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The equivalence between these two objective functions is stated and proved as Theorem 1.
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He did not always manage to achieve the delicate balance that he sought between these two objectives.
However, the inherent competition between these two objectives often leads to instability and reduces productivity.
The conflict between these two objectives suggested employing a multiobjective optimisation technique.
While they're not always at loggerheads, documents obtained by Guardian Australia show that IHMS clearly sees the potential for conflict between these two objectives.
Pareto frontiers, which define the optimal tradeoffs between these two objectives, are generated for six scenarios constructed from recent historical data from West Texas, the United Kingdom, and India.
Unfortunately, optimal marketing actions to maximize profits often perform poorly in minimizing the defection probability due to a conflict between these two objectives.
Two strategies for solving the multi-objective problem are investigated: weighted average and constrained optimisation, and the latter is recommended for the ease in determining the balance between these two objectives.
Moreover, to reveal the level of conflict between these two objectives, the distribution of each design variables in their allowable range is also shown in two-dimensional objective spaces.
Since there is a trade-off between these two objectives, formal multiobjective optimization techniques are required to establish the optimal Pareto solutions that can then be used for decision-making purposes.
These analyses allow for the identification of trade-offs between these two objectives, while the methods proposed can similarly be applied to the two key societal objectives of energy production and conservation, thus providing information that could be valuable to stakeholders and policymakers when making decisions on array design.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com