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The real eigenvalues of the problem (Equations 1 to 5) are bounded below.
The eigenvalues of the problem (Equations 1 to 5) consist of the zeros of function ω.
Then, the eigenvalues of the problem (Equations 1 to 5) are analytically simple.
The optimization problem (Equations 24 to 25) is a quadratically constrained quadratic programming (QCQP) problem [13].
Now, we can rewrite the considered problem (Equations 1 to 5) in the operator form as AF = λF.
The paper is organized as follows: we will describe first the problem equations as well as the CFD simulations procedure.
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Then, problem (Equation 1) has one positive solution.
However, the combinatorial optimization problem Equation (17) is NP-hard and intractable.
Furthermore, to guarantee that the optimization problem Equation 19 has solutions, S ≥ l k is required.
By ironing algorithm, we get a feasible solution of the problem (Equation 12).
The problem (Equation 38) is a QCQP problem and can be solved effectively using the CVX toolbox.
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