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These conventions will prove to be sufficient to convert Problem 3 into another formulation of Problem 2, now as a minimization problem.
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The variational formulation of Problem I is as follows.
We can show that problem (3.1) and (3.2) is an abstract formulation of problem (3.27).
Then, we extend the formulation of problem P1 to derive the distributed algorithm for problem P2.
Using the bilinear mappings and, we construct the weak formulation of problem (1.1) as follows: (3.5).
end{aligned} Now we give the backward formulation of problem (4.2).
For the numerical simulation, we choose T = 1 and use an alternative formulation of problem (1a) and (1b), (36a).
We mention that the complete formulation of problem ((mathcal{EP})) corresponds to a boundary value problem, in the unknown z, described by an elliptic differential equation in Ω.
In the following, we also provide an SDP formulation of problem (19): begin{array}{*{20}l} min_{t_{mathrm{s}},mathbf{b}} & ~~tau_{mathrm{s}} end{array} (20a).
In this regard, note that in the general formulation of Problem (17), the complete data structure is computed for every tree node s.
The Lagrange dual optimization formulation of problem (5) ~ (7) becomes: (8) s.t.
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