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The results of the proposed DVS algorithm are compared with results provided by the presented algorithms in [27, 38], as the most relevant anchor algorithms.
Moreover, it is often not possible to include in an article all the necessary resources, such as data, which were processed by the presented algorithms.
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The iterative methods are followed by convergence theorems and the presented algorithms are tested by solving some numerical examples.
We calibrate the presented algorithms by means of the Design of Experiments (DOE) approach.
In this model we study the area requirement of planar straight-line grid drawings of trees and we assess the output quality of the presented algorithms by computing the competitive ratio with respect to the best known offline algorithms.
But now we test the hypothesis that the wind speed as random variable has the optimized CDF obtained by the presented algorithm.
The results provided by the presented algorithm in [38] show that it closely tracks the intentional camera movements but at the cost of slightly reduced stabilization capabilities.
In order to document the effect of the reduction of the necessary scanned path, we compare the path length saved by the presented algorithm to the standard matrix-based approach for the surface shown in Figure 3.
The numerical results indicated that the results obtained by the presented algorithm have a good agreement with results produced by MCRT, meanwhile CPU time requirement of the algorithm is about a few seconds, much shorter than that of MCRT.
It is shown that under some mild conditions, the sequence generated by the presented algorithm converges strongly to the common solution of mixed equilibrium problems and fixed points problems.
It is shown that under some mild conditions the sequence { x n } generated by the presented algorithm converges strongly to the common solution of mixed equilibrium problems and fixed points problems.
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