Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs
1 Institut Universitaire de France,
UPMC Univ Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions,
2 MATHICSE-CSQI, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
3 Applied Mathematics and Computational Sciences, and SRI Center for Uncertainty Quantification in Computational Science and Engineering, KAUST, 23955-6900 Thuwal, Saudi Arabia.
Revised: 20 July 2014
Motivated by the numerical treatment of parametric and stochastic PDEs, we analyze the least-squares method for polynomial approximation of multivariate functions based on random sampling according to a given probability measure. Recent work has shown that in the univariate case, the least-squares method is quasi-optimal in expectation in [A. Cohen, M A. Davenport and D. Leviatan. Found. Comput. Math. 13 (2013) 819–834] and in probability in [G. Migliorati, F. Nobile, E. von Schwerin, R. Tempone, Found. Comput. Math. 14 (2014) 419–456], under suitable conditions that relate the number of samples with respect to the dimension of the polynomial space. Here “quasi-optimal” means that the accuracy of the least-squares approximation is comparable with that of the best approximation in the given polynomial space. In this paper, we discuss the quasi-optimality of the polynomial least-squares method in arbitrary dimension. Our analysis applies to any arbitrary multivariate polynomial space (including tensor product, total degree or hyperbolic crosses), under the minimal requirement that its associated index set is downward closed. The optimality criterion only involves the relation between the number of samples and the dimension of the polynomial space, independently of the anisotropic shape and of the number of variables. We extend our results to the approximation of Hilbert space-valued functions in order to apply them to the approximation of parametric and stochastic elliptic PDEs. As a particular case, we discuss “inclusion type” elliptic PDE models, and derive an exponential convergence estimate for the least-squares method. Numerical results confirm our estimate, yet pointing out a gap between the condition necessary to achieve optimality in the theory, and the condition that in practice yields the optimal convergence rate.
Mathematics Subject Classification: 41A10 / 41A25 / 65N35 / 65N12 / 65N15 / 35J25
Key words: Approximation theory / polynomial approximation / least squares / parametric and stochastic PDEs / high-dimensional approximation
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