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A multiobjective optimization problem can be solved by optimizing several equations simultaneously or by combining multiple optimization targets.
With these two equalities, (19) can be solved by optimizing the parameter δ only.
Theorem 1 suggests that the wideband DOA estimation problem can be solved by optimizing (26) using existing methods, e.g., OMP.
The above dual problem can be solved by optimizing the transmit power for a fixed set of Lagrange multipliers and updating the Lagrange multipliers iteratively.
The problem formulated by Eq. (6) can be solved by optimizing one parameter at a time while considering the others as being constant.
The problem of Eq. (6) can be solved by optimizing ω i (i∈C k ) on each cell separately as the influence of each cell C k on the objective function in Eq. (6) is independent.
Similar(51)
Then, two optimization problems are solved by optimizing the constraint ellipsoid of the estimation error subject to the underlying protocol.
The resulting problems were solved by optimizing the independent variables with a multi-objective genetic algorithm.
This issue was solved by optimizing both the adhesion of the Ti film to the substrate and the thickness of the Ti film.
This problem was solved by optimizing the synthesis of ZnO.
The above problem can be solved by iteratively optimizing p s with given p p and optimizing p p with given p s.
More suggestions(15)
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