Chapter 2: Algorithm Complexity Section 2.1: Big-Theta notation Unlike Big-O notation, which represents only upper bound of the running time for some algorithm, Big-Theta is a tight bound; both upper and lower bound. Tight bound is more precise, but also more difficult to compute. The Big-Theta notation is symmetric: f(x) = Ө(g(x)) <=> g(x) = Ө(f(x)) An intuitive way to grasp it is that f(x) = Ө(g(x)) means that the graphs of f(x) and g(x) grow in the same rate, or that the graphs 'behave' similarly for big enough values of x. The full mathematical expression of the Big-Theta notation is as follows: Ө(f(x)) = {g: N0 -> R and c1, c2, n0 > 0, where c1 < abs(g(n) / f(n)), for every n > n0 and abs is the absolute value } An example If the algorithm for the input n takes 42n^2 + 25n + 4 operations to finish, we say that is O(n^2), but is also O(n^3) and O(n^100). However, it is Ө(n^2) and it is not Ө(n^3), Ө(n^4) etc. Algorithm that is Ө(f(n)) is also O(f(n)), but ...