Constant: A program whose running time’s order of growth is constant executes a fixed number of operations and its running time does not depend on N.

Logarithmic: A program whose running time’s order of growth is logarithmic is barely slower than constant. The base of the logarithm is not relevant with respect to the order of growth, so its order of growth is log N.

Linear: Programs that spend a constant amount of time processing each piece of input data, or that are based on a single for loop. Its running time is proportional to N.

Linearithmic: Programs whose running time for a problem of size N has order of growth N log N.

Quadratic: Programs whose running time has order of growth N^2 has two nested for loops, used for some calculation involving all pairs of N elements.

Cubic: Programs whose running time has order of growth N^3 has three nested for loops, used for some calculation involving all triples of N elements.

Exponencial: Exponencial algorithms are extremely slow and its order of growth is b^N for any constant b > 1.

order of growth description example
constant 1 statement add two numbers
logarithmic log N divide in half binary search
linear N loop find the maximum
linearithmic N log N divide and conquer mergesort
quadratic N^2 double loop check all pairs
cubic N^3 triple loop check all triples
exponential 2^N exhaustive search check all subsets

Note: this is not a complete set, it only contains the most common classfications.