MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Big O Notation is a metric for determining an algorithm’s efficiency. Put simply, it gives an estimate of how long it takes your code to run on different sets of inputs. You can also see it as a way to measure how effectively your code scales as your input size increases.

Detailed explanation-2: -Big O Notation is a way to measure an algorithm’s efficiency. It measures the time it takes to run your function as the input grows. Or in other words, how well does the function scale.

Detailed explanation-3: -Big O Notation is a tool used to describe the time complexity of algorithms. It calculates the time taken to run an algorithm as the input grows. In other words, it calculates the worst-case time complexity of an algorithm. Big O Notation in Data Structure describes the upper bound of an algorithm’s runtime.

Detailed explanation-4: -Big-O means “is of the same order as”. The corresponding little-o means “is ul-timately smaller than”: f (n) = o(1) means that f (n)/c !

Detailed explanation-5: -Definition: A theoretical measure of the execution of an algorithm, usually the time or memory needed, given the problem size n, which is usually the number of items. Informally, saying some equation f(n) = o(g(n)) means f(n) becomes insignificant relative to g(n) as n approaches infinity.