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Theorem 3. The objective of problem (63) can achieve its minimum when the relay filter and the filter are constructed as follows: (68).
If either x ∗ T B x ∗ or x ∗ T D x ∗ is zero, then the functions involved in the objective of problem (P) are not differentiable.
The objective of problem (32) can achieve its minimum when the BS filter and the relay filter are constructed as follows: (33).
In the inner loop of Algorithm 3, each subcarrier is successively adjusted among users to improve the objective of problem (2).
While the objective of Problem STATIC is concave in, the objective of Problem OPT is not necessarily concave in P. Perhaps the easiest way to verify this is to consider the case when G has two nodes connected by an edge, and there are C = 2 channels both of which are available to the two nodes.
The objective of problem (49) is a log-concave function of { a R 2 } ℓ = 1 L. Applying the logarithmic function to the objective of (49), we obtain ln ∏ n = 1 K ∏ ℓ = 1 L e − Q β n / γ n, a n = − Q ∑ n = 1 K ∑ ℓ = 1 L β n γ n, a n = − Q ∑ n = 1 K ∑ ℓ = 1 L β n ( a R 2 δ n + ψ n a R 2 ϕ n , (50).
Similar(53)
In the following we present an algorithm that computes ⪯ C s -robust solutions while varying the weights in the vector of objectives of problem ( P ( U ) y ∗ biobj ).
Moreover, at any of these solutions the objective function of problem (4) will be exactly the same as the objective function of problem (2).
In other words, the optimal objective value of problem (AP) is less than or equal to the optimal objective value of problem (DCLFP).
The objective function of Problem 1 can be rewritten as.
The objective function of problem (13) is upper bounded by that of the following problem.
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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