A preconditioning approach for improved estimation of sparse polynomial chaos expansions

Abstract

Compressive sampling has been widely used for sparse polynomial chaos (PC) approximation of stochastic functions. The recovery accuracy of compressive sampling highly depends on the incoherence properties of the measurement matrix. In this paper, we consider preconditioning the underdetermined system of equations that is to be solved. Premultiplying a linear equation system by a non-singular matrix results in an equivalent equation system, but it can potentially improve the incoherence properties of the resulting preconditioned measurement matrix and lead to a better recovery accuracy. When measurements are noisy, however, preconditioning can also potentially result in a worse signal-to-noise ratio, thereby deteriorating recovery accuracy. In this work, we propose a preconditioning scheme that improves the incoherence properties of measurement matrix and at the same time prevents undesirable deterioration of signal-to-noise ratio. We provide theoretical motivations and numerical examples that demonstrate the promise of the proposed approach in improving the accuracy of estimated polynomial chaos expansions.

References in corpus (4)

• Compressive Sampling of Polynomial Chaos Expansions: Convergence Analysis and Sampling Strategies
• Coherence Motivated Sampling and Convergence Analysis of Least-Squares Polynomial Chaos Regression
• A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions
• A Near-Optimal Sampling Strategy for Sparse Recovery of Polynomial Chaos Expansions

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