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.