Computational complexity categorizes algorithmic resource consumption—such as execution time or memory—as the input problem size n approaches infinity. Big-O notation establishes rigorous asymptotic upper bounds, distinguishing tractable polynomial algorithms from intractable exponential and factorial scaling classes.