Sentence examples for convergence of the value from inspiring English sources

The importance of strong convergence is underlined in [16], where a convex function f is minimized via the proximal-point algorithm: it is shown that the rate of convergence of the value sequence { f ( x n ) } is better when { x n } converges strongly than when it converges weakly.

The important of strong convergence is also underlined in [22], where a convex function f is minimized via the proximal point algorithm: it is shown that the rate of convergence of the value sequence { f ( x n ) } is better when { x n } converges strongly that it converges weakly.

The importance of strong convergence is also underlined in [7], where a convex function f is minimized via the proximal-point algorithm: it is shown that the rate of convergence of the value sequence { f ( x n ) } is better when { x n } converges strongly than when it converges weakly.

Delphi surveys allow a range of individuals to express their opinion which can then be re-assessed by considering the input from other participants, with the aim of reaching some convergence on the values of interest.

Low Bayes factors are a sign for high convergence of the values and compatibility of the inferences, while high Bayes factors indicate incompatibility.

To show the convergence of RankCost, the values of loss function during the learning process on four data sets are collected and presented in Figure 3. First, we can empirically conclude that the loss function defined in Eq. (4) is convex.

The results showed that the convergence of the mean value of the maximum strain improved gradually as a function of the number of simulations and it stabilised around a value of 0.008 after 6200 simulations.

By using the method of quasilinearization, in [13], the authors investigated the uniform and quadratic convergence of the initial value problem for singular differential systems.

The same termination conditions are used for the SA algorithm, and adding surface geophones improves the convergence of the DDrms value, as shown in Fig. 8.

The graph shows the convergence of the z value of a typical point on a flat sample area (point A) and close to a steep slope (point B) if the iterative refinement process is not stopped by the precision criterion.

Unlike other previous works, with additional optimization ability in the simulation model employed in this study, it is hoped that a faster solving time for finding convergence of the objective value can be achieved.