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The importance of minimization as a strategy is highlighted, and both host-based and network-based protections are introduced.

However, timetable models in literature do not typically consider energy minimization as a goal, and punctuality requirements under uncertainty.

We model the F(R C G, t C G ) minimization as a nonlinear optimization problem and figure it out by using the Levenberg Marquardt method [19, 20].

There is lot of chances that the optimization model could have assigned smaller pipe sizes to almost all the redundant pipes in the looped network in view of cost minimization as a prime objective.

Since the optimization is met with equality for fixed ({gamma _{i,j}}left (q right),{bar lambda _{2,i}},{{mathbf {w}}_{i,j}}left (q right)), reversal of the SINR constraint and the reversal of the C 1 minimization as a maximization over ({bar lambda _{1,i,j}}left (q right)) do not change the optimization problem.

Quite a lot research has been carried out in this regard in which for most of the proposed algorithms, kriging variance minimization as a criterion for uncertainty assessment is defined as objective function and the problem could be solved through optimization methods.

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Computational complexity of the stress minimization as an optimization problem using the SMACOF algorithm is roughly (O(n^{4})) for reaching at a local, approximate solution.

As caution, we note some of the challenges associated with analysis via the BiFold framework: Computational complexity of the stress minimization as an optimization problem using the SMACOF algorithm is roughly (O(n^{4})) for reaching at a local, approximate solution.

The results show that the consideration of CO2 emission minimization as an objective function during the GSCND process together with techno-economic criteria produces improved FSC environmental performance compared to both organic and conventional orange juice production.

Since a good correlation exists between these two parameters and the assembly location in the core, it is recommended to include consideration of the wear risk minimization as an additional criterion for the design of the core loading pattern.

Taking the ℓ 1,2 mixed norm minimization as an example, the matrix V k can be estimated by the following constrained optimization problem [12]: begin{aligned} & underset{boldsymbol{V}}{text{minimize}} & & sum_{i=1}^{L} left|boldsymbol{bar{v}}_{i}right| & text{s.t.} & & |boldsymbol{Y}-boldsymbol{A}boldsymbol{V}|leqvarepsilon.

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