The  total  of  time  needed  by  an algorithm to run to its completion is termed as time complexity. The asymptotic running time of an algorithm is given in terms of functions. The upper bound for a function 'f' is provided by the big oh notation (O). Taking 'g' to be a function from the non-negative integers to the positive real numbers, we define O(g) as a set of function f  such that  for some real constant c>0 and for some non negative integers constant n0, f(n)≤cg(n) for all n≥n0. Mathematically, O(g(n))={f(n): there are positive constants such that 0≤f f(n)≤cg(n) for all n, n≥n0} , we say "f is oh of g"

 

 

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