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Big O notation and Worst case Analysis of an Algorithm?
in this we will ans the following question Is big-O notation a tool to do best, worst, & average case analysis of an algorithm? Big O for worst-case running time and Ω is for the best-case, but why is Ω used in worst case sometimes? What is the difference between Big O notation and Worst case Analysis of an Algorithm? As for my understanding both will give upper bound of any given function. Please explain the difference. There is a common misconception that Big-O means worst-case, Big-Omega means best-case, Big-Theta means average-case. How do O and Ω relate to worst and best case?
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