Answer:
In our previous articles on Analysis of Algorithms, we had discussed asymptotic notations, their worst and best case performance, etc. in brief. In this article, we discuss the analysis of the algorithm using Big – O asymptotic notation in complete detail.
Step-by-step explanation:
Introduction. Big O notation is used to describe the complexity of an algorithm when measuring its efficiency, which in this case means how well the algorithm scales with the size of the dataset.
We can express algorithmic complexity using the big-O notation. For a problem of size N:
A constant-time function/method is “order 1” : O(1)
A linear-time function/method is “order N” : O(N)
A quadratic-time function/method is “order N squared” : O(N 2 )
Definition: Let g and f be functions from the set of natural numbers to itself. The function f is said to be O(g) (read big-oh of g), if there is a constant c > 0 and a natural number n0 such that f (n) ≤ cg(n) for all n >= n0 .
Note: O(g) is a set!