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In this paper, we extend our previous work from [31, 32] in two specific ways: (i) we extend the network source model used from exactly sparse to near-sparse signals, and (ii) we provide a detailed mathematical and numerical justification of the usage of sparse recovery algorithms (including a bound on the reconstruction error) for this source model.
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The obtained results by the use of steady state probability approximation method provide a numerical justification for the aforementioned simplification.
Through quantitative measurements, we can establish numerical justifications.
Here, a case study is fully detailed, including the aspects of historical, damage and geometric investigations, of advanced numerical analysis, of justification of remedial measures and of detailing the adopted strengthening.
Then, a case study is fully detailed, including the aspects of survey, advanced numerical analysis, diagnosis, justification of remedial measures and detailing of the adopted strengthening.
It is possible that this is not feasible for numerical reasons, but some justification of the approach would be useful.
Numerical results have been presented for justification of theoretical results.
To explain the algorithm, we first describe a numerical example and then present the formal justification of the method.
We give analytic justification of the model and propose a series of numerical experiments studying the influences of the various sources of errors between the quantum and the kinetic models.
Justification of knowledge relates to how mathematical claims are warranted.
His justification of Prop.
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