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The quality of each network and regression model was measured using coefficients of determination, relative approximation errors (RAE), and root mean square errors (RMS).
More specifically, we show that the existence of a polynomial-time relative approximation algorithm for major classes of problem instances implies that NP ⊆ P. We present our proof and explore the implications of the result.
Factual Place Rank is a relative approximation of the place's significance according to its electronic footprint; it is supported as a sort option by default.
Figure 2 The necessary number of sample as function of the relative approximation error.
The results are shown in Figs. 1 and 2. Fig. 1 Relative approximation error vs. estimation of sparsity in the SP and CoSaMP algorithms for Gaussian matrix.
The relative approximation error defined by begin{array}{*{20}l} frac{|mathbf{x}^{ast}-mathbf{x}|_{2}}{|mathbf{x}|_{2}}, end{array} (28).
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A modified reconstitution procedure is used to uncover the relative approximations used in these second order solutions.
The relative intensity approximation root mean square (RMS) error and approximation errors' cross correlation have been used as performance estimation criteria.
Consequently, the partitioning into loops and stems must be seen as only an relative coarse approximation to model rRNA sequences.
This approach provides relative accurate approximations of the true cluster centers.
Our second reduction nicely complements the first one by showing that any LP-relative ρ-approximation for the problem of finding a min-cost solution for a set of players yields a truthful, no-bossy, (ρ+1 -approximation for the SCM problem (and hence, a truthful (ρ+1 -approximationtion cost-sharing mechanism).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com