the “Big O notation”. The Big O notation is a language we use to describe the time complexity of an algorithm. It’s how we compare the efficiency of different approaches to a problem, and helps us to make decisions.