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The robust optimization method optimizes the performance while at the same time maximizing its belief value.
The second method optimizes the trade-off between low cost production and high utility value of the energy.
The suggested greedy search method optimizes the magnitude response and the phase response simultaneously and also finds the lowest order of the filter.
The method optimizes the sampling scheme for a defined spatial population based on selected covariates, which are assumed to represent the variability of the target variables.
Coming from the industry, this method optimizes the number of tests to be performed by methodologically varying several parameters at the same time.
The proposed method optimizes the construction of radial basis function (RBF) networks, based on a cooperative particle swarm optimization (CPSO) framework.
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Thus the method optimized the contrast resolution by adaptively selecting the samples of the excitation.
This method optimized the allocation of renewable/non-renewable units and smart metering units simultaneously to minimize total carbon emissions cost.
Based on our experimental analyses using the RF and triplet supertree method, optimizing the supertree based on different distance measures can result in very different supertrees (Table 1).
These Runge Kutta methods optimize the time step necessary for stable solutions, while holding dispersion and dissipation fixed.
Alternatively, we proposed several transductive methods, optimizing the combining rule for objective functions measured on the test batch.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com