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Generally, Computational complexity of algorithms are referred to through space complexity (space needed for running program) and time complexity (time needed for running the program). In computer of science, the concept of runtime complexity has been studied vigorously. Sufficient research is being carried out to determine more efficient algorithms for present problems. We studied several asymptotic notations, to define the time complexity and space complexity of algorithms, say the big-O, Omega & Theta notations. These asymptotic orders of time & space complexity define how best or worst an algorithm is for an adequately large input.
We studied regarding the process of calculation of runtime complexity of several algorithms. The exact analysis of insertion sort was discussed to define the best case, worst case & average case scenario.
Q. Create a heap with the given list of keys: 8, 20, 9, 4, 15, 10, 7, 22, 3, 12 Ans: Creation
Q. Calculate that how many key comparisons and assignments an insertion sort makes in its worst case? Ans: The worst case performance occurs in insertion
In the last section, we discussed regarding shortest path algorithm that starts with a single source and determines shortest path to all vertices in the graph. In this section, we
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What is the best-case number of comparisons performed by mergesort on an input sequence of 2 k distinct numbers?
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The complexity of searching an element from a set of n elements using Binary search algorithm is O(log n)
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