That is the big O notation, see here:
http://en.wikipedia.org/wiki/Big_O_notationIt's the common way to describe the complexity of operations and algorithms. Example:
O(1) means that your algorithm always takes a constant amount of operations, no matter how big n (the dimension) is. 6, 10, 89078 are all elements of the set O(1), because they are constant multiples of "1".
O(n) means that the cost of your algorithm/ operation increases linearly with the size of your "problem". n, 4/3n, 4n+5 and the like are all elements of O(n).
n^2, n^2 - 3n + 7 are elements of O(n^2) and so on.
