Work

Practical error estimates for sparse recovery in linear inverse problems

Year: 2009

Type: preprint

Abstract: The effectiveness of using model sparsity as a priori information when solving linear inverse problems is studied. We investigate the reconstruction quality of such a method in the non-idealized case and compute some typical recovery errors (depending on the sparsity of the desired solution, the number of data, the noise level on the data, and various properties of the measurement matrix); they are compared to known theoretical bounds and illustrated on a magnetic tomography example.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.0908.3636

Authors: Loris, Ignace, Verhoeven, Caroline

Institution: Vrije Universiteit Brussel

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Fonds Wetenschappelijk Onderzoek, Vlaamse regering

Award: Applications of sparsity to the regularization of ill-posed inverse problems in physics

OpenAlex ID: W1484431794

Cites: 20

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Related to: 20