An algorithm is made up of 2 modules M1 and M2. If time complexity of module M1 and M2 are and respectively, the time complexity of algorithm is :

Explanation: Understanding Algorithm Structure: When an algorithm consists of discrete modules executed one after the other (sequentially), the total execution time is the sum of the time taken by each individual module. Applying Big-O Asymptotic Rules (The Sum Rule): In computer science, when expressing total time complexity asymptotically using Big-O notation ( ), we look for the dominant term that dictates the growth rate as approaches infinity. The Sum Rule of asymptotic notation states: Why Upper Bound depends on the Maximum: Suppose module M1 takes linear time and module M2 takes quadratic time . Running them sequentially takes operations. For very large inputs ( ), the term grows so rapidly that the impact of the single term becomes completely insignificant. Thus, the total complexity scales at

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AMU 2026 — Computer PYQ

AMU | Computer | 2026

An algorithm is made up of 2 modules M1 and M2. If time complexity of module M1 and M2 are $h (n)$ and $g (n)$ respectively, the time complexity of algorithm is :

AMU 2026 — Computer PYQ

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