[section:lgamma Log Gamma]

[h4 Synopsis]

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

   namespace boost{ namespace math{
   
   template <class T>
   ``__sf_result`` lgamma(T z);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` lgamma(T z, const ``__Policy``&);
   
   template <class T>
   ``__sf_result`` lgamma(T z, int* sign);
   
   template <class T, class ``__Policy``>
   ``__sf_result`` lgamma(T z, int* sign, const ``__Policy``&);
   
   }} // namespaces

[h4 Description]

The [@http://en.wikipedia.org/wiki/Gamma_function lgamma function] is defined by:

[equation lgamm1]

The second form of the function takes a pointer to an integer,
which if non-null is set on output to the sign of tgamma(z).

[optional_policy]

[graph lgamma]

There are effectively two versions of this function internally: a fully
generic version that is slow, but reasonably accurate, and a much more
efficient approximation that is used where the number of digits in the significand
of T correspond to a certain __lanczos.  In practice, any built-in
floating-point type you will encounter has an appropriate __lanczos
defined for it.  It is also possible, given enough machine time, to generate
further __lanczos's using the program libs/math/tools/lanczos_generator.cpp.

The return type of these functions is computed using the __arg_promotion_rules:
the result is of type `double` if T is an integer type, or type T otherwise.

[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 
various other libraries. Unless otherwise specified any
floating point type that is narrower than the one shown will have
__zero_error.

Note that while the relative errors near the positive roots of lgamma
are very low, the lgamma function has an infinite number of irrational
roots for negative arguments: very close to these negative roots only
a low absolute error can be guaranteed.

[table_lgamma]

[h4 Testing]

The main tests for this function involve comparisons against the logs of 
the factorials which can be independently calculated to very high accuracy.

Random tests in key problem areas are also used.

[h4 Implementation]

The generic version of this function is implemented using Sterling's approximation
for large arguments:

[equation gamma6]

For small arguments, the logarithm of tgamma is used.

For negative /z/ the logarithm version of the 
reflection formula is used:

[equation lgamm3]

For types of known precision, the __lanczos is used, a traits class 
`boost::math::lanczos::lanczos_traits` maps type T to an appropriate
approximation.  The logarithmic version of the __lanczos is:

[equation lgamm4]

Where L[sub e,g][space] is the Lanczos sum, scaled by e[super g].

As before the reflection formula is used for /z < 0/.

When z is very near 1 or 2, then the logarithmic version of the __lanczos
suffers very badly from cancellation error: indeed for values sufficiently
close to 1 or 2, arbitrarily large relative errors can be obtained (even though
the absolute error is tiny).  

For types with up to 113 bits of precision
(up to and including 128-bit long doubles), root-preserving 
rational approximations [jm_rationals] are used
over the intervals [1,2] and [2,3].  Over the interval [2,3] the approximation
form used is:

   lgamma(z) = (z-2)(z+1)(Y + R(z-2));
   
Where Y is a constant, and R(z-2) is the rational approximation: optimised
so that it's absolute error is tiny compared to Y.  In addition 
small values of z greater
than 3 can handled by argument reduction using the recurrence relation:

   lgamma(z+1) = log(z) + lgamma(z);
   
Over the interval [1,2] two approximations have to be used, one for small z uses:

   lgamma(z) = (z-1)(z-2)(Y + R(z-1));
   
Once again Y is a constant, and R(z-1) is optimised for low absolute error
compared to Y.  For z > 1.5 the above form wouldn't converge to a 
minimax solution but this similar form does:

   lgamma(z) = (2-z)(1-z)(Y + R(2-z));
   
Finally for z < 1 the recurrence relation can be used to move to z > 1:

   lgamma(z) = lgamma(z+1) - log(z);
   
Note that while this involves a subtraction, it appears not
to suffer from cancellation error: as z decreases from 1 
the `-log(z)` term grows positive much more
rapidly than the `lgamma(z+1)` term becomes negative.  So in this
specific case, significant digits are preserved, rather than cancelled.

For other types which do have a __lanczos defined for them 
the current solution is as follows: imagine we
balance the two terms in the __lanczos by dividing the power term by its value
at /z = 1/, and then multiplying the Lanczos coefficients by the same value.
Now each term will take the value 1 at /z = 1/ and we can rearrange the power terms
in terms of log1p.  Likewise if we subtract 1 from the Lanczos sum part 
(algebraically, by subtracting the value of each term at /z = 1/), we obtain
a new summation that can be also be fed into log1p.  Crucially, all of the 
terms tend to zero, as /z -> 1/:

[equation lgamm5]

The C[sub k][space] terms in the above are the same as in the __lanczos.

A similar rearrangement can be performed at /z = 2/:

[equation lgamm6]

[endsect][/section:lgamma The Log Gamma Function]

[/ 
  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).
]
