A Christoffel function weighted least squares algorithm for collocation approximations

Abstract

We propose, theoretically investigate, and numerically validate an algorithm for the Monte Carlo solution of least-squares polynomial approximation problems in a collocation frame- work. Our method is motivated by generalized Polynomial Chaos approximation in uncertainty quantification where a polynomial approximation is formed from a combination of orthogonal polynomials. A standard Monte Carlo approach would draw samples according to the density of orthogonality. Our proposed algorithm samples with respect to the equilibrium measure of the parametric domain, and subsequently solves a weighted least-squares problem, with weights given by evaluations of the Christoffel function. We present theoretical analysis to motivate the algorithm, and numerical results that show our method is superior to standard Monte Carlo methods in many situations of interest.

29 pages, 11 figures

References in corpus (3)

• Coherence Motivated Sampling and Convergence Analysis of Least-Squares Polynomial Chaos Regression
• Stochastic collocation on unstructured multivariate meshes
• Bergman kernels for weighted polynomials and weighted equilibrium measures of C^n

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