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Based on (8), the formulation of the problem can be rewritten as max F d min x ̆ ∈ X ̆ d x ̆ 2 = x ̆ ∗ F d ∗ H v ∗ H v F d x ̆. (13).
From (8) and (10), the problem in (13) can be rewritten as max h r h r H R t h r h r H R c + R z h r. (14).
In this case, problem (34) can be rewritten as max p ∈ P ∑ m = 1 N m w m r m subject to ∑ m = 1 N m ∑ b = 1 N b p m, b ≤ P T. (44).
Then, based on the first-order Taylor expansion, the objective function in (9) can be rewritten as max U P c, S c = ∑ k = 1 K c U k ′ R ̄ k ( t - 1 ) R k ( t ), (11).
When considering the so-called FC, the unified optimization problem in (34) can be rewritten as max p ∈ P ∑ m = 1 N m w m min N sc ∑ b = 1 N b ρ m, b, Q m N o T o subject to ∑ m = 1 N m ∑ b = 1 N b p m, b ( t ) ≤ P T. (60).
The conditions | d(p k, x i ) - d(p k, x j )| < ε can be rewritten as max k = 1 q | d i k − d j k | < ε.
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Thus, the optimization problem is rewritten as max ∑ i = 1 m s i · ∑ j ∈ J i r ij x ij - c · ∑ j ∈ J i ∩ J i - 1 r ij ( x ij - z ij ) + ∑ j ∈ J i - J i - 1 r ij x ij (7).
Condition (5.4) can be rewritten as (5.7).
then Theorem 2.8 can be rewritten as follows, let and.
After setting P to P m, problem (11) can be rewritten as follows: max A, B R P m, A, B s.t.
The optimization problem can be rewritten as follows max P k i u k s.t P k ∈ X k, com (6).
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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