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Practical algorithms for multivariate rational approximation
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DOI：10.1016/j.cpc.2020.107663.png)
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Abstract
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We present two approaches for computing rational approximations to multivariate functions, motivated by their effectiveness as surrogate models for high-energy physics (HEP) applications. Our first approach builds on the Stieltjes process to efficiently and robustly compute the coefficients of the rational approximation. Our second approach is based on an optimization formulation that allows us to include structural constraints on the rational approximation (in particular, constraints demanding the absence of singularities), resulting in a semi-infinite optimization problem that we solve using an outer approximation approach. We present results for synthetic and real-life HEP data, and we compare the approximation quality of our approaches with that of traditional polynomial approximations. Published by Elsevier B.V.
Keywords:
Discrete least-squares
Multivariate rational approximation
Semi-infinite optimization
Surrogate modeling
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