Cuba¶
Introduction¶
Cuba.jl
is a Julia library for multidimensional
numerical integration
of real-valued functions of real arguments, using different algorithms.
This is just a Julia wrapper around the C Cuba library, version 4.2, by Thomas Hahn. All the credits goes to him for the underlying functions, blame me for any problem with the Julia interface.
All algorithms provided by Cuba library are supported in Cuba.jl
:
Algorithm | Basic integration method | Type | Variance reduction |
---|---|---|---|
Vegas | Sobol quasi-random sample | Monte Carlo | importance sampling |
or Mersenne Twister pseudo-random sample | Monte Carlo | ||
or Ranlux pseudo-random sample | Monte Carlo | ||
Suave | Sobol quasi-random sample | Monte Carlo | globally adaptive subdivision and importance sampling |
or Mersenne Twister pseudo-random sample | Monte Carlo | ||
or Ranlux pseudo-random sample | Monte Carlo | ||
Divonne | Korobov quasi-random sample | Monte Carlo |
aided by methods from numerical optimization |
or Sobol quasi-random sample | Monte Carlo | ||
or Mersenne Twister pseudo-random sample | Monte Carlo | ||
or Ranlux pseudo-random sample | Monte Carlo | ||
or cubature rules | deterministic | ||
Cuhre | cubature rules | deterministic | globally adaptive subdivision |
For more details on the algorithms see the manual included in Cuba library and
available in deps/cuba-julia/cuba.pdf
after successful installation
of Cuba.jl
.
Integration is always performed on the \(n\)-dimensional unit hypercube \([0, 1]^{n}\).
Tip
If you want to compute an integral over a different set, you have to scale the integrand function in order to have an equivalent integral on \([0, 1]^{n}\) using substitution rules. For example, recall that in one dimension
where the final \((b - a)\) is the one-dimensional version of the Jacobian.
Integration over a semi-infinite or an inifite domain is a bit trickier, but you can follow this advice from Steven G. Johnson: to compute an integral over a semi-infinite interval, you can perform the change of variables \(x=a+y/(1-y)\):
For an infinite interval, you can perform the change of variables \(x=(2y - 1)/((1 - y)y)\):
In addition, recall that for an even function \(\int_{-\infty}^{\infty} f(x)\,\mathrm{d}x = 2\int_{0}^{\infty}f(x)\,\mathrm{d}x\), while the integral of an odd function over the infinite interval \((-\infty, \infty)\) is zero.
All this generalizes straightforwardly to more than one dimension. In
Examples section you can find the computation of a 3-dimensional integral
with non-constant boundaries using Cuba.jl
and two integrals over
infinite domains.
Cuba.jl
is available for GNU/Linux, Mac OS, and Windows (i686
and
x86_64
architectures).
Installation¶
Cuba.jl
is available for Julia 0.5 and later versions, and can be
installed with Julia built-in package
manager. In a
Julia session run the commands
julia> Pkg.update()
julia> Pkg.add("Cuba")
Installation script on GNU/Linux and Mac OS systems will download Cuba Library source code and build the Cuba shared object. In order to accomplish this task a C compiler is needed. Instead, on Windows a prebuilt version of the library is downloaded.
Older versions are also available for Julia 0.4.
Usage¶
After installing the package, run
using Cuba
or put this command into your Julia script.
Cuba.jl
provides the following functions to integrate:
-
vegas
(integrand, ndim=1, ncomp=1[; keywords...])¶
-
suave
(integrand, ndim=1, ncomp=1[; keywords...])¶
-
divonne
(integrand, ndim=1, ncomp=1[; keywords...])¶
-
cuhre
(integrand, ndim=1, ncomp=1[; keywords...])¶
Large parts of the following sections are borrowed from Cuba manual. Refer to it for more information on the details.
All 64-bit integers functions (llvegas
, llsuave
, lldivonne
,
llcuhre
) are wrapped and exported as well. See Long-integer Versions for
more information.
Arguments¶
The only mandatory argument of integrator functions is:
integrand
(type:Function
): the function to be integrated
Optional positional arguments are:
ndim
(type:Integer
): the number of dimensions of the integratation domain. Defaults to 1 if omittedncomp
(type:Integer
): the number of components of the integrand. Default to 1 if omitted
ndim
and ncomp
arguments must appear in this order, so you cannot omit
ndim
but not ncomp
. integrand
should be a function integrand(x,
f)
taking two arguments:
- the input vector
x
of lengthndim
- the output vector
f
of lengthncomp
, used to set the value of each component of the integrand at pointx
Also anonymous functions
are allowed as integrand
. For those familiar with Cubature.jl
package,
this is the same syntax used for integrating vector-valued functions.
For example, the integral
can be computed with one of the following lines
vegas((x,f)->f[1]=cos(x[1]))
# => 0.8414910005259609 ± 5.2708169787733e-5
suave((x,f)->f[1]=cos(x[1]))
# => 0.8411523690658836 ± 8.357995611133613e-5
divonne((x,f)->f[1]=cos(x[1]))
# => 0.841468071955942 ± 5.3955070531551656e-5
cuhre((x,f)->f[1]=cos(x[1]))
# => 0.8414709848078966 ± 2.2204460420128823e-16
In section Examples you can find more complete examples. Note that x
and
f
are both arrays with type Float64
, so Cuba.jl
can be used to
integrate real-valued functions of real arguments. See how to work with a
complex integrand.
Note: if you used Cuba.jl
until version 0.4, be aware that the user
interface has been reworked in version 0.5 in a backward incompatible way.
Optional Keywords¶
All other arguments required by Cuba integrator routines can be passed as
optional keywords. Cuba.jl
uses some reasonable default values in order to
enable users to invoke integrator functions with a minimal set of arguments.
Anyway, if you want to make sure future changes to some default values of
keywords will not affect your current script, explicitely specify the value of
the keywords.
Common Keywords¶
These are optional keywords common to all functions:
nvec
(type:Integer
, default:1
): the maximum number of points to be given to the integrand routine in each invocation. Usually this is 1 but if the integrand can profit from e.g. Single Instruction Multiple Data (SIMD) vectorization, a larger value can be chosen. See Vectorization section.reltol
(type:Real
, default:1e-4
), andabstol
(type:Real
, default:1e-12
): the requested relative (\(\varepsilon_{\text{rel}}\)) and absolute (\(\varepsilon_{\text{abs}}\)) accuracies. The integrator tries to find an estimate \(\hat{I}\) for the integral \(I\) which for every component \(c\) fulfills \(|\hat{I}_c - I_c|\leq \max(\varepsilon_{\text{abs}}, \varepsilon_{\text{rel}} |I_c|)\).flags
(type:Integer
, default:0
): flags governing the integration:Bits 0 and 1 are taken as the verbosity level, i.e.
0
to3
, unless theCUBAVERBOSE
environment variable contains an even higher value (used for debugging).Level
0
does not print any output, level1
prints “reasonable” information on the progress of the integration, level2
also echoes the input parameters, and level3
further prints the subregion results (if applicable).Bit 2 =
0
: all sets of samples collected on a subregion during the various iterations or phases contribute to the final result.Bit 2 =
1
, only the last (largest) set of samples is used in the final result.(Vegas and Suave only)
Bit 3 =
0
, apply additional smoothing to the importance function, this moderately improves convergence for many integrands.Bit 3 =
1
, use the importance function without smoothing, this should be chosen if the integrand has sharp edges.Bit 4 =
0
, delete the state file (if one is chosen) when the integration terminates successfully.Bit 4 =
1
, retain the state file.(Vegas only)
Bit 5 =
0
, take the integrator’s state from the state file, if one is present.Bit 5 =
1
, reset the integrator’s state even if a state file is present, i.e. keep only the grid. Together with Bit 4 this allows a grid adapted by one integration to be used for another integrand.Bits 8–31 =:
level
determines the random-number generator.
To select e.g. last samples only and verbosity level 2, pass
6 = 4 + 2
for the flags.seed
(type:Integer
, default:0
): the seed for the pseudo-random-number generator. This keyword is not available forcuhre()
. The random-number generator is chosen as follows:seed
level
(bits 8–31 offlags
)Generator zero N/A Sobol (quasi-random) non-zero zero Mersenne Twister (pseudo-random) non-zero non-zero Ranlux (pseudo-random) Ranlux implements Marsaglia and Zaman’s 24-bit RCARRY algorithm with generation period \(p\), i.e. for every 24 generated numbers used, another \(p - 24\) are skipped. The luxury level is encoded in
level
as follows:- Level 1 (\(p = 48\)): very long period, passes the gap test but fails spectral test.
- Level 2 (\(p = 97\)): passes all known tests, but theoretically still defective.
- Level 3 (\(p = 223\)): any theoretically possible correlations have very small chance of being observed.
- Level 4 (\(p = 389\)): highest possible luxury, all 24 bits chaotic.
Levels 5–23 default to 3, values above 24 directly specify the period \(p\). Note that Ranlux’s original level 0, (mis)used for selecting Mersenne Twister in Cuba, is equivalent to
level
=24
.minevals
(type:Real
, default:0
): the minimum number of integrand evaluations requiredmaxevals
(type:Real
, default:1000000
): the (approximate) maximum number of integrand evaluations allowedstatefile
(type:AbstractString
, default:""
): a filename for storing the internal state. To not store the internal state, put""
(empty string, this is the default) orC_NULL
(C null pointer).Cuba can store its entire internal state (i.e. all the information to resume an interrupted integration) in an external file. The state file is updated after every iteration. If, on a subsequent invocation, a Cuba routine finds a file of the specified name, it loads the internal state and continues from the point it left off. Needless to say, using an existing state file with a different integrand generally leads to wrong results.
This feature is useful mainly to define “check-points” in long-running integrations from which the calculation can be restarted.
Once the integration reaches the prescribed accuracy, the state file is removed, unless bit 4 of
flags
(see above) explicitly requests that it be kept.spin
(type:Ptr{Void}
, default:C_NULL
): this is the placeholder for the “spinning cores” pointer.Cuba.jl
does not support parallelization, so this keyword should not have a value different fromC_NULL
.
Vegas-Specific Keywords¶
These optional keywords can be passed only to vegas()
:
nstart
(type:Integer
, default:1000
): the number of integrand evaluations per iteration to start withnincrease
(type:Integer
, default:500
): the increase in the number of integrand evaluations per iterationnbatch
(type:Integer
, default:1000
): the batch size for samplingVegas samples points not all at once, but in batches of size
nbatch
, to avoid excessive memory consumption.1000
is a reasonable value, though it should not affect performance too muchgridno
(type:Integer
, default:0
): the slot in the internal grid table.It may accelerate convergence to keep the grid accumulated during one integration for the next one, if the integrands are reasonably similar to each other. Vegas maintains an internal table with space for ten grids for this purpose. The slot in this grid is specified by
gridno
.If a grid number between
1
and10
is selected, the grid is not discarded at the end of the integration, but stored in the respective slot of the table for a future invocation. The grid is only re-used if the dimension of the subsequent integration is the same as the one it originates from.In repeated invocations it may become necessary to flush a slot in memory, in which case the negative of the grid number should be set.
Suave-Specific Keywords¶
These optional keywords can be passed only to suave()
:
nnew
(type:Integer
, default:1000
): the number of new integrand evaluations in each subdivisionnmin
(type:Integer
, default:2
): the minimum number of samples a former pass must contribute to a subregion to be considered in that region’s compound integral value. Increasingnmin
may reduce jumps in the \(\chi^2\) valueflatness
(type:Real
, default:.25
): the type of norm used to compute the fluctuation of a sample. This determines how prominently “outliers”, i.e. individual samples with a large fluctuation, figure in the total fluctuation, which in turn determines how a region is split up. As suggested by its name,flatness
should be chosen large for “flat” integrands and small for “volatile” integrands with high peaks. Note that sinceflatness
appears in the exponent, one should not use too large values (say, no more than a few hundred) lest terms be truncated internally to prevent overflow.
Divonne-Specific Keywords¶
These optional keywords can be passed only to divonne()
:
key1
(type:Integer
, default:47
): determines sampling in the partitioning phase:key1
\(= 7, 9, 11, 13\) selects the cubature rule of degreekey1
. Note that the degree-11 rule is available only in 3 dimensions, the degree-13 rule only in 2 dimensions.For other values of
key1
, a quasi-random sample of \(n_1 = |\verb|key1||\) points is used, where the sign ofkey1
determines the type of sample,key1
\(> 0\), use a Korobov quasi-random sample,key1
\(< 0\), use a “standard” sample (a Sobol quasi-random sample ifseed
\(= 0\), otherwise a pseudo-random sample).key2
(type:Integer
, default:1
): determines sampling in the final integration phase:key2
\(= 7, 9, 11, 13\) selects the cubature rule of degreekey2
. Note that the degree-11 rule is available only in 3 dimensions, the degree-13 rule only in 2 dimensions.For other values of
key2
, a quasi-random sample is used, where the sign ofkey2
determines the type of sample,key2
\(> 0\), use a Korobov quasi-random sample,key2
\(< 0\), use a “standard” sample (see description ofkey1
above),
and \(n_2 = |\verb|key2||\) determines the number of points,
- \(n_2\geq 40\), sample \(n_2\) points,
- \(n_2 < 40\), sample \(n_2\,n_{\text{need}}\) points, where \(n_{\text{need}}\) is the number of points needed to reach the prescribed accuracy, as estimated by Divonne from the results of the partitioning phase
key3
(type:Integer
, default:1
): sets the strategy for the refinement phase:key3
\(= 0\), do not treat the subregion any further.key3
\(= 1\), split the subregion up once more.Otherwise, the subregion is sampled a third time with
key3
specifying the sampling parameters exactly askey2
above.maxpass
(type:Integer
, default:5
): controls the thoroughness of the partitioning phase: The partitioning phase terminates when the estimated total number of integrand evaluations (partitioning plus final integration) does not decrease formaxpass
successive iterations.A decrease in points generally indicates that Divonne discovered new structures of the integrand and was able to find a more effective partitioning.
maxpass
can be understood as the number of “safety” iterations that are performed before the partition is accepted as final and counting consequently restarts at zero whenever new structures are found.border
(type:Real
, default:0.
): the width of the border of the integration region. Points falling into this border region will not be sampled directly, but will be extrapolated from two samples from the interior. Use a non-zeroborder
if the integrand function cannot produce values directly on the integration boundarymaxchisq
(type:Real
, default:10.
): the \(\chi^2\) value a single subregion is allowed to have in the final integration phase. Regions which fail this \(\chi^2\) test and whose sample averages differ by more thanmindeviation
move on to the refinement phase.mindeviation
(type:Real
, default:0.25
): a bound, given as the fraction of the requested error of the entire integral, which determines whether it is worthwhile further examining a region that failed the \(\chi^2\) test. Only if the two sampling averages obtained for the region differ by more than this bound is the region further treated.ngiven
(type:Integer
, default:0
): the number of points in thexgiven
arrayldxgiven
(type:Integer
, default:0
): the leading dimension ofxgiven
, i.e. the offset between one point and the next in memoryxgiven
(type:AbstractArray{Real}
, default:zeros(typeof(0.0), ldxgiven, ngiven)
): a list of points where the integrand might have peaks. Divonne will consider these points when partitioning the integration region. The idea here is to help the integrator find the extrema of the integrand in the presence of very narrow peaks. Even if only the approximate location of such peaks is known, this can considerably speed up convergence.nextra
(type:Integer
, default:0
): the maximum number of extra points the peak-finder subroutine will return. Ifnextra
is zero,peakfinder
is not called and an arbitrary object may be passed in its place, e.g. just 0peakfinder
(type:Ptr{Void}
, default:C_NULL
): the peak-finder subroutine
Cuhre-Specific Keyword¶
This optional keyword can be passed only to cuhre()
:
key
(type:Integer
, default:0
): chooses the basic integration rule:key
\(= 7, 9, 11, 13\) selects the cubature rule of degreekey
. Note that the degree-11 rule is available only in 3 dimensions, the degree-13 rule only in 2 dimensions.For other values, the default rule is taken, which is the degree-13 rule in 2 dimensions, the degree-11 rule in 3 dimensions, and the degree-9 rule otherwise.
Output¶
The integrating functions vegas()
, suave()
, divonne()
, and
cuhre()
(and the corresponding 64-bit integers functions) return the
6-tuple
(integral, error, probability, neval, fail, nregions)
The first three elements of the tuple are arrays with length ncomp
, the last
three ones are scalars. In particular, if you assign the output of integrator
functions to the variable named result
, you can access the value of the
i
-th component of the integral with result[1][i]
and the associated
error with result[2][i]
.
integral
(type:Cdouble
array withncomp
components): the integral ofintegrand
over the unit hypercubeerror
(type:Cdouble
array withncomp
components): the presumed absolute error for each component ofintegral
probability
(type:Cdouble
array withncomp
components): the \(\chi^2\) -probability (not the \(\chi^2\) -value itself!) thaterror
is not a reliable estimate of the true integration error. To judge the reliability of the result expressed throughprob
, remember that it is the null hypothesis that is tested by the \(\chi^2\) test, which is thaterror
is a reliable estimate. In statistics, the null hypothesis may be rejected only ifprob
is fairly close to unity, sayprob
\(>.95\)neval
(type:Cint
): the actual number of integrand evaluations neededfail
(type:Cint
): an error flag:fail
=0
, the desired accuracy was reachedfail
=-1
, dimension out of rangefail
>0
, the accuracy goal was not met within the allowed maximum number of integrand evaluations. While Vegas, Suave, and Cuhre simply return1
, Divonne can estimate the number of points by whichmaxevals
needs to be increased to reach the desired accuracy and returns this value.
nregions
(type:Cint
): the actual number of subregions needed (always0
invegas()
)
Long-integer Versions¶
The following arguments:
- for Vegas:
nvec
,minevals
,maxevals
,nstart
,nincrease
,nbatch
,neval
, - for Suave:
nvec
,minevals
,maxevals
,nnew
,nmin
,neval
, - for Divonne:
nvec
,minevals
,maxevals
,ngiven
,nextra
,neval
, - for Cuhre:
nvec
,minevals
,maxevals
,neval
,
are passed, in vegas
, suave
, divonne
, cuhre
, to the Cuba library
as 32-bit integers, so they are limited to be at most
julia> typemax(Int32)
2147483647
Thus, for example, this attempt to set a large number of maximum evaluations
julia> cuhre((x,f) -> f[1] = cos(x[1]), maxevals = 3e9)
ERROR: InexactError()
in trunc(::Type{Int32}, ::Float64) at ./float.jl:458
in #cuhre#7(::Int64, ::Float64, ::Float64, ::Int64, ::Int64, ::Float64, ::Int64, ::String, ::Ptr{Void}, ::Cuba.#cuhre, ::##1#2, ::Int64, ::Int64) at /home/user/.julia/v0.5/Cuba/src/Cuba.jl:467
in (::Cuba.#kw##cuhre)(::Array{Any,1}, ::Cuba.#cuhre, ::Function, ::Int64, ::Int64) at ./<missing>:0 (repeats 2 times)
results in an error (InexactError
) because the number “3 billions” exceeds
the maximum integer number that can be represented with a 32-bit integer
(\(2^{31} - 1\)).
The functions llvegas
, llsuave
, lldivonne
, llcuhre
take 64-bit
integers for all the number-of-points-type quantities listed above. They have
the same syntax as the corresponding 32-bit integers functions and should be
used in cases where convergence is not reached within the ordinary 32-bit
integer range.
Please note that the maximum integer that can be represented as a 64-bit integer is
julia> typemax(Int64)
9223372036854775807
and there is no way to overcome this limit.
Vectorization¶
Vectorization means evaluating the integrand function for several points at once. This is also known as Single Instruction Multiple Data (SIMD) paradigm and is different from ordinary parallelization where independent threads are executed concurrently. It is usually possible to employ vectorization on top of parallelization.
Cuba.jl
cannot automatically vectorize the integrand function, of course,
but it does pass (up to) nvec
points per integrand call (Common
Keywords). This value need not correspond to the hardware vector length –
computing several points in one call can also make sense e.g. if the
computations have significant intermediate results in common.
A note for disambiguation: The nbatch
argument of Vegas is related in
purpose but not identical to nvec
. It internally partitions the sampling
done by Vegas but has no bearing on the number of points given to the integrand.
On the other hand, it it pointless to choose nvec
> nbatch
for Vegas.
Examples¶
One dimensional integral¶
The integrand of
has an algebraic-logarithmic divergence for \(x = 0\), but the integral is
convergent and its value is \(-4\). Cuba.jl
integrator routines can
handle this class of functions and you can easily compute the numerical
approximation of this integral using one of the following commands:
vegas( (x,f) -> f[1] = log(x[1])/sqrt(x[1]))
# => -3.9981623937128483 ± 0.0004406643716840934
suave( (x,f) -> f[1] = log(x[1])/sqrt(x[1]))
# => -3.999976286717149 ± 0.0003950486666134314
divonne( (x,f) -> f[1] = log(x[1])/sqrt(x[1]))
# => -3.9997602130594374 ± 0.00035678748149012664
cuhre( (x,f) -> f[1] = log(x[1])/sqrt(x[1]))
# => -4.00000035506719 ± 0.0003395484028625721
Vector-valued integrand¶
Consider the integral
where \(\Omega = [0, 1]^{3}\) and
In this case it is more convenient to write a simple Julia script to compute the above integral
using Cuba
function integrand(x, f)
f[1] = sin(x[1])*cos(x[2])*exp(x[3])
f[2] = exp(-(x[1]^2 + x[2]^2 + x[3]^2))
f[3] = 1/(1 - x[1]*x[2]*x[3])
end
result = cuhre(integrand, 3, 3, abstol=1e-12, reltol=1e-10)
answer = [(e-1)*(1-cos(1))*sin(1), (sqrt(pi)*erf(1)/2)^3, zeta(3)]
for i = 1:3
println("Component ", i)
println(" Result of Cuba: ", result[1][i], " ± ", result[2][i])
println(" Exact result: ", answer[i])
println(" Actual error: ", abs(result[1][i] - answer[i]))
end
This is the output
Component 1
Result of Cuba: 0.6646696797813739 ± 1.0050367631018485e-13
Exact result: 0.6646696797813771
Actual error: 3.219646771412954e-15
Component 2
Result of Cuba: 0.4165383858806454 ± 2.932866749838454e-11
Exact result: 0.41653838588663805
Actual error: 5.9926508200192075e-12
Component 3
Result of Cuba: 1.2020569031649702 ± 1.1958522385908214e-10
Exact result: 1.2020569031595951
Actual error: 5.375033751420233e-12
Integral with non-constant boundaries¶
The integral
has non-constant boundaries. By applying the substitution rule repeatedly, you can scale the integrand function and get this equivalent integral over the fixed domain \(\Omega = [0, 1]^{3}\)
that can be computed with Cuba.jl
using the following Julia script
using Cuba
function integrand(x, f)
f[1] = 2pi^3*x[2]*x[3]^2*cos(pi*x[2]*x[3]*(2*x[1] - 1.0))*
sin(pi*x[2]*x[3])*exp(pi*x[3])
end
result = cuhre(integrand, 3, 1, abstol=1e-12, reltol=1e-10)
answer = pi*e^pi - (4e^pi - 4)/5
println("Result of Cuba: ", result[1][1], " ± ", result[2][1])
println("Exact result: ", answer)
println("Actual error: ", abs(result[1][1] - answer))
This is the output
Result of Cuba: 54.98607586826157 ± 5.460606521639899e-9
Exact result: 54.98607586789537
Actual error: 3.6619951515604043e-10
Integrals over Infinite Domains¶
Cuba.jl
assumes always as integration domain the hypercube \([0, 1]^n\),
but we have seen that using integration by substitution we can calculate
integrals over different domains as well. In the Introduction we also
proposed two useful substitutions that can be employed to change an infinite or
semi-infinite domain into a finite one.
As a first example, consider the following integral with a semi-infinite domain:
whose exact result is \(\pi\log 2\). This can be computed with the following Julia script:
using Cuba
# The function we want to integrate over [0, ∞).
func(x) = log(1 + x^2)/(1 + x^2)
# Scale the function in order to integrate over [0, 1].
function integrand(x, f)
f[1] = func(x[1]/(1 - x[1]))/(1 - x[1])^2
end
result = cuhre(integrand, abstol = 1e-12, reltol = 1e-10)
answer = pi*log(2)
println("Result of Cuba: ", result[1][1], " ± ", result[2][1])
println("Exact result: ", answer)
println("Actual error: ", abs(result[1][1] - answer))
This is the output:
Result of Cuba: 2.177586090305688 ± 2.1503995410096295e-10
Exact result: 2.177586090303602
Actual error: 2.085887018665744e-12
Now we want to calculate this integral, over an infinite domain
which gives \(\pi\). You can calculate the result with the code below. Note that integrand function has value \(1/2\) for \(x=0\), but you have to inform Julia about this.
using Cuba
# The function we want to integrate over (-∞, ∞).
func(x) = x==0 ? 0.5*one(x) : (1 - cos(x))/x^2
# Scale the function in order to integrate over [0, 1].
function integrand(x, f)
f[1] = func((2*x[1] - 1)/x[1]/(1 - x[1])) *
(2*x[1]^2 - 2*x[1] + 1)/x[1]^2/(1 - x[1])^2
end
result = cuhre(integrand, abstol = 1e-7, reltol = 1e-7)
answer = float(pi)
println("Result of Cuba: ", result[1][1], " ± ", result[2][1])
println("Exact result: ", answer)
println("Actual error: ", abs(result[1][1] - answer))
The output of this script is
Result of Cuba: 3.1415928900555046 ± 2.050669142074607e-6
Exact result: 3.141592653589793
Actual error: 2.3646571145619077e-7
Complex integrand¶
As already explained, Cuba.jl
operates on real quantities, so if you want to
integrate a complex-valued function of complex arguments you have to treat
complex quantities as 2-component arrays of real numbers. For example, if you
do not remember Euler’s formula, you can compute this
simple integral
with the following Julia script
using Cuba
function integrand(x, f)
# Complex integrand, scaled to integrate in [0, 1].
tmp = cis(x[1]*pi/2)*pi/2
# Assign to two components of "f" the real
# and imaginary part of the integrand.
f[1], f[2] = reim(tmp)
end
result = cuhre(integrand, 1, 2)
println("Result of Cuba: ", complex(result[1]...))
println("Exact result: ", complex(1.0, 1.0))
This is the output
Result of Cuba: 1.0 + 1.0im
Exact result: 1.0 + 1.0im
Passing data to the integrand function¶
Cuba Library allows program written in C and Fortran to pass extra data to the
integrand function with userdata
argument. This is useful, for example,
when the integrand function depends on changing parameters. In Cuba.jl
the
userdata
argument is not available, but you do not normally need it.
For example, the cumulative distribution function \(F(x;k)\) of chi-squared distribution is defined by
The cumulative distribution function depends on parameter \(k\), but the
function passed as integrand to Cuba.jl
integrator routines accepts as
arguments only the input and output vectors. However you can easily define a
function to calculate a numerical approximation of \(F(x; k)\) based on the
above integral expression because the integrand can access any variable visible
in its scope. The
following Julia script computes \(F(x = \pi; k)\) for different \(k\)
and compares the result with more precise values, based on the analytic
expression of the cumulative distribution function, provided by GSL.jl package.
using Cuba, GSL
function chi2cdf(x::Real, k::Real)
k2 = k/2
# Chi-squared probability density function, without constant denominator.
# The result of integration will be divided by that factor.
function chi2pdf(t::Float64)
# "k2" is taken from the outside.
return t^(k2 - 1.0)*exp(-t/2)
end
# Neither "x" is passed directly to the integrand function,
# but is visible to it. "x" is used to scale the function
# in order to actually integrate in [0, 1].
x*cuhre((t,f) -> f[1] = chi2pdf(t[1]*x))[1][1]/(2^k2*gamma(k2))
end
x = pi
@printf("Result of Cuba: %.6f %.6f %.6f %.6f %.6f\n",
map((k) -> chi2cdf(x, k), collect(1:5))...)
@printf("Exact result: %.6f %.6f %.6f %.6f %.6f\n",
map((k) -> cdf_chisq_P(x, k), collect(1:5))...)
This is the output
Result of Cuba: 0.923681 0.792120 0.629694 0.465584 0.321833
Exact result: 0.923681 0.792120 0.629695 0.465584 0.321833
Performance¶
Cuba.jl
cannot (yet?)
take advantage of parallelization capabilities of Cuba Library. Nonetheless, it
has performances comparable with equivalent native C or Fortran codes based on
Cuba library when CUBACORES
environment variable is set to 0
(i.e.,
multithreading is disabled). The following is the result of running the
benchmark present in test
directory on a 64-bit GNU/Linux system running
Julia 0.6.0-pre.alpha.46 (commit bb76add105). The C and FORTRAN 77 benchmark
codes have been compiled with GCC 6.3.0.
$ CUBACORES=0 julia -e 'cd(Pkg.dir("Cuba")); include("test/benchmark.jl")'
INFO: Performance of Cuba.jl:
0.278550 seconds (Vegas)
0.585464 seconds (Suave)
0.334314 seconds (Divonne)
0.242272 seconds (Cuhre)
INFO: Performance of Cuba Library in C:
0.999586 seconds (Vegas)
0.617721 seconds (Suave)
0.339684 seconds (Divonne)
0.274668 seconds (Cuhre)
INFO: Performance of Cuba Library in Fortran:
0.832000 seconds (Vegas)
0.600000 seconds (Suave)
0.308000 seconds (Divonne)
0.232000 seconds (Cuhre)
Of course, native C and Fortran codes making use of Cuba Library outperform
Cuba.jl
when higher values of CUBACORES
are used, for example:
$ CUBACORES=1 julia -e 'cd(Pkg.dir("Cuba")); include("test/benchmark.jl")'
INFO: Performance of Cuba.jl:
0.277771 seconds (Vegas)
0.585925 seconds (Suave)
0.329523 seconds (Divonne)
0.245176 seconds (Cuhre)
INFO: Performance of Cuba Library in C:
0.123311 seconds (Vegas)
0.511874 seconds (Suave)
0.140986 seconds (Divonne)
0.089292 seconds (Cuhre)
INFO: Performance of Cuba Library in Fortran:
0.120000 seconds (Vegas)
0.620000 seconds (Suave)
0.136000 seconds (Divonne)
0.092000 seconds (Cuhre)
Cuba.jl
internally fixes CUBACORES
to 0 in order to prevent from
forking julia
processes that would only slow down calculations
eating up the memory, without actually taking advantage of concurrency.
Furthemore, without this measure, adding more Julia processes with
addprocs()
would only make the program segfault.
Development¶
Cuba.jl
is developed on GitHub: https://github.com/giordano/Cuba.jl. Feel
free to report bugs and make suggestions at
https://github.com/giordano/Cuba.jl/issues.
License¶
The Cuba.jl package is licensed under the GNU Lesser General Public License, the same as Cuba library. The original author is Mosè Giordano.
Credits¶
If you use this library for your work, please credit Thomas Hahn. Citable papers about Cuba Library:
- Hahn, T. 2005, Computer Physics Communications, 168, 78. DOI:10.1016/j.cpc.2005.01.010. arXiv:hep-ph/0404043. Bibcode:2005CoPhC.168...78H.
- Hahn, T. 2015, Journal of Physics Conference Series, 608, 012066. DOI:10.1088/1742-6596/608/1/012066. arXiv:1408.6373. Bibcode:2015JPhCS.608a2066H.