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The aerodynamics of closed wings up to stall can be approximated by linear functions and their derivatives.
Although scatter is somewhat large, the S N data could be approximated by linear lines in double logarithmic plots for both loading conditions up to the giga cycle regime.
Referring to [48], Eq. (22) can be approximated by linear programming.
In some range, exponential function can be approximated by linear function.
Thus, if the set of solutions can be approximated by linear subspaces with an exponentially decaying error term, then the POD-Greedy algorithm will in fact find an approximation with an exponentially decaying error term, though possibly with suboptimal parameters in the error bound.
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The previous algorithm was based on the following approximations: (i) gene expression dynamics were approximated by linear ordinary differential equations (ode); and (ii) the system was treated as deterministic by considering only the mean experimental data for the analysis.
Nonconvex/convex matrix inequalities obtained from the H∞ norm condition for each frequency are approximated by linear matrix inequalities, respectively.
In this approach, the necessary conditions of optimality (NCO) are approximated by linear combinations of available measurements over the entire operation region.
We discuss the approach on an illustrative example where the Redlich-Kwong equation of state is approximated by linear surrogate models.
Missing daily values of runoff between October and April were approximated by linear interpolation.
The data were approximated by linear dependences in the coordinates lgL on lg[HL] using the least squares method.
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