[section:error_function Error Functions]

[h4 Synopsis]

``
#include <boost/math/special_functions/erf.hpp>
``

   namespace boost{ namespace math{
   
   template <class T>
   ``__sf_result`` erf(T z);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` erf(T z, const ``__Policy``&);
   
   template <class T>
   ``__sf_result`` erfc(T z);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` erfc(T z, const ``__Policy``&);
   
   }} // namespaces
   
The return type of these functions is computed using the __arg_pomotion_rules:
the return type is `double` if T is an integer type, and T otherwise.

[optional_policy]

[h4 Description]

   template <class T>
   ``__sf_result`` erf(T z);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` erf(T z, const ``__Policy``&);
   
Returns the [@http://en.wikipedia.org/wiki/Error_function error function]
[@http://functions.wolfram.com/GammaBetaErf/Erf/ erf] of z:

[equation erf1]

[$../graphs/erf1.png]

   template <class T>
   ``__sf_result`` erfc(T z);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` erfc(T z, const ``__Policy``&);
   
Returns the complement of the [@http://functions.wolfram.com/GammaBetaErf/Erfc/ error function] of z:

[equation erf2]

[$../graphs/erf2.png]

[h4 Accuracy]

The following table shows the peak errors (in units of epsilon) 
found on various platforms with various floating point types, 
along with comparisons to the __gsl, __glibc, __hpc and __cephes libraries.
Unless otherwise specified any floating point type that is narrower
than the one shown will have __zero_error.

[table Errors In the Function erf(z)
[[Significand Size] [Platform and Compiler] [z < 0.5]  [0.5 < z < 8] [z > 8]]
[[53] [Win32, Visual C++ 8] [Peak=0 Mean=0

GSL Peak=2.0 Mean=0.3 

__cephes Peak=1.1 Mean=0.7]  [Peak=0.9 Mean=0.09 

GSL Peak=2.3 Mean=0.3 

__cephes Peak=1.3 Mean=0.2] [Peak=0 Mean=0

GSL Peak=0 Mean=0 

__cephes Peak=0 Mean=0]]
[[64] [RedHat Linux IA32, gcc-3.3] [Peak=0.7 Mean=0.07

__glibc Peak=0.9 Mean=0.2]  [Peak=0.9 Mean=0.2

__glibc Peak=0.9 Mean=0.07] [Peak=0 Mean=0

__glibc Peak=0 Mean=0]]
[[64] [Redhat Linux IA64, gcc-3.4.4] [Peak=0.7 Mean=0.07

__glibc Peak=0 Mean=0]  [Peak=0.9 Mean=0.1

__glibc Peak=0.5 Mean=0.03] [Peak=0 Mean=0

__glibc Peak=0 Mean=0]]
[[113] [HPUX IA64, aCC A.06.06] [Peak=0.8 Mean=0.1

__hpc Lib Peak=0.9 Mean=0.2]  [Peak=0.9 Mean=0.1

__hpc Lib Peak=0.5 Mean=0.02] [Peak=0 Mean=0

__hpc Lib Peak=0 Mean=0]]
]

[table Errors In the Function erfc(z)
[[Significand Size] [Platform and Compiler] [z < 0.5]  [0.5 < z < 8] [z > 8]]
[[53] [Win32, Visual C++ 8] [Peak=0.7 Mean=0.06 

GSL Peak=1.0 Mean=0.4 

__cephes Peak=0.7 Mean=0.06]  [Peak=0.99 Mean=0.3

GSL Peak=2.6 Mean=0.6 

__cephes Peak=3.6 Mean=0.7] [Peak=1.0 Mean=0.2

GSL Peak=3.9 Mean=0.4 

__cephes Peak=2.7 Mean=0.4]]
[[64] [RedHat Linux IA32, gcc-3.3] [Peak=0 Mean=0

__glibc Peak=0 Mean=0]  [Peak=1.4 Mean=0.3

__glibc Peak=1.3 Mean=0.3] [Peak=1.6 Mean=0.4

__glibc Peak=1.3 Mean=0.4]]
[[64] [Redhat Linux IA64, gcc-3.4.4] [Peak=0 Mean=0

__glibc Peak=0 Mean=0]  [Peak=1.4 Mean=0.3

__glibc Peak=0 Mean=0] [Peak=1.5 Mean=0.4

__glibc Peak=0 Mean=0] ]
[[113] [HPUX IA64, aCC A.06.06] [Peak=0 Mean=0

__hpc Peak=0 Mean=0]  [Peak=1.5 Mean=0.3

__hpc Peak=0.9 Mean=0.08] [Peak=1.6 Mean=0.4

__hpc Peak=0.9 Mean=0.1]]
]

[h4 Testing]

The tests for these functions come in two parts:
basic sanity checks use spot values calculated using
[@http://functions.wolfram.com/webMathematica/FunctionEvaluation.jsp?name=Erf Mathworld's online evaluator],
while accuracy checks use high-precision test values calculated at 1000-bit precision with
[@http://shoup.net/ntl/doc/RR.txt NTL::RR] and this implementation. 
Note that the generic and type-specific
versions of these functions use differing implementations internally, so this
gives us reasonably independent test data.  Using our test data to test other
"known good" implementations also provides an additional sanity check. 

[h4 Implementation]

All versions of these functions first use the usual reflection formulas
to make their arguments positive:

   erf(-z) = 1 - erf(z);
   
   erfc(-z) = 2 - erfc(z);  // preferred when -z < -0.5
   
   erfc(-z) = 1 + erf(z);   // preferred when -0.5 <= -z < 0

The generic versions of these functions are implemented in terms of
the incomplete gamma function.

When the significand (mantissa) size is recognised
(currently for 53, 64 and 113-bit reals, plus single-precision 24-bit handled via promotion to double)
then a series of rational approximations [jm_rationals] are used.

For `z <= 0.5` then a rational approximation to erf is used, based on the 
observation that:

   erf(z)/z ~ 1.12....
   
Therefore erf is calculated using:

   erf(z) = z * (1.125F + R(z));
   
where the rational approximation R(z) is optimised for absolute error:
as long as its absolute error is small enough compared to 1.125, then any 
round-off error incurred during the computation of R(z) will effectively 
disappear from the result.  As a result the error for erf and erfc in this
region is very low: the last bit is incorrect in only a very small number of 
cases.

For `z > 0.5` we observe that over a small interval [a, b) then:

   erfc(z) * exp(z*z) * z ~ c
   
for some constant c.

Therefore for `z > 0.5` we calculate erfc using:

   erfc(z) = exp(-z*z) * (c + R(z)) / z;
   
Again R(z) is optimised for absolute error, and the constant `c` is
the average of `erfc(z) * exp(z*z) * z` taken at the endpoints of the range.
Once again, as long as the absolute error in R(z) is small
compared to `c` then `c + R(z)` will be correctly rounded, and the error
in the result will depend only on the accuracy of the exp function.  In practice,
in all but a very small number of cases, the error is confined to the last bit
of the result.

[endsect]
[/ :error_function The Error Functions]

[/ 
  Copyright 2006 John Maddock and Paul A. Bristow.
  Distributed under the Boost Software License, Version 1.0.
  (See accompanying file LICENSE_1_0.txt or copy at
  http://www.boost.org/LICENSE_1_0.txt).
]
