The Dickman–de Bruijn function ρ(u) plotted on a logarithmic scale. The horizontal axis is the argument u, and the vertical axis is the value of the function. The graph nearly makes a downward line on the logarithmic scale, demonstrating that the logarithm of the function is quasilinear.
The Dickman–de Bruijn used to calculate the probability that the largest and 2nd largest factor of x is less than x^a
The main purpose of the Dickman–de Bruijn function is to estimate the frequency of smooth numbers at a given size. This can be used to optimize various number-theoretical algorithms such as P–1 factoring and can be useful of its own right.
It can be shown that
which is related to the estimate below.
The Golomb–Dickman constant has an alternate definition in terms of the Dickman–de Bruijn function.
Estimation
A first approximation might be A better estimate is
For each interval [n−1,n] with n an integer, there is an analytic function such that . For 0≤u≤1, . For 1≤u≤2, . For 2≤u≤3,
with Li2 the dilogarithm. Other can be calculated using infinite series.
An alternate method is computing lower and upper bounds with the trapezoidal rule; a mesh of progressively finer sizes allows for arbitrary accuracy. For high precision calculations (hundreds of digits), a recursive series expansion about the midpoints of the intervals is superior. Values for u≤7 can be usefully computed via numerical integration in ordinary double-precision floating-point.:99
Extension
Friedlander defines a two-dimensional analog of . This function is used to estimate a function similar to de Bruijn's, but counting the number of y-smooth integers with at most one prime factor greater than z. Then
This class of numbers may be encountered in the two-stage variant of P-1 factoring. However, Kruppa's estimate of the probability of finding a factor by P-1 does not make use of this result.:100
See also
Buchstab function, a function used similarly to estimate the number of rough numbers, whose convergence to is controlled by the Dickman function
1234Kruppa, Alexander (2010). Speeding up Integer Multiplication and Factorization(PDF) (PhD thesis). Henri Poincaré University. – Work describes algorithms that Kruppa had contributed to GMP-ECM and other factoring programs. Some chapters have been published elsewhere.