Sentence examples for the sequence of approximation from inspiring English sources

It can be observed that with a higher level of decomposition, the sequence of approximation coefficients becomes more smoothed.

The overall trend of the traffic time series is better highlighted by the sequence of approximation coefficients obtained at the time resolution of 96 h (corresponding to the sixth decomposition level), a6.

Even better approximations were found by using polygons with more sides, but these only served to deepen the mystery, because no exact value could be reached, and no pattern could be observed in the sequence of approximations.

The zeroth order hyperasymptotic approximation has a relative error of O in α; the sequence of approximations may be extrapolated to give the proportionality constant in the limiting asymptotic expression for α as ε ⇒ 0. The first-order hyperasymptotic approximation has a relative error of only O ε2); its extrapolation gives the O correction to the limit as ε ⇒ 0.

The aim of this paper is to show that a completely different phenomenon appears when one considers the analogous problem for three dimensional axisymmetric flows: in such a case, either the sequence of approximations is strongly convergent, or the weak limit is not a solution of the equation.

The sequence obtained by computing the second difference of the sequence of approximations is more stationary than the sequence obtained by computing the first difference of approximations or than the sequence of approximations itself.

Provided that the initial vectors ((tilde{u},tilde{w})) are close enough to ((u,w)) the sequence of approximations ((tilde{u},tilde{w})) converges quadratically to ((u,w)).

Provided that he initial vectors ((tilde{u},tilde{w})) are close enough to ((u,w)), the sequence of approximations ((tilde{u},tilde{w})) converges quadratically to ((u,w)).

The sequences of approximation coefficients for six levels of decomposition are shown in Figure 6. Figure 6 The approximation coefficient sequences.

The trust region method is used to manage the sequence of the approximation optimization problems based on a reliability index.

In the data mining context, the separation of the last sequence of approximation coefficients can be regarded as a data preparation operation.