Library for multidimensional numerical integration with four independent algorithms: Vegas, Suave, Divonne, and Cuhre.



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Cuba.jl is a library for multidimensional numerical integration with different algorithms in Julia.

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. Feel free to report bugs and make suggestions at https://github.com/giordano/Cuba.jl/issues.

All algorithms provided by Cuba library are supported in Cuba.jl:

  • vegas (type: Monte Carlo; variance reduction with importance sampling)
  • suave (type: Monte Carlo; variance reduction with globally adaptive subdivision + importance sampling)
  • divonne (type: Monte Carlo or deterministic; variance reduction with stratified sampling, aided by methods from numerical optimization)
  • cuhre (type: deterministic; variance reduction with 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 performed on the n-dimensional unit hypercube $[0, 1]^n$. 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 $[0, 1]^n$. For example, recall that in one dimension

∫_a^b dx f[x] → ∫_0^1 dy f[a + (b - a) y] (b - a)

where the final (b - a) is the one-dimensional version of the Jacobian. This generalizes straightforwardly to more than one dimension.

Cuba.jl is available for GNU/Linux, Mac OS, and Windows (i686 and x86_64 architectures).


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.


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, ncomp[; keywords...])
suave(integrand, ndim, ncomp[; keywords...])
divonne(integrand, ndim, ncomp[; keywords...])
cuhre(integrand, ndim, ncomp[; keywords...])

These functions wrap the 64-bit integers functions provided by the Cuba library.

The only mandatory argument is:

  • function: the name of the function to be integrated

Optional positional arguments are:

  • ndim: the number of dimensions of the integration domain. Defaults to 1
  • ncomp: the number of components of the integrand. Defaults to 1

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 length ndim
  • the output vector f of length ncomp, used to set the value of each component of the integrand at point x

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

∫_0^1 cos(x) dx = sin(1) = 0.8414709848078965

can be computed with one of the following commands

julia> vegas((x, f) -> f[1] = cos(x[1]))
 1: 0.8414910005259612 ± 5.2708169787342156e-5 (prob.: 0.028607201258072673)
Integrand evaluations: 13500
Fail:                  0
Number of subregions:  0

julia> suave((x, f) -> f[1] = cos(x[1]))
 1: 0.84115236906584 ± 8.357995609919512e-5 (prob.: 1.0)
Integrand evaluations: 22000
Fail:                  0
Number of subregions:  22

julia> divonne((x, f) -> f[1] = cos(x[1]))
 1: 0.841468071955942 ± 5.3955070531551656e-5 (prob.: 0.0)
Integrand evaluations: 1686
Fail:                  0
Number of subregions:  14

julia> cuhre((x, f) -> f[1] = cos(x[1]))
 1: 0.8414709848078966 ± 2.2204460420128823e-16 (prob.: 3.443539937576958e-5)
Integrand evaluations: 195
Fail:                  0
Number of subregions:  2

The integrating functions vegas, suave, divonne, and cuhre return an Integral object whose fields are

integral :: Vector{Float64}
error    :: Vector{Float64}
probl    :: Vector{Float64}
neval    :: Int64
fail     :: Int32
nregions :: Int32

The first three fields are vectors with length ncomp, the last three ones are scalars. The Integral object can also be iterated over like a tuple. In particular, if you assign the output of integration functions to the variable named result, you can access the value of the i-th component of the integral with result[1][i] or result.integral[i] and the associated error with result[2][i] or result.error[i]. The details of other quantities can be found in Cuba manual.

All other arguments listed in Cuba documentation can be passed as optional keywords.

Note: if you used Cuba.jl until version 0.0.4, be aware that the user interface has been reworked in version 0.0.5 in a backward incompatible way.


A more detailed manual of Cuba.jl, with many complete examples, is available at http://cubajl.readthedocs.io/. You can also download the latest PDF version from https://media.readthedocs.org/pdf/cubajl/latest/cubajl.pdf.


Here is an example of a 3-component integral in 3D space (so ndim=3 and ncomp=3) using the integrand function tested in test/runtests.jl:

using Cuba

function func(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])

result = cuhre(func, 3, 3, abstol=1e-12, reltol=1e-10)
println("Results of Cuba:")
for i=1:3; println("Component $i: ", result[1][i], " ± ", result[2][i]); end
println("Exact results:")
println("Component 1: ", (e-1)*(1-cos(1))*sin(1))
println("Component 2: ", (sqrt(pi)*erf(1)/2)^3)
println("Component 3: ", zeta(3))

This is the output

Results of Cuba:
Component 1: 0.6646696797813739 ± 1.0050367631018485e-13
Component 2: 0.4165383858806454 ± 2.932866749838454e-11
Component 3: 1.2020569031649702 ± 1.1958522385908214e-10
Exact results:
Component 1: 0.6646696797813771
Component 2: 0.41653838588663805
Component 3: 1.2020569031595951


Cuba.jl cannot (yet?) take advantage of parallelization capabilities of Cuba Library. Nonetheless, it has performances competitive 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 0.7.0-DEV.363 (commit 6071f1a02e) equipped with an Intel(R) Core(TM) i7-4700MQ CPU. 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:
  0.271304 seconds (Vegas)
  0.579783 seconds (Suave)
  0.329504 seconds (Divonne)
  0.238852 seconds (Cuhre)
INFO: Performance of Cuba Library in C:
  0.319799 seconds (Vegas)
  0.619774 seconds (Suave)
  0.340317 seconds (Divonne)
  0.266906 seconds (Cuhre)
INFO: Performance of Cuba Library in Fortran:
  0.272000 seconds (Vegas)
  0.584000 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:
  0.279524 seconds (Vegas)
  0.581078 seconds (Suave)
  0.327319 seconds (Divonne)
  0.241211 seconds (Cuhre)
INFO: Performance of Cuba Library in C:
  0.115113 seconds (Vegas)
  0.596503 seconds (Suave)
  0.152511 seconds (Divonne)
  0.085805 seconds (Cuhre)
INFO: Performance of Cuba Library in Fortran:
  0.108000 seconds (Vegas)
  0.604000 seconds (Suave)
  0.160000 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.

Related projects

Another Julia package for multidimenensional numerical integration is available: Cubature.jl, by Steven G. Johnson.


The Cuba.jl package is licensed under the GNU Lesser General Public License, the same as Cuba library. The original author is Mosè Giordano. If you use this library for your work, please credit Thomas Hahn (citable papers about Cuba library: http://adsabs.harvard.edu/abs/2005CoPhC.168...78H and http://adsabs.harvard.edu/abs/2015JPhCS.608a2066H).

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