Using asymptotic analysis, we can very well conclude the best case, average case, and worst case scenario of an algorithm. Asymptotic analysis is input bound i.

Other than the "input" all other factors are considered constant. Asymptotic analysis refers to computing the running time of any operation in mathematical units of computation. For example, the running time of one operation is computed as f n and may be for another operation it is computed as g n 2.

This means the first operation running time will increase linearly with the increase in n and the running time of the second operation will increase exponentially when n increases. Similarly, the running time of both operations will be nearly the same if n is significantly small.

Following are the commonly used asymptotic notations to calculate the running time complexity of an algorithm. It measures the worst case time complexity or the longest amount of time an algorithm can possibly take to complete.

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It measures the best case time complexity or the best amount of time an algorithm can possibly take to complete. Data Structures - Asymptotic Analysis Advertisements.

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## Algorithms Lecture 1 -- Introduction to asymptotic notations

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