Stable soft extrapolation of entire functions. (7th December 2018)

• Record Type: Journal Article
• Authors: Batenkov, Dmitry; Demanet, Laurent; Mhaskar, Hrushikesh N
• Is Part Of: Inverse problems. Volume 35:Number 1(2019)
• Publication Date: 2018-12-07
• Subjects: extrapolation -- entire functions -- analytic continuation -- optimal recovery -- bandlimited functions -- deconvolution; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/aaedde
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: Soft extrapolation refers to the problem of recovering a function from its samples, multiplied by a fast-decaying window and perturbed by an additive noise, over an interval which is potentially larger than the essential support of the window. To achieve stable recovery one must use some prior knowledge about the function class, and a core theoretical question is to provide bounds on the possible amount of extrapolation, depending on the sample perturbation level and the function prior. In this paper we consider soft extrapolation of entire functions of finite order and type (containing the class of bandlimited functions as a special case), multiplied by a super-exponentially decaying window (such as a Gaussian). We consider a weighted least-squares polynomial approximation with judiciously chosen number of terms and a number of samples which scales linearly with the degree of approximation. It is shown that this simple procedure provides stable recovery with an extrapolation factor which scales logarithmically with the perturbation level and is inversely proportional to the characteristic lengthscale of the function. The pointwise extrapolation error exhibits a Hölder-type continuity with an exponent derived from weighted potential theory, which changes from 1 near the available samples, to 0 when the extrapolation distance reaches the characteristic smoothness length scale of the function. The algorithm is asymptotically minimax, in the sense that there is essentially no better algorithm yielding meaningfully lower error over the same smoothness class. When viewed in the dual domain, soft extrapolation of an entire function of order 1 and finite exponential type corresponds to the problem of (stable) simultaneous de-convolution and super-resolution for objects of small space/time extent. Our results then show that the amount of achievable super-resolution is inversely proportional to the object size, and therefore can be significant for small objects. These results can be considered as a first step towards analyzing the much more realistic 'multiband' model of a sparse combination of compactly-supported 'approximate spikes', which appears in applications such as synthetic aperture radar, seismic imaging and direction of arrival estimation, and for which only limited special cases are well-understood.