[section:zeta Riemann Zeta Function]

[h4 Synopsis]

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

   namespace boost{ namespace math{
   
   template <class T>
   ``__sf_result`` zeta(T z);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` zeta(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`` zeta(T z);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` zeta(T z, const ``__Policy``&);
   
Returns the [@http://mathworld.wolfram.com/RiemannZetaFunction.html zeta function]
of z:

[equation zeta1]

[graph zeta1]

[graph zeta2]

[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 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 zeta(z)
[[Significand Size] [Platform and Compiler] [z > 0][z < 0]]
[[53] [Win32, Visual C++ 8] [Peak=0.99 Mean=0.1

GSL Peak=8.7 Mean=1.0 

__cephes Peak=2.1 Mean=1.1
]  [Peak=7.1 Mean=3.0 

GSL Peak=137 Mean=14 

__cephes Peak=5084 Mean=470
]]
[[64] [RedHat Linux IA_EM64, gcc-4.1] [Peak=0.99 Mean=0.5]  [Peak=570 Mean=60]]
[[64] [Redhat Linux IA64, gcc-4.1] [Peak=0.99 Mean=0.5]  [Peak=559 Mean=56]]
[[113] [HPUX IA64, aCC A.06.06] [Peak=1.0 Mean=0.4]  [Peak=1018 Mean=79]]
]

[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=Zeta 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:

[equation zeta3]

The generic versions of these functions are implemented using the series:

[equation zeta1]

for large z, and using the globally convergent series:

[equation zeta2]

In all other cases.  The crossover point for these is chosen so that the first
series is used only if it will converge reasonably quickly, the problem with this
series is that convergence become slower the more terms you take, so we really do
have to be certain of convergence before using this series, even though the 
alternative is often quite slow.

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 0 < z < 1 the approximating form is:

[equation zeta4]

For a rational approximation R(1-z) and a constant C.

For 1 < z < 4 the approximating form is:

[equation zeta5]

For a rational approximation R(n-z) and a constant C and integer n.

For z > 4 the approximating form is:

[zeta](z) = 1 + e[super R(z - n)]

For a rational approximation R(z-n) and integer n, note that the accuracy 
required for R(z-n) is not full machine precision, but an absolute error
of: [epsilon]/R(0).  This saves us quite a few digits when dealing with large 
z, especially when [epsilon] is small.

[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).
]