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Now that we have established that maximizing the weighted sum rate is equivalent to the maximization of a wide set of utility functions, we focus on methods that allow to achieve this goal.
Another popular DSM design is the maximization of a weighted sum of data rates, as follows maximize s ∈ S ∑ n ∈ N w n R n subject to P n ≤ P n, tot, n ∈ N, (7). in which the weights w = [w1,..., w N ] are used to give more priority to some users with respect to the other users.
Our CGP method also supports the maximization of a WSR using SZF.
Second, our proposed method also enables the maximization of a weighted sum rate.
Since it involves maximization of a convex function, (31a) is not a convex optimization problem.
This inverse problem is solved using the maximization of a fourth-order statistical criterion of the backpropagated pressures.
The objective in clutch design can be stated as maximization of a maintainable friction coefficient and minimization of wear.
Mathematically, optimization involves the minimization or maximization of a given function subject to a number of constraints.
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It is based on the maximization of an objective function called network modularity [50, 51].
We formulate personalization of the steering parameters as the maximization of an expected utility function.
Process optimization involves the minimization (or maximization) of an objective function, that can be established from a technical and (or) economic viewpoint taking into account safety of process.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com