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<div class="titlepage"><div><div><h3 class="title">
<a name="math_toolkit.backgrounders.lanczos"></a><a class="link" href="lanczos.html" title="The Lanczos Approximation"> The Lanczos Approximation</a>
</h3></div></div></div>
<a name="math_toolkit.backgrounders.lanczos.motivation"></a><h5>
<a name="id1288612"></a>
<a class="link" href="lanczos.html#math_toolkit.backgrounders.lanczos.motivation">Motivation</a>
</h5>
<p>
<span class="emphasis"><em>Why base gamma and gamma-like functions on the Lanczos approximation?</em></span>
</p>
<p>
First of all I should make clear that for the gamma function over real numbers
(as opposed to complex ones) the Lanczos approximation (See <a href="http://en.wikipedia.org/wiki/Lanczos_approximation" target="_top">Wikipedia
or </a> <a href="http://mathworld.wolfram.com/LanczosApproximation.html" target="_top">Mathworld</a>)
appears to offer no clear advantage over more traditional methods such as
<a href="http://en.wikipedia.org/wiki/Stirling_approximation" target="_top">Stirling's
approximation</a>. <a class="link" href="lanczos.html#pugh">Pugh</a> carried out an extensive
comparison of the various methods available and discovered that they were
all very similar in terms of complexity and relative error. However, the
Lanczos approximation does have a couple of properties that make it worthy
of further consideration:
</p>
<div class="itemizedlist"><ul type="disc">
<li>
The approximation has an easy to compute truncation error that holds
for all <span class="emphasis"><em>z > 0</em></span>. In practice that means we can
use the same approximation for all <span class="emphasis"><em>z > 0</em></span>, and
be certain that no matter how large or small <span class="emphasis"><em>z</em></span> is,
the truncation error will <span class="emphasis"><em>at worst</em></span> be bounded by
some finite value.
</li>
<li>
The approximation has a form that is particularly amenable to analytic
manipulation, in particular ratios of gamma or gamma-like functions are
particularly easy to compute without resorting to logarithms.
</li>
</ul></div>
<p>
It is the combination of these two properties that make the approximation
attractive: Stirling's approximation is highly accurate for large z, and
has some of the same analytic properties as the Lanczos approximation, but
can't easily be used across the whole range of z.
</p>
<p>
As the simplest example, consider the ratio of two gamma functions: one could
compute the result via lgamma:
</p>
<pre class="programlisting"><span class="identifier">exp</span><span class="special">(</span><span class="identifier">lgamma</span><span class="special">(</span><span class="identifier">a</span><span class="special">)</span> <span class="special">-</span> <span class="identifier">lgamma</span><span class="special">(</span><span class="identifier">b</span><span class="special">));</span>
</pre>
<p>
However, even if lgamma is uniformly accurate to 0.5ulp, the worst case relative
error in the above can easily be shown to be:
</p>
<pre class="programlisting"><span class="identifier">Erel</span> <span class="special">></span> <span class="identifier">a</span> <span class="special">*</span> <span class="identifier">log</span><span class="special">(</span><span class="identifier">a</span><span class="special">)/</span><span class="number">2</span> <span class="special">+</span> <span class="identifier">b</span> <span class="special">*</span> <span class="identifier">log</span><span class="special">(</span><span class="identifier">b</span><span class="special">)/</span><span class="number">2</span>
</pre>
<p>
For small <span class="emphasis"><em>a</em></span> and <span class="emphasis"><em>b</em></span> that's not a
problem, but to put the relationship another way: <span class="emphasis"><em>each time a and
b increase in magnitude by a factor of 10, at least one decimal digit of
precision will be lost.</em></span>
</p>
<p>
In contrast, by analytically combining like power terms in a ratio of Lanczos
approximation's, these errors can be virtually eliminated for small <span class="emphasis"><em>a</em></span>
and <span class="emphasis"><em>b</em></span>, and kept under control for very large (or very
small for that matter) <span class="emphasis"><em>a</em></span> and <span class="emphasis"><em>b</em></span>.
Of course, computing large powers is itself a notoriously hard problem, but
even so, analytic combinations of Lanczos approximations can make the difference
between obtaining a valid result, or simply garbage. Refer to the implementation
notes for the <a class="link" href="../special/sf_beta/beta_function.html" title="Beta">beta</a>
function for an example of this method in practice. The incomplete <a class="link" href="../special/sf_gamma/igamma.html" title="Incomplete Gamma Functions">gamma_p gamma</a> and
<a class="link" href="../special/sf_beta/ibeta_function.html" title="Incomplete Beta Functions">beta</a> functions
use similar analytic combinations of power terms, to combine gamma and beta
functions divided by large powers into single (simpler) expressions.
</p>
<a name="math_toolkit.backgrounders.lanczos.the_approximation"></a><h5>
<a name="id1288870"></a>
<a class="link" href="lanczos.html#math_toolkit.backgrounders.lanczos.the_approximation">The
Approximation</a>
</h5>
<p>
The Lanczos Approximation to the Gamma Function is given by:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos0.png"></span>
</p>
<p>
Where S<sub>g</sub>(z) is an infinite sum, that is convergent for all z > 0, and
<span class="emphasis"><em>g</em></span> is an arbitrary parameter that controls the "shape"
of the terms in the sum which is given by:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos0a.png"></span>
</p>
<p>
With individual coefficients defined in closed form by:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos0b.png"></span>
</p>
<p>
However, evaluation of the sum in that form can lead to numerical instability
in the computation of the ratios of rising and falling factorials (effectively
we're multiplying by a series of numbers very close to 1, so roundoff errors
can accumulate quite rapidly).
</p>
<p>
The Lanczos approximation is therefore often written in partial fraction
form with the leading constants absorbed by the coefficients in the sum:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos1.png"></span>
</p>
<p>
where:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos2.png"></span>
</p>
<p>
Again parameter <span class="emphasis"><em>g</em></span> is an arbitrarily chosen constant,
and <span class="emphasis"><em>N</em></span> is an arbitrarily chosen number of terms to evaluate
in the "Lanczos sum" part.
</p>
<div class="note"><table border="0" summary="Note">
<tr>
<td rowspan="2" align="center" valign="top" width="25"><img alt="[Note]" src="../../../../../../../doc/src/images/note.png"></td>
<th align="left">Note</th>
</tr>
<tr><td align="left" valign="top"><p>
Some authors choose to define the sum from k=1 to N, and hence end up with
N+1 coefficients. This happens to confuse both the following discussion
and the code (since C++ deals with half open array ranges, rather than
the closed range of the sum). This convention is consistent with <a class="link" href="lanczos.html#godfrey">Godfrey</a>, but not <a class="link" href="lanczos.html#pugh">Pugh</a>,
so take care when referring to the literature in this field.
</p></td></tr>
</table></div>
<a name="math_toolkit.backgrounders.lanczos.computing_the_coefficients"></a><h5>
<a name="id1289062"></a>
<a class="link" href="lanczos.html#math_toolkit.backgrounders.lanczos.computing_the_coefficients">Computing
the Coefficients</a>
</h5>
<p>
The coefficients C0..CN-1 need to be computed from <span class="emphasis"><em>N</em></span>
and <span class="emphasis"><em>g</em></span> at high precision, and then stored as part of
the program. Calculation of the coefficients is performed via the method
of <a class="link" href="lanczos.html#godfrey">Godfrey</a>; let the constants be contained
in a column vector P, then:
</p>
<p>
P = B D C F
</p>
<p>
where B is an NxN matrix:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos4.png"></span>
</p>
<p>
D is an NxN matrix:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos3.png"></span>
</p>
<p>
C is an NxN matrix:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos5.png"></span>
</p>
<p>
and F is an N element column vector:
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos6.png"></span>
</p>
<p>
Note than the matrices B, D and C contain all integer terms and depend only
on <span class="emphasis"><em>N</em></span>, this product should be computed first, and then
multiplied by <span class="emphasis"><em>F</em></span> as the last step.
</p>
<a name="math_toolkit.backgrounders.lanczos.choosing_the_right_parameters"></a><h5>
<a name="id1289219"></a>
<a class="link" href="lanczos.html#math_toolkit.backgrounders.lanczos.choosing_the_right_parameters">Choosing
the Right Parameters</a>
</h5>
<p>
The trick is to choose <span class="emphasis"><em>N</em></span> and <span class="emphasis"><em>g</em></span>
to give the desired level of accuracy: choosing a small value for <span class="emphasis"><em>g</em></span>
leads to a strictly convergent series, but one which converges only slowly.
Choosing a larger value of <span class="emphasis"><em>g</em></span> causes the terms in the
series to be large and/or divergent for about the first <span class="emphasis"><em>g-1</em></span>
terms, and to then suddenly converge with a "crunch".
</p>
<p>
<a class="link" href="lanczos.html#pugh">Pugh</a> has determined the optimal value of <span class="emphasis"><em>g</em></span>
for <span class="emphasis"><em>N</em></span> in the range <span class="emphasis"><em>1 <= N <= 60</em></span>:
unfortunately in practice choosing these values leads to cancellation errors
in the Lanczos sum as the largest term in the (alternating) series is approximately
1000 times larger than the result. These optimal values appear not to be
useful in practice unless the evaluation can be done with a number of guard
digits <span class="emphasis"><em>and</em></span> the coefficients are stored at higher precision
than that desired in the result. These values are best reserved for say,
computing to float precision with double precision arithmetic.
</p>
<div class="table">
<a name="id1289273"></a><p class="title"><b>Table 53. Optimal choices for N and g when computing with guard digits (source:
Pugh)</b></p>
<div class="table-contents"><table class="table" summary="Optimal choices for N and g when computing with guard digits (source:
Pugh)">
<colgroup>
<col>
<col>
<col>
<col>
</colgroup>
<thead><tr>
<th>
<p>
Significand Size
</p>
</th>
<th>
<p>
N
</p>
</th>
<th>
<p>
g
</p>
</th>
<th>
<p>
Max Error
</p>
</th>
</tr></thead>
<tbody>
<tr>
<td>
<p>
24
</p>
</td>
<td>
<p>
6
</p>
</td>
<td>
<p>
5.581
</p>
</td>
<td>
<p>
9.51e-12
</p>
</td>
</tr>
<tr>
<td>
<p>
53
</p>
</td>
<td>
<p>
13
</p>
</td>
<td>
<p>
13.144565
</p>
</td>
<td>
<p>
9.2213e-23
</p>
</td>
</tr>
</tbody>
</table></div>
</div>
<br class="table-break"><p>
The alternative described by <a class="link" href="lanczos.html#godfrey">Godfrey</a> is to
perform an exhaustive search of the <span class="emphasis"><em>N</em></span> and <span class="emphasis"><em>g</em></span>
parameter space to determine the optimal combination for a given <span class="emphasis"><em>p</em></span>
digit floating-point type. Repeating this work found a good approximation
for double precision arithmetic (close to the one <a class="link" href="lanczos.html#godfrey">Godfrey</a>
found), but failed to find really good approximations for 80 or 128-bit long
doubles. Further it was observed that the approximations obtained tended
to optimised for the small values of z (1 < z < 200) used to test the
implementation against the factorials. Computing ratios of gamma functions
with large arguments were observed to suffer from error resulting from the
truncation of the Lancozos series.
</p>
<p>
<a class="link" href="lanczos.html#pugh">Pugh</a> identified all the locations where the theoretical
error of the approximation were at a minimum, but unfortunately has published
only the largest of these minima. However, he makes the observation that
the minima coincide closely with the location where the first neglected term
(a<sub>N</sub>) in the Lanczos series S<sub>g</sub>(z) changes sign. These locations are quite
easy to locate, albeit with considerable computer time. These "sweet
spots" need only be computed once, tabulated, and then searched when
required for an approximation that delivers the required precision for some
fixed precision type.
</p>
<p>
Unfortunately, following this path failed to find a really good approximation
for 128-bit long doubles, and those found for 64 and 80-bit reals required
an excessive number of terms. There are two competing issues here: high precision
requires a large value of <span class="emphasis"><em>g</em></span>, but avoiding cancellation
errors in the evaluation requires a small <span class="emphasis"><em>g</em></span>.
</p>
<p>
At this point note that the Lanczos sum can be converted into rational form
(a ratio of two polynomials, obtained from the partial-fraction form using
polynomial arithmetic), and doing so changes the coefficients so that <span class="emphasis"><em>they
are all positive</em></span>. That means that the sum in rational form can
be evaluated without cancellation error, albeit with double the number of
coefficients for a given N. Repeating the search of the "sweet spots",
this time evaluating the Lanczos sum in rational form, and testing only those
"sweet spots" whose theoretical error is less than the machine
epsilon for the type being tested, yielded good approximations for all the
types tested. The optimal values found were quite close to the best cases
reported by <a class="link" href="lanczos.html#pugh">Pugh</a> (just slightly larger <span class="emphasis"><em>N</em></span>
and slightly smaller <span class="emphasis"><em>g</em></span> for a given precision than <a class="link" href="lanczos.html#pugh">Pugh</a> reports), and even though converting to rational
form doubles the number of stored coefficients, it should be noted that half
of them are integers (and therefore require less storage space) and the approximations
require a smaller <span class="emphasis"><em>N</em></span> than would otherwise be required,
so fewer floating point operations may be required overall.
</p>
<p>
The following table shows the optimal values for <span class="emphasis"><em>N</em></span> and
<span class="emphasis"><em>g</em></span> when computing at fixed precision. These should be
taken as work in progress: there are no values for 106-bit significand machines
(Darwin long doubles & NTL quad_float), and further optimisation of the
values of <span class="emphasis"><em>g</em></span> may be possible. Errors given in the table
are estimates of the error due to truncation of the Lanczos infinite series
to <span class="emphasis"><em>N</em></span> terms. They are calculated from the sum of the
first five neglected terms - and are known to be rather pessimistic estimates
- although it is noticeable that the best combinations of <span class="emphasis"><em>N</em></span>
and <span class="emphasis"><em>g</em></span> occurred when the estimated truncation error almost
exactly matches the machine epsilon for the type in question.
</p>
<div class="table">
<a name="id1289495"></a><p class="title"><b>Table 54. Optimum value for N and g when computing at fixed precision</b></p>
<div class="table-contents"><table class="table" summary="Optimum value for N and g when computing at fixed precision">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<thead><tr>
<th>
<p>
Significand Size
</p>
</th>
<th>
<p>
Platform/Compiler Used
</p>
</th>
<th>
<p>
N
</p>
</th>
<th>
<p>
g
</p>
</th>
<th>
<p>
Max Truncation Error
</p>
</th>
</tr></thead>
<tbody>
<tr>
<td>
<p>
24
</p>
</td>
<td>
<p>
Win32, VC++ 7.1
</p>
</td>
<td>
<p>
6
</p>
</td>
<td>
<p>
1.428456135094165802001953125
</p>
</td>
<td>
<p>
9.41e-007
</p>
</td>
</tr>
<tr>
<td>
<p>
53
</p>
</td>
<td>
<p>
Win32, VC++ 7.1
</p>
</td>
<td>
<p>
13
</p>
</td>
<td>
<p>
6.024680040776729583740234375
</p>
</td>
<td>
<p>
3.23e-016
</p>
</td>
</tr>
<tr>
<td>
<p>
64
</p>
</td>
<td>
<p>
Suse Linux 9 IA64, gcc-3.3.3
</p>
</td>
<td>
<p>
17
</p>
</td>
<td>
<p>
12.2252227365970611572265625
</p>
</td>
<td>
<p>
2.34e-024
</p>
</td>
</tr>
<tr>
<td>
<p>
116
</p>
</td>
<td>
<p>
HP Tru64 Unix 5.1B / Alpha, Compaq C++ V7.1-006
</p>
</td>
<td>
<p>
24
</p>
</td>
<td>
<p>
20.3209821879863739013671875
</p>
</td>
<td>
<p>
4.75e-035
</p>
</td>
</tr>
</tbody>
</table></div>
</div>
<br class="table-break"><p>
Finally note that the Lanczos approximation can be written as follows by
removing a factor of exp(g) from the denominator, and then dividing all the
coefficients by exp(g):
</p>
<p>
<span class="inlinemediaobject"><img src="../../../equations/lanczos7.png"></span>
</p>
<p>
This form is more convenient for calculating lgamma, but for the gamma function
the division by <span class="emphasis"><em>e</em></span> turns a possibly exact quality into
an inexact value: this reduces accuracy in the common case that the input
is exact, and so isn't used for the gamma function.
</p>
<a name="math_toolkit.backgrounders.lanczos.references"></a><h5>
<a name="id1289756"></a>
<a class="link" href="lanczos.html#math_toolkit.backgrounders.lanczos.references">References</a>
</h5>
<a name="godfrey"></a><a name="pugh"></a><div class="orderedlist"><ol type="1">
<li>
Paul Godfrey, <a href="http://my.fit.edu/~gabdo/gamma.txt" target="_top">"A
note on the computation of the convergent Lanczos complex Gamma approximation"</a>.
</li>
<li>
Glendon Ralph Pugh, <a href="http://bh0.physics.ubc.ca/People/matt/Doc/ThesesOthers/Phd/pugh.pdf" target="_top">"An
Analysis of the Lanczos Gamma Approximation"</a>, PhD Thesis
November 2004.
</li>
<li>
Viktor T. Toth, <a href="http://www.rskey.org/gamma.htm" target="_top">"Calculators
and the Gamma Function"</a>.
</li>
<li>
Mathworld, <a href="http://mathworld.wolfram.com/LanczosApproximation.html" target="_top">The
Lanczos Approximation</a>.
</li>
</ol></div>
</div>
<table xmlns:rev="http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" width="100%"><tr>
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<td align="right"><div class="copyright-footer">Copyright © 2006 , 2007, 2008, 2009 John Maddock, Paul A. Bristow,
Hubert Holin, Xiaogang Zhang, Bruno Lalande, Johan Råde, Gautam Sewani
and Thijs van den Berg<p>
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
</p>
</div></td>
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