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For now, we assume that the effective channel gains g i 2 ρ i 's in G Σ 1 2 are perfectly known at eNB for the purpose of solving this optimization problem.
The problem of finding cliques (a clique, in brief, is a complete subgraph of a graph) of fixed size in a general graph is a part of solving this optimization problem.
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Instead of solving this difficult optimization problem, Gönen et al. estimated kernel weights by using the gating function.
In addition to the limitation of linear combinations, solving this optimization problem is only possible for a small number of kernels and small number of data points.
Using the method of Lagrange multipliers to solve this optimization problem leads to the following update equation: w ( n + 1 ) = w ( n ) + μ X G ( n ) X G T ( n ) X G ( n ) - 1 e G ( n ) (15).
Hence, there is a need for global optimization methods capable of solving this problem efficiently.
The optimization problem needs to evaluate the expected K L divergence repeatedly which requires repeated calibration of the spatially varying parameter, and this significantly increases the computational effort of solving the optimization problem.
A "Two-step RSM-Enumeration" algorithm is employed to efficiently solve this optimization problem of mixed-type variables.
The details of solving the optimization problem in SVM are introduced in [21].
The next section will present the detailed description of solving the optimization problem.
The complexity of the process, however, requires the automation of solving the optimization problem.
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