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It reduces the complexity of dealing with many variables by identifying single crossover points of greatest concern, and progressively building understanding through three stages of analysis.
It reduces the complexity of model and improves the performance of model (Miranian and Abdollahzade 2013; Mellit et al. 2013).
It reduces the complexity of the design, development and testing phases because each layer focuses on its own task and it does not have to consider other layers.
It reduces the complexity of the original problem, e.g., the Hubbard model [20] in the thermodynamic limit, by mapping it onto a simpler impurity problem that is subject to a self-consistency condition relating its properties to those of the original model.
It reduces the complexity of images from thousands of pixels to only a few hundred superpixels.
Based on slender-body theory and homogenization it reduces the complexity of the problem enormously and makes numerical simulations possible.
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They provide a relatively general framework for analysis that can be easily applied because it reduces the complexities of culture and its interactions into quantifiable dimensions [ 20, 22, 23].
It reduced the complexity of the available data, and yielded a two-dimensional solution with eigenvalues of 0.879 and 0.752 for the first and second dimension, respectively.
It also reduces the complexity of the high-radix partial multiplications to binary partial multiplications.
It is demonstrated that the proposed algorithm performance is slightly lower than the JSF-WF performance, while it significantly reduces the complexity of the allocation algorithm.
It also reduces the complexity of partitioning data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com