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In the Rietveld method, the weighted sum of squares residual, S r, between the observed and the calculated intensities of powder diffraction data is minimized in a nonlinear least-squares method.
Six methods have been selected for comparison: the weighted sum method, the weighted product method, VIKOR, TOPSIS, PROMETHEE II, and a procedure based on the PEG-theorem.
In the weighted average method, the weighted sum of nodal contact stresses over all the load cases is regarded as the reference.
Compared with the direct method, the weighted method achieves about 0.2 dB gain at BER = 10−3.
In brief, in WLS method the weighted squared error due to inequality of both sides of (16) is minimized.
This is because, for our proposed method, the weighted SR problem is approximately converted into a source beamforming decoupled one which significantly reduces the computational complexity.
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Hence, in this study, we combine the average method with the weighted method (i.e. the mean of extrinsic information is derived by weighting before transmitting to the next decoder).
Comparing with other adaptive control methods, the weighted average of a few nodes' states is used as target orbit to design controller.
We also introduce two common scalarization methods, the weighted sum scalarization and ϵ-constraint scalarization, to compute robust efficient solutions of uncertain multiobjective linear programs.
Also, we show that two scalarization methods, the weighted sum method and the ϵ-constraint method, can be used to find robust efficient solutions of UMLP with ellipsoidal uncertainty sets and general norm uncertainty sets.
We first propose two 'naive' approaches: the nearest profile method and the weighted profile method, and we finally propose a more sophisticated approach: the bipartite graph learning method.
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