Sparse data-driven quadrature rules via ℓ^p-quasi-norm minimization

2020/12/09 by Manucci, Mattia, Aguado, Jose Vicente, Borzacchiello, Domenico
#65D32 #65K05 #65N30 #65R10 #90C05 #FOS: Mathematics #Numerical Analysis (math.NA)

paper · doi:10.48550/arxiv.2012.05264

Abstract

In this paper we show the use of the focal underdetermined system solver to recover sparse empirical quadrature rules for parametrized integrals from existing data, consisting of the values of given parametric functions sampled on a discrete set of points. This algorithm, originally proposed for image and signal reconstruction, relies on an approximated ℓ^p-quasi-norm minimization. The choice of 0

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