A stability barrier for reconstructions from Fourier samples

Abstract

We prove that any stable method for resolving the Gibbs phenomenon - that is, recovering high-order accuracy from the first $m$ Fourier coefficients of an analytic and nonperiodic function - can converge at best root-exponentially fast in $m$. Any method with faster convergence must also be unstable, and in particular, exponential convergence implies exponential ill-conditioning. This result is analogous to a recent theorem of Platte, Trefethen & Kuijlaars concerning recovery from pointwise function values on an equispaced $m$-grid. The main step in our proof is an estimate for the maximal behaviour of a polynomial of degree $n$ with bounded $m$-term Fourier series, which is related to a conjecture of Hrycak & Groechenig. In the second part of the paper we discuss the implications of our main theorem to polynomial-based interpolation and least-squares approaches for overcoming the Gibbs phenomenon. Finally, we consider the use of so-called Fourier extensions as an attractive alternative for this problem. We present numerical results demonstrating rapid convergence in a stable manner.

References in corpus (2)

• On the numerical stability of Fourier extensions
• Beyond consistent reconstructions: optimality and sharp bounds for generalized sampling, and application to the uniform resampling problem

Cited by in corpus (2)

• Algebraic signal sampling, Gibbs phenomenon and Prony-type systems
• Beyond consistent reconstructions: optimality and sharp bounds for generalized sampling, and application to the uniform resampling problem