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Before proceeding with the examples, we would like to highlight a fact regarding the relationship between the cost function and the matrix K ̃.
Figure 7 shows the relationship between the cost function and varying normalized utility of both players versus the number of primary users.
Furthermore, the impact on the mean number of evaluated levels is not very significant: we note an increase of about 7% between the cost function TP_cost and the two others.
In this section, we address the mapping between the cost function and the stationary probability used in MCMC, the technique of slice sampling method and their application to the image transmission problem.
The results show that the relationship between the cost function and the production time can have a signification effect on the economical production quantity and the average amount of the total cost in EPQ classic model.
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Therefore, it is not necessary to consider trade-offs between different aspects of the cost function that may create many different local minima.
To tackle this effect, the final depth estimate is taken as the minimum point of quadratic polynomial which approximates the cost function between three discrete depth candidates: d, d - 1 and d + 1 f ( d ) = a d 2 + b d + c, (17) d min = - b 2 a. (18).
In the presence of a focal lesion, standard warping with SyN may seem inappropriate as the assumption of a one-to-one mapping between the source and the template images is violated because of the abnormal shapes and intensity values of the focal lesion that cause a mismatch between both images and bias the cost function being optimized substantially.
Here, we use the G1/S model to show the differences of the cost function surfaces between NLP-P0 and NLP-P3: in this case, the cost function of P0 is a highly irregular and complicated manifold with multiple local minima; the augmented cost function adopted in problem P3 is a much "smoother" function and hence it is easier for the NLP algorithm to converge to the solution.
This uses as the cost function the difference between the calculated and observed X-ray or neutron diffraction pattern of the glass as described in section 12.
Then, the cost function for a link between sensor node and can be estimated as (4).
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Justyna Jupowicz-Kozak
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