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It can be shown that for a given set of Lagrange multipliers, the subproblems SP1, SP2, and SP3 are constrained integer optimization problem, which is equivalent to the optimal matching problem in the bipartite graph theory, thus can be solved by applying the classical algorithms such as modified K-M algorithm [24].
Both subproblems are constrained integer optimization problem, which can be transformed as an optimal matching problem in the bipartite graph and solved based on the modified K-M algorithm.
The latter research [40] solved the optimal matching problem among services using an exhaustive method.
Variational method reduced the optimal matching problem to the solution of a set of functional equations for the amplitude and phase of the wavelet spectrum.
Figure 3 shows the bipartite graph optimal matching problem of Eq. (21) with D A ={1,2,…,M 1}, where M 1 is the maximum number of D2D pairs allowed to access the cellular spectrum.
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If H is an optimal matching, then the optimization problem formulated in (48) is solved.
Once determined, using the weights in the optimal assignment in the matching problem will yield an annotation of the p cells in an input worm.
If H is an optimal matching of G0, the optimization problem is solved and the optimal user association strategy can be obtained correspondingly.
These naturally lead to optimal matching and exact covering problems.
The utItisy funchallengingdepends on toe number of data pointsolvea cell—adding a new point to an existing cell will always increase its utility.
In recent years, we have witnessed rapid advances in the development of methods for approximate and optimal solutions to the protein structure matching problem.
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