Sentence examples for the parallel iteration from inspiring English sources

There has been a lot of study (see [1 13]) on the parallel iteration methods for solving the large sparse system of linear equations (1.1).

It is shown experimentally, that the QR factorization with the complete column pivoting, optionally followed by the LQ factorization of the R-factor, can lead to a substantial decrease of the number of outer parallel iteration steps, whereby the details depend on the condition number and on the distribution of singular values including their multiplicity.

Best results in numerical experiments performed on the cluster of personal computers were achieved for well-conditioned matrices with a multiple minimal singular value, where the number of parallel iteration steps was reduced by two orders of magnitude.

In this case, the recommended Jacobi method with ℓ=2 is just the ScaLAPACK routine with some additional matrix multiplications, and it computes the SVD in one parallel iteration step.

Such a concentration should hopefully lead to fewer outer parallel iteration steps needed for the convergence of the entire algorithm.

The zero pattern in weighting matrices is determined in advance, while the non-zero entries of weighting matrices are determined by finding the optimal solution in a hyperplane of α points generated by the parallel multisplitting iterations.

They also proved that the three-step iterative process leads to highly parallel iterations under certain conditions.

However, it is generally recognized that the cyclic algorithm (like the Gauss-Seidel iteration) is faster than the parallel algorithm (like Jacob iteration).

The departure point for the following iteration of the parallel optimization is the point s * k which gave the minimum value of the objective function.

We set up a toy experiment with synthetic data to compare the results of the parallel and sequential mean-field iteration.

The departure point for the following iteration of the parallel optimization is the point s * k which gave the minimum value of the objective function Stopping conditions if the values of the objective function in s * k-1 and s * k differ less than 5%%, or if the two processors do not find any better solution than the initial one.