algorithm Algorithm Complexity Big-Theta notation
Example
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 not vice versa!
Formal mathematical definition
Ө(g(x)) is a set of functions.
Ө(g(x)) = {f(x) such that there exist positive constants c1, c2, N such that 0 <= c1*g(x) <= f(x) <= c2*g(x) for all x > N}
Because Ө(g(x)) is a set, we could write f(x) ∈ Ө(g(x))
to indicate that f(x) is a member of Ө(g(x)). Instead, we will usually write
f(x) = Ө(g(x)) to express the same notion - that's the common way.
Whenever Ө(g(x)) appears in a formula, we interpret it as standing for some anonymous function that we do not care to name. For example the equation T(n) = T(n/2) + Ө(n), means T(n) = T(n/2) + f(n) where f(n) is a function in the set Ө(n).
Let f and g be two functions defined on some subset of the real numbers. We write f(x) = Ө(g(x)) as x->infinity if and only if there are positive constants K and L and a real number x0 such that holds:
K|g(x)| <= f(x) <= L|g(x)| for all x >= x0.
The definition is equal to:
f(x) = O(g(x)) and f(x) = Ω(g(x))
A method that uses limits
if limit(x->infinity) f(x)/g(x) = c ∈ (0,∞) i.e. the limit exists and it's positive, then f(x) = Ө(g(x))
Common Complexity Classes
| Name | Notation | n = 10 | n = 100 |
|---|---|---|---|
| Constant | Ө(1) | 1 | 1 |
| Logarithmic | Ө(log(n)) | 3 | 7 |
| Linear | Ө(n) | 10 | 100 |
| Linearithmic | Ө(n*log(n)) | 30 | 700 |
| Quadratic | Ө(n^2) | 100 | 10 000 |
| Exponential | Ө(2^n) | 1 024 | 1.267650e+ 30 |
| Factorial | Ө(n!) | 3 628 800 | 9.332622e+157 |
