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The game was solved as a standard pursuit-evasion game, using a surrogate function approach.
This optimization problem is solved by majorization minimization method, which iteratively minimizes the surrogate function.
The proposed surrogate function is valid only in the specific ranges of the design variables.
In this case, we are using a surrogate function approach, which gives an approximation of the solution.
We next introduce the surrogate function for the term y i T C i - 1 y i.
In this work, we deepen the theoretical bases for our approach and we also solve the capacity game posed without using the surrogate function approach.
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There are many surrogate functions (also called proxies) that might be of use.
The weighted singular value thresholding problem gains a closed form solution because of the special properties of nonconvex surrogate functions.
The stochastic collocation scheme is employed for stochastic variables whereas Kriging based surrogate functions are employed for the cost function.
Due to the special properties of nonconvex surrogate functions, the algorithm iteratively has a closed form solution to solve a weighted singular value thresholding problem.
The results of the 1-D model enable the construction of surrogate functions for the thermal resistance and the pressure drop of the heat sink, which are considered as the objective functions for the multi-objective optimization through a genetic algorithm that leads to the optimal geometric parameters.
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