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maximizes the inner problem and solves (17).
The crux of our approach is to construct a tight convex relaxation of the inner problem.
Then, for the rate assignment that maximizes the inner problem has to hold (from (26)).
Assume for a given (31). is the solution of inner problem, obtained by (30).
In this way, we provide a tight convex relaxation of our inner problem. .
In this way, we provide a tight convex relaxation of our inner problem.
The inner problem can be solved by each base station individually as we will see shortly.
Note that we focus on the inner problem, where the traffic is the most restrictive factor, therefore, in this case.
A rate allocation that maximizes the inner problem can be calculated directly for a given independently of and.
From the inner problem we can get the optimal charging and discharging process of the energy storage system.
A dynamic model in the inner problem captures the network dynamics and enables temporal regulation of the metabolic network.
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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