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Then we propose approximation algorithms for this problem.
In this article we propose approximation schemes for solving nonlinear initial boundary value problem with Volterra operator.
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To obtain an implementation friendly architecture, we propose approximations for the mean and variance estimation functions within the algorithm.
Unlike previous work in the literature, which propose approximations for the nonlinear constraints, we present an exact solution methodology using logic-based Benders decomposition.
Previous works, e.g.[8, 14, 15], propose approximations aimed at reducing the communication cost, but, in all aforementioned schemes, processing and sensing at different time scales are still required.
We illustrate the application and advantages of the proposed approximation.
We have carried out extensive computer simulations to assess the validity of the proposed approximation tools.
In addition, the numerical results of our experiments confirm the accuracy of the proposed approximation model.
The proposed approximation is based on the extreme value theory of multivariable functions [24].
According to these results, we see that the proposed approximation follows simulated values well.
In this section, we overview the proposed approximation algorithm for the MCDS problem.
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