I think what you ask for is the distribution achieved using a square root function.

[position] = sqrt(rand(0, 1))

This will give a distribution in the single dimension field [0, 1] where the probability for a position is equivalent to that position, i.e. a "triangular distribution".

I think what you ask for is the distribution achieved using a square root function.

[position] = sqrt(rand(0, 1))

This will give a distribution in the single dimension field [0, 1] where the probability for a position is equivalent to that position, i.e. a "triangular distribution".

Alternate squareroot-free generation:

[position] = 1-abs(rand(0, 1)-rand(0, 1))

A square root in optimal implementation is just a few multiplication and sum commands with no branches. (See: http://en.wikipedia.org/wiki/Fast_inverse_square_root). Which one of these two functions are faster may vary depending on platform and random generator. On an x86 platform for instance it would take only a few unpredictable branches in the random generator to make the second method slower.