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- W1710463105 abstract "We investigate the sparse spikes deconvolution problem onto spaces of algebraic polynomials. Our framework encompasses the measure reconstruction problem from a combination of noiseless and noisy moment measurements. We study a TV-norm regularization procedure to localize the support and estimate the weights of a target discrete measure in this frame. Furthermore, we derive quantitative bounds on the support recovery and the amplitude errors under a Chebyshev-type minimal separation condition on its support. Incidentally, we study the localization of the knots of non-uniform splines when a Gaussian perturbation of their inner-products with a known polynomial basis is observed (i.e. a small degree polynomial approximation is known) and the boundary conditions are known. We prove that the knots can be recovered in a grid-free manner using semidefinite programming." @default.
- W1710463105 created "2016-06-24" @default.
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- W1710463105 date "2015-10-01" @default.
- W1710463105 modified "2023-09-26" @default.
- W1710463105 title "Non-uniform spline recovery from small degree polynomial approximation" @default.
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- W1710463105 doi "https://doi.org/10.1016/j.jmaa.2015.05.034" @default.
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