On a near optimal sampling strategy for least squares polynomial regression

DOI10.1016/j.jcp.2016.09.032zbMath1384.62249OpenAlexW2520065071MaRDI QIDQ1674720

Yeonjong Shin, Dongbin Xiu

Publication date: 26 October 2017

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2016.09.032

zbMATH Keywords

polynomial regressionleast squaresChristoffel function

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Approximation by polynomials (41A10) Algorithms for approximation of functions (65D15)

Related Items

Special issue: Predictive multiscale materials modeling ⋮ Basis adaptive sample efficient polynomial chaos (BASE-PC) ⋮ Pluripotential numerics ⋮ A preconditioning approach for improved estimation of sparse polynomial chaos expansions ⋮ A multi-fidelity polynomial chaos-greedy Kaczmarz approach for resource-efficient uncertainty quantification on limited budget ⋮ Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark

Uses Software

• TESTPACK

Cites Work

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