This module provides one and multidimensional adaptive integration routines for the Julia language, including support for vectorvalued integrands and facilitation of parallel evaluation of integrands, based on the Cubature Package by Steven G. Johnson.
See also the HCubature package for a pureJulia implementation of hadaptive cubature using the same algorithm (which is therefore much more flexible in the types that it can integrate).
Adaptive integration works by evaluating the integrand at more and
more points until the integrand converges to a specified tolerance
(with the error estimated by comparing integral estimates with
different numbers of points). The Cubature module implements two
schemes for this adaptation: hadaptivity (routines hquadrature
,
hcubature
, hquadrature_v
, and hcubature_v
) and padaptivity
(routines pquadrature
, pcubature
, pquadrature_v
, and
pcubature_v
). The h and padaptive routines accept the same
parameters, so you can use them interchangeably, but they have
very different convergence characteristics.
hadaptive integration works by recursively subdividing the integration domain into smaller and smaller regions, applying the same fixedorder (fixed number of points) integration rule within each subregion and subdividing a region if its error estimate is too large. (Technically, we use a GaussKronrod rule in 1d and a GenzMalik rule in higher dimensions.) This is wellsuited for functions that have localized sharp features (peaks, kinks, etcetera) in a portion of the domain, because it will adaptively add more points in this region while using a coarser set of points elsewhere. The hadaptive routines should be your default choice if you know very little about the function you are integrating.
padaptive integration works by repeatedly doubling the number of points in the same domain, fitting to higher and higher degree polynomials (in a stable way) until convergence is achieved to the specified tolerance. (Technically, we use ClenshawCurtis quadrature rules.) This is bestsuited for integrating smooth functions (infinitely differentiable, ideally analytic) in low dimensions (ideally 1 or 2), especially when high accuracy is required.
One technical difference that is sometimes important for functions with singularities at the edges of the integration domain: our hadaptive algorithm only evaluates the integrand at the interior of the domain (never at the edges), whereas our padaptive algorithm also evaluates the integrand at the edges.
(The names "hadaptive" and "padaptive" refer to the fact that the size of the subdomains is often denoted h while the degree of the polynomial fitting is often called p.)
Before using any of the routines below (and after installing, see above),
you should include using Cubature
in your code to import the functions
from the Cubature module.
The simplest case is to integrate a single realvalued integrand f(x)
from xmin
to xmax
, in which case you can call (similar to
Julia's builtin quadgk
routine):
(val,err) = hquadrature(f::Function, xmin::Real, xmax::Real;
reltol=1e8, abstol=0, maxevals=0)
for hadaptive integration, or pquadrature
(with the same arguments)
for padaptive integration. The return value is a tuple of val
(the
estimated integral) and err
(the estimated absolute error in val
,
usually a conservative upper bound). The required arguments are:

f
is the integrand, a functionf(x::Float64)
that accepts a real argument (in the integration domain) and returns a real value. 
xmin
andxmax
are the boundaries of the integration domain. (That is,f
is integrated fromxmin
toxmax
.) They must be finite; to compute integrals over infinite or semiinfinite domains, you can use a change of variables.
There are also the following optional keyword arguments:

reltol
is the required relative error tolerance: the adaptive integration will terminate whenerr
≤reltol
*val
; the default is1e8
. 
The optional argument
abstol
is a required absolute error tolerance: the adaptive integration will terminate whenerr
≤abstol
. More precisely, the integration will terminate when either the relative or the absoluteerror tolerances are met.abstol
defaults to 0, which means that it is ignored, but it can be useful to specify an absoute error tolerance for integrands that may integrate to zero (or nearly zero) because of large cancellations, in which case the problem is illconditioned and a small relative error tolerance may be unachievable. 
The optional argument
maxevals
specifies a (rough) maximum number of function evaluations: the integration will be terminated (and the current estimates returned) if this number is exceeded. The defaultmaxevals
is 0, in which casemaxevals
is ignored (no maximum).
Here is an example that integrates f(x) = x^3 from 0 to 1, printing the x coordinates that are evaluated:
hquadrature(x > begin println(x); x^3; end, 0,1)
and returning (0.25,2.7755575615628914e15)
, which is the correct
answer 0.25. If we instead integrate from 1 to 1, the function may
never exit: the exact integral is zero, and it is nearly impossible to
satisfy the default reltol
bound in floatingpoint arithmetic. In
that case, you have to specify an abstol
as explained above:
hquadrature(x > begin println(x); x^3; end, 1,1, abstol=1e8)
in which case it quickly returns.
The next simplest case is to integrate a single realvalued integrand f(x)
over a multidimensional box,
with each coordinate x[i]
integrated from xmin[i]
to xmax[i]
.
(val,err) = hcubature(f::Function, xmin, xmax;
reltol=1e8, abstol=0, maxevals=0)
for hadaptive integration, or pcubature
(with the same arguments)
for padaptive integration. The return value is a tuple of val
(the
estimated integral) and err
(the estimated absolute error in val
,
usually a conservative upper bound). The arguments are:

f
is the integrand, a functionf(x::Vector{Float64})
that accepts a vectorx
(in the integration domain) and returns a real value. 
xmin
andxmax
are arrays or tuples (or any iterable container) specifying the boundariesxmin[i]
andxmax[i]
of the integration domain in each coordinate. They must havelength(xmin) == length(xmax)
. (As above, the components must be finite, but you can treat infinite domains via a change of variables). 
The optional keyword arguments
reltol
,abstol
, andmaxevals
specify termination criteria as forhquadrature
above.
Here is the same 1d example as above, integrating f(x) = x^3 from 0 to 1 while the x coordinates that are evaluated:
hcubature(x > begin println(x[1]); x[1]^3; end, 0,1)
which again returns the correct integral 0.25. The only difference from
before is that the argument x
of our integrand is now an array, so
we must use x[1]
to access its value. If we have multiple coordinates, we use x[1]
, x[2]
, etcetera, as in this example integrating f(x,y) = x^3 y in the unit box [0,1]x[0,1] (the exact integral is 0.125):
hcubature(x > begin println(x[1],",",x[2]); x[1]^3*x[2]; end, [0,0],[1,1])
In many applications, one wishes to compute integrals of several
different integrands over the same domain. Of course, you could simply
call hquadrature
or hcubature
multiple times, once for each integrand.
However, in cases where the integrands are closely related functions, it
is sometimes much more efficient to compute them together for a given
point x
than computing them separately. For example, if you have
a complexvalued integrand, you could compute two separate integrals
of the real and imaginary parts, but it is often more efficient and
convenient to compute the real and imaginary parts at the same time.
The Cubature module supports this situation by allowing you to
integrate a vectorvalued integrand, computing fdim
real integrals
at once for any given dimension fdim
(the dimension of the
integrand, which is independent of the dimensionality of the
integration domain). This is achieved by calling one of:
(val,err) = hquadrature(fdim::Integer, f::Function, xmin, xmax;
reltol=1e8, abstol=0, maxevals=0,
error_norm = Cubature.INDIVIDUAL)
(val,err) = hcubature(fdim::Integer, f::Function, xmin, xmax;
reltol=1e8, abstol=0, maxevals=0,
error_norm = Cubature.INDIVIDUAL)
for hadaptive integration, or pquadrature
/pcubature
(with the
same arguments) for padaptive integration. The return value is a
tuple of two vectors of length fdim
: val
(the estimated integrals
val[i]
) and err
(the estimated absolute errors err[i]
in
val[i]
). The arguments are:

fdim
the dimension (number of components) of the integrand, i.e. the number of realvalued integrals to perform simultaneously 
f
, the integrand. This is a functionf(x, v)
of two arguments: the pointx
in the integration domain (aFloat64
forhquadrature
and aVector{Float64}
forhcubature
), and the vectorv::Vector{Float64}
of lengthfdim
which is used to output the integrand values. That is, the functionf
should setv[i]
to the value of thei
th integrand upon return. (The return value off
is ignored.) Note: the contents ofv
must be overwritten inplace byf
. If you are not settingv[i]
individually, you should dov[:] = ...
and notv = ...
. 
xmin
andxmax
specify the boundaries of the integration domain, as forhquadrature
andhcubature
of scalar integrands above. 
The optional keyword arguments
reltol
,abstol
, andmaxevals
specify termination criteria as forhquadrature
above. 
The optional keyword argument
error_norm
specifies how the convergence criteria for the different integrands are combined. That is, given a vectorval
of integral estimates and a vectorerr
of error estimates, how do we decide whether to stop?error_norm
should be one of the following constants:
Cubature.INDIVIDUAL
, the default. This terminates the integration when all of the integrals, taken individually, converge. That is, it checkserr[i]
≤reltol
*val[i]
 orerr[i]
≤abstol
, and only stops when one of these is true for alli
. 
Cubature.PAIRED
. This is likeCubature.INDIVIDUAL
, but applies the convergence criteria to consecutive pairs of integrands, as if these integrands were real and imaginary parts of complex numbers. (This is mainly useful for integrating complex functions in cases where you only care about error in the complex plane as opposed to error in the real and imaginary parts taken individually.) 
Cubature.L1
,Cubature.L2
, orCubature.LINF
. These terminate the integration when err
 ≤reltol
*val
 or err
 ≤abstol
, where ... denotes a norm applied to the whole vector of errors or integrals. In particular, the L1 norm (sum of absolute values), the L2 norm (the rootmeansquare value), or the Linfinity norm (the maximum absolute value), respectively. These are useful if you only care about the error in the vector of integrals taken as a whole in some norm, rather than the relative error in the components taken individually (which could be large if some of the components integrate almost to zero). We provide three different norms for completeness, but probably the choice of norm doesn't matter too much; pickCubature.L1
if you aren't sure.

Here is an example, similar to above, which integrates a vector of three integrands (x, x^2, x^3) from 0 to 1:
hquadrature(3, (x,v) > v[:] = x.^[1:3], 0,1)
returning ([0.5, 0.333333, 0.25],[5.55112e15, 3.70074e15, 2.77556e15])
, which are of course the correct integrals.
These numerical integration algorithms actually call your integrand function for batches of points at a time, not just pointbypoint. It is useful to expose this information for parellelization: your code may be able to evaluate the integrand in parallel for multiple points.
This is provided by a "vectorized" interface to the Cubature module:
functions hquadrature_v
, pquadrature_v
, hcubature_v
, and
pcubature_v
, which have exactly the same arguments as the
functions described in the previous sections, except that the
integrand function f
must accept different arguments.
In particular, for the _v
integration routines, the integrand must
be a function f(x,v)
where x
is an array of n
points to evaluate
and v
is an array in which to store the values of the integrands at
those points. n
is determined at runtime and varies between calls
to f
. The shape of the arrays depends upon which routine is called:

For
hquadrature_v
andpquadrature_v
with realvalued integrands (nofdim
argument),x
andv
are both 1dFloat64
arrays of lengthn
of the points (input) and values (output), respectively. 
For
hcubature_v
andpcubature_v
with realvalued integrands (nofdim
argument) ind
integration dimensions,x
is a 2dFloat64
array of sized
×n
holding the pointsx[:,i]
at which to evaluate the integrand, andv
is a 1dFloat64
array of lengthn
in which to store the resulting integrand values. 
For
hquadrature_v
andpquadrature_v
with vectorvalued integrands (anfdim
argument),x
is a 1dFloat64
array of lengthn
of points at which to evaluate the integrands, andv
is a 2dFloat64
array of sizefdim
×n
in which to store the valuesv[:,i]
at these points. 
For
hcubature_v
andpcubature_v
with vectorvalued integrands (anfdim
argument) ind
integration dimensions,x
is a 2dFloat64
array of lengthd
×n
of pointsx[:,i]
at which to evaluate the integrands, andv
is a 2dFloat64
array of sizefdim
×n
in which to store the valuesv[:,i]
at these points.
The hadaptive integration routines are based on those described in:
 A. C. Genz and A. A. Malik, "An adaptive algorithm for numeric integration over an Ndimensional rectangular region," J. Comput. Appl. Math., vol. 6 (no. 4), 295302 (1980).
 J. Berntsen, T. O. Espelid, and A. Genz, "An adaptive algorithm for the approximate calculation of multiple integrals," ACM Trans. Math. Soft., vol. 17 (no. 4), 437451 (1991).
which we implemented in a C library, the Cubature Package, that is called from Julia.
Note that we do ''not'' use any of the original DCUHRE code by Genz, which is not under a free/opensource license.) Our code is based in part on code borrowed from the HIntLib numericintegration library by Rudolf Schürer and from code for GaussKronrod quadrature (for 1d integrals) from the GNU Scientific Library, both of which are free software under the GNU GPL. (Another freesoftware multidimensional integration library, unrelated to our code here but also implementing the GenzMalik algorithm among other techniques, is Cuba.)
The hcubature_v
technique is adapted from I. Gladwell,
"Vectorization of one dimensional quadrature codes," pp. 230238 in
Numerical Integration. Recent Developments, Software and
Applications, G. Fairweather and P. M. Keast, eds., NATO ASI Series
C203, Dordrecht (1987), as described in J. M. Bull and T. L. Freeman,
"Parallel Globally Adaptive Algorithms for Multidimensional
Integration,"
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.6638
(1994).
The padaptive integration algorithm is simply a tensor product of nested ClenshawCurtis quadrature rules for poweroftwo sizes, using a precomputed table of points and weights up to order 2^20.
This module was written by Steven G. Johnson.