WSJT-X/boost/libs/math/doc/sf/digamma.qbk

[section:digamma Digamma]

[h4 Synopsis]

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

  namespace boost{ namespace math{
  
  template <class T>
  ``__sf_result`` digamma(T z);
  
  template <class T, class ``__Policy``>
  ``__sf_result`` digamma(T z, const ``__Policy``&);
  
  }} // namespaces
  
[h4 Description]

Returns the digamma or psi function of /x/. Digamma is defined as the logarithmic
derivative of the gamma function:

[equation digamma1]

[graph digamma]

[optional_policy]

The return type of this function is computed using the __arg_promotion_rules:
the result is of type `double` when T is an integer type, and 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.
Unless otherwise specified any floating point type that is narrower
than the one shown will have __zero_error.

[table_digamma]

As shown above, error rates for positive arguments are generally very low.
For negative arguments there are an infinite number of irrational roots:
relative errors very close to these can be arbitrarily large, although
absolute error will remain very low.

[h4 Testing]

There are two sets of tests: spot values are computed using
the online calculator at functions.wolfram.com, while random test values
are generated using the high-precision reference implementation (a 
differentiated __lanczos see below).

[h4 Implementation]

The implementation is divided up into the following domains:

For Negative arguments the reflection formula:

   digamma(1-x) = digamma(x) + pi/tan(pi*x);
   
is used to make /x/ positive.

For arguments in the range [0,1] the recurrence relation:

   digamma(x) = digamma(x+1) - 1/x
   
is used to shift the evaluation to [1,2].

For arguments in the range [1,2] a rational approximation [jm_rationals] is used (see below).

For arguments in the range [2,BIG] the recurrence relation:

   digamma(x+1) = digamma(x) + 1/x;
   
is used to shift the evaluation to the range [1,2].

For arguments > BIG the asymptotic expansion:

[equation digamma2]

can be used.  However, this expansion is divergent after a few terms: 
exactly how many terms depends on the size of /x/.  Therefore the value
of /BIG/ must be chosen so that the series can be truncated at a term
that is too small to have any effect on the result when evaluated at /BIG/.
Choosing BIG=10 for up to 80-bit reals, and BIG=20 for 128-bit reals allows
the series to truncated after a suitably small number of terms and evaluated
as a polynomial in `1/(x*x)`.

The arbitrary precision version of this function uses recurrence relations until
x > BIG, and then evaluation via the asymptotic expansion above.  As special cases
integer and half integer arguments are handled via:

[equation digamma4]

[equation digamma5]

The rational approximation [jm_rationals] in the range [1,2] is derived as follows.

First a high precision approximation to digamma was constructed using a 60-term
differentiated __lanczos, the form used is:

[equation digamma3]

Where P(x) and Q(x) are the polynomials from the rational form of the Lanczos sum,
and P'(x) and Q'(x) are their first derivatives.  The Lanzos part of this
approximation has a theoretical precision of ~100 decimal digits.  However, 
cancellation in the above sum will reduce that to around `99-(1/y)` digits
if /y/ is the result.  This approximation was used to calculate the positive root
of digamma, and was found to agree with the value used by 
Cody to 25 digits (See Math. Comp. 27, 123-127 (1973) by Cody, Strecok and Thacher)
and with the value used by Morris to 35 digits (See TOMS Algorithm 708).

Likewise a few spot tests agreed with values calculated using
functions.wolfram.com to >40 digits.
That's sufficiently precise to insure that the approximation below is
accurate to double precision.  Achieving 128-bit long double precision requires that
the location of the root is known to ~70 digits, and it's not clear whether 
the value calculated by this method meets that requirement: the difficulty
lies in independently verifying the value obtained.

The rational approximation [jm_rationals] was optimised for absolute error using the form:

   digamma(x) = (x - X0)(Y + R(x - 1));
   
Where X0 is the positive root of digamma, Y is a constant, and R(x - 1) is the
rational approximation.  Note that since X0 is irrational, we need twice as many
digits in X0 as in x in order to avoid cancellation error during the subtraction
(this assumes that /x/ is an exact value, if it's not then all bets are off).  That
means that even when x is the value of the root rounded to the nearest 
representable value, the result of digamma(x) ['[*will not be zero]].


[endsect][/section:digamma The Digamma 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).
]