2005
DOI: 10.1016/j.cpc.2005.01.010
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Cuba—a library for multidimensional numerical integration

Abstract: The Cuba library provides new implementations of four general-purpose multidimensional integration algorithms: Vegas, Suave, Divonne, and Cuhre. Suave is a new algorithm, Divonne is a known algorithm to which important details have been added, and Vegas and Cuhre are new implementations of existing algorithms with only few improvements over the original versions. All four algorithms can integrate vector integrands and have very similar Fortran, C/C++, and Mathematica interfaces.

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Cited by 896 publications
(804 citation statements)
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References 10 publications
(12 reference statements)
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“…iHixs evaluates the contribution to the cross-section in NNLO QCD and includes important electroweak effects. A detailed description of the theoretical contributions [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] which are incorporated and accounted for in iHixs can be found in the corresponding publication [5].…”
Section: Introductionmentioning
confidence: 99%
“…iHixs evaluates the contribution to the cross-section in NNLO QCD and includes important electroweak effects. A detailed description of the theoretical contributions [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] which are incorporated and accounted for in iHixs can be found in the corresponding publication [5].…”
Section: Introductionmentioning
confidence: 99%
“…We calculated them (for q 2 = −1) using FIESTA with the Cuba [16] Vegas integrator and 1 500 000 sampling points for integration. Our results alongside with the corresponding analytical expressions (transformed to the numerical form) from [13] Here for each MI we provide our numerical result for coefficients of ε-expansion in comparison (in parentheses) with the known from [13] analiycal results (if any).…”
Section: Numerical Results For Four-loop Massless Propagatorsmentioning
confidence: 99%
“…The value I of (7) does not depend on ρ, but the corresponding variance of (8) does, so we may minimise the variance (8) by varying the function ρ subject to the constraint that it is correctly normalised N(ρ) ≡ dx ρ(x) = 1. Performing the required functional differentiation with the Lagrange multiplier λ…”
Section: Importance Samplingmentioning
confidence: 99%