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The paper contains mathematical justification of basic facts concerning the Brownian motor theory.
The present paper is devoted to a mathematical justification of this phenomenon.
After a physical and mathematical justification of the proposed formalism, some of its potentialities for n-heptane combustion are presented.
Just the appearance of this term in the homogenized relations provides mathematical justification of the chemical activity of nanostructural materials (see [8] for more detail).
For the sake of brevity, we do not report the mathematical details here, but a mathematical justification of the correction is reported in the Appendix.
The book is addressed to mathematicians interested in optimization and related topics, and also to engineers, control theorists, economists and applied scientists who can find here a mathematical justification of practical procedures they encounter.
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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.
In particular, CS theory provides a mathematical justification for the use of L1-penalized regression to recover sparse vectors of coefficients and highlights the difference between selection of the markers with nonzero coefficients and the fitting of the precise coefficient values.
Full mathematical justification for the derived formula, and derivations of precise estimates for the remainders of the topological asymptotic expansion are provided.
The paper of Elad & Hel-Or 2001 provided an adequate mathematical justification for the Shift and Add method for the simple case of an additive Gaussian noise model.
Finally, we provide the estimates for the remainders of the topological asymptotic expansion and perform a complete mathematical justification for the derived formulas.
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