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- Simone Brugiapaglia, Stefano Micheletti, Simona Perotto
- Computers & Mathematics with Applications
- 2015

- Simone Brugiapaglia, Ben Adcock, R. K. Archibald
- 2017 International Conference on Sampling Theory…
- 2017

From a numerical analysis perspective, assessing the robustness of ℓ<sup>1</sup>-minimization is a fundamental issue in compressed sensing and sparse regularization. Yet, the recovery guarantees available in the literature usually depend on a priori estimates of the noise, which can be very hard to obtain in practice, especially when the noise term… (More)

We present a theoretical analysis of the CORSING (COmpRessed SolvING) method for the numerical approximation of partial differential equations based on compressed sensing. In particular, we show that the best s-term approximation of the weak solution of a PDE with respect to an orthonormal system of N trial functions, can be recovered via a Petrov-Galerkin… (More)

- Simone Brugiapaglia, Ben Adcock
- ArXiv
- 2017

Quadratically-constrained basis pursuit has become a popular device in sparse regularization; in particular, in the context of compressed sensing. However, the majority of theoretical error estimates for this regularizer assume an a priori bound on the noise level, which is usually lacking in practice. In this paper, we develop stability and robustness… (More)

- Simone Brugiapaglia, Luca Gemignani
- J. Computational Applied Mathematics
- 2014

In this paper we propose a variation of the Ehrlich–Aberth method for the simultaneous refinement of the zeros of H-palindromic polynomials.

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