Boost C++ Libraries of the most highly regarded and expertly designed C++ library projects in the world. Herb Sutter and Andrei Alexandrescu, C++ Coding Standards



#include <boost/math/special_functions/beta.hpp>
namespace boost{ namespace math{

template <class T1, class T2>
calculated-result-type beta(T1 a, T2 b);

template <class T1, class T2, class Policy>
calculated-result-type beta(T1 a, T2 b, const Policy&);

}} // namespaces

The beta function is defined by:

The final Policy argument is optional and can be used to control the behaviour of the function: how it handles errors, what level of precision to use etc. Refer to the policy documentation for more details.

The return type of these functions is computed using the result type calculation rules when T1 and T2 are different types.


The following table shows peak errors for various domains of input arguments, along with comparisons to the GSL-1.9 and Cephes libraries. Note that only results for the widest floating point type on the system are given as narrower types have effectively zero error.

Table 8.17. Error rates for beta

GNU C++ version 7.1.0

GNU C++ version 7.1.0
long double

Sun compiler version 0x5150
Sun Solaris
long double

Microsoft Visual C++ version 14.1

Beta Function: Small Values

Max = 0ε (Mean = 0ε)

(GSL 2.1: Max = +INFε (Mean = +INFε) And other failures.)
(Rmath 3.2.3: Max = 1.14ε (Mean = 0.574ε))

Max = 2.86ε (Mean = 1.22ε)

(<cmath>: Max = 364ε (Mean = 76.2ε))

Max = 2.86ε (Mean = 1.22ε)

Max = 2.23ε (Mean = 1.14ε)

Beta Function: Medium Values

Max = 0.978ε (Mean = 0.0595ε)

(GSL 2.1: Max = 1.18e+03ε (Mean = 238ε))
(Rmath 3.2.3: Max = 1.09e+03ε (Mean = 265ε))

Max = 61.4ε (Mean = 19.4ε)

(<cmath>: Max = 1.07e+03ε (Mean = 264ε))

Max = 107ε (Mean = 24.5ε)

Max = 96.5ε (Mean = 22.4ε)

Beta Function: Divergent Values

Max = 0ε (Mean = 0ε)

(GSL 2.1: Max = 12.1ε (Mean = 1.99ε))
(Rmath 3.2.3: Max = 176ε (Mean = 28ε))

Max = 8.99ε (Mean = 2.44ε)

(<cmath>: Max = 128ε (Mean = 23.8ε))

Max = 18.8ε (Mean = 2.71ε)

Max = 11.4ε (Mean = 2.19ε)

Note that the worst errors occur when a or b are large, and that when this is the case the result is very close to zero, so absolute errors will be very small.


A mixture of spot tests of exact values, and randomly generated test data are used: the test data was computed using NTL::RR at 1000-bit precision.


Traditional methods of evaluating the beta function either involve evaluating the gamma functions directly, or taking logarithms and then exponentiating the result. However, the former is prone to overflows for even very modest arguments, while the latter is prone to cancellation errors. As an alternative, if we regard the gamma function as a white-box containing the Lanczos approximation, then we can combine the power terms:

which is almost the ideal solution, however almost all of the error occurs in evaluating the power terms when a or b are large. If we assume that a > b then the larger of the two power terms can be reduced by a factor of b, which immediately cuts the maximum error in half:

This may not be the final solution, but it is very competitive compared to other implementation methods.

The generic implementation - where no Lanczos approximation approximation is available - is implemented in a very similar way to the generic version of the gamma function by means of Sterling's approximation. Again in order to avoid numerical overflow the power terms that prefix the series are collected together

There are a few special cases worth mentioning:

When a or b are less than one, we can use the recurrence relations:

to move to a more favorable region where they are both greater than 1.

In addition: