Sentence examples for same rate of convergence from inspiring English sources

In this article we consider partitioning and nearest neighbor estimates and show that by replacing the boundedness assumption on X by a proper moment condition the same rate of convergence can be shown as for bounded data.

A priori error estimates show that the h-version of the finite element algorithm exhibits the same rate of convergence as it is known for problems with smooth solutions.

On the other hand, for unconditional bases, CTGA has the same rate of convergence as TGA.

If 0 < l < ∞, then it can be said that { a n } and { b n } have the <span class="lh">same rate of convergence.

When (rgeq p), the nonlinear estimator does the same rate of convergence to that of the linear one, i.e., (n^{-frac{sp}{2s+3}}), ignoring the log factor.

One advantage of this method is that the endpoints of time ([0,T ]times[0,T ]), for example, ((t,s )= (0,0 )) and ((t,s )= (T,T )), nearly have the same rate of convergence in some cases.

Convergence studies for a heterogeneous and anisotropic porous medium confirm the same rates of convergence predicted for homogeneous problem with smooth solutions.

In this article we consider the kernel estimate and show that by replacing the boundedness assumption on X by a proper moment condition the same (optimal) rate of convergence can be shown as for bounded data.

Numerical results and convergence studies also show that the present NEM based on local search algorithm possesses the same accuracy and rate of convergence as they are in previous NEM.

It can, in fact, be built as a modification on top of any consistent and stable finite-difference scheme, making its grid convergence uniform in time and at the same time keeping the rate of convergence the same as that of the unmodified scheme.

Based on the results shown in the MSD plot in Figure 5, we may observe that for the same rate convergence, the steady-state value of the MSD produced by the lattice implementation is much smaller than in the case of transversal implementation.