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Big-O, Omega and Theta Notations

Explore the definitions and applications of Big-O, Omega, and Theta notations within time complexity analysis. Understand how these asymptotic notations measure upper, lower, and tight bounds to evaluate algorithm performance, enabling you to assess and compare different algorithms effectively.

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Big-OO notation

Definition: “f(n)f(n) is big-OO of g(n)g(n)” or f(n)f(n) = O(g(n))O(g(n)), if there are two positive constants cc and n0n0 such that f(n)cg(n)f(n) ≤ c g(n) for all nn0n ≥ n0,

In other words, cg(n)c g(n) is an upper bound for f(n)f(n) for all nn0n ≥ n0. The function f(n)f(n) growth is slower than cg(n)c g(n). For a sufficiently large value of input nn ...