Sentence examples for repeating computation from inspiring English sources

Remark 3.1 The system of equations (16) or (17) has no repeating computation and is different from the existing POD-based reduced-order numerical computational methods (see, e.g., [14 20, 22 31]) based on POD technique.

To avoid repeating computation of tensors [ Eq. (40)–(43)], we store them in a file.

Thus, its computing load is by a factor of 100 times larger than that in this article and its truncation error accumulation in the computational process is increased greatly; as well we have repeating computations of the classical FD scheme with first-order time accuracy on [ 0, t N ].

Especially, it has no repeating computations and uses the given solutions on the first fewer M time steps for Problem III to extrapolate other ((n-M)) solutions, which is completely different from existing reduced-order approaches (see, e.g., [14 35] etc).

Though a POD-based reduced-order FD scheme with first-order time accuracy without adopting the extrapolation technique has been developed for NSIBEs (see [17]), it is also to test and verify only the comparison of solutions on the same time span [ 0, t N ] and it belongs to repeating computations.

The POD-based reduced-order FD extrapolating model with fully second-order accuracy here utilizes the given data (on the very short time span [ 0, t 0 ] and t 0 ≪ t N ) to predict future physic phenomena (on time span [ t 0, t N ] ) and has no repeating computations.

However, almost all existing POD-based reduced-order numerical methods employ the numerical solutions obtained from classical numerical methods on the total time span [ 0, t N ] to construct the POD basis and POD-based reduced-order models, and then recompute the solutions on the same time span [ 0, t N ], which is actually belong to repeating computations.

However, almost all existing POD-based reduced-order numerical methods (see, e.g., [14 35]) employ numerical solutions obtained from classical numerical methods on the total time span ([0,T]) to form POD bases and establish reduced-order models, and then recompute the solutions on the same time span ([0,T]), which actually entails repeating computations on the same time span ([0,T]).

In the following section we introduce an algorithm to avoid repeating computations by storing intermediate results in multidimensional tables called anterior and posterior cutsets.

Now it is convenient to introduce the posterior cutset which will be used to avoid repeating computations in calculating genotype probabilities.

With this CDAC method, the on-the-fly DRGEP process as well as the chemistry integration only needs to be conducted at the cluster level, dramatically reducing the unnecessary repeated computation for similar computational cells.