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We could not compute inverse solutions that maximize ascendancy because the objective function is unbounded.
Because the objective function for the optimization is clearly a quadratic of line-current flow, the convergence character of this algorithm is close to linear and a small iteration is needed in the optimization process.
Indeed, this optimization problem is so complex that for traditional optimization algorithms it may be difficult or impossible to solve it because the objective function is not available in analytic form.
However, Equation (15) is not a concave optimization problem because the objective function is not concave.
Because the objective function of the partitioning procedure is to minimize the workload, the positive values represent improvements obtained.
Because the objective function of this model is maximization, the values for reduction percent mentioned in Table 7 are positive.
Similar(39)
This problem is convex because its the objective function is jointly convex in (N 1,N 2,…,N Q ) and the constraints are affine in N q.
So, we skip from presenting such details here, and continue our model formulation only for Model A. Because of the objective function of this model is formed from inventory related costs such as the purchasing price, transportation costs, carrying and ordering costs, shrinkage cost, it is a minimizing type objective function.
The solution is trivial for θ = 0 or θ = 1, because the objective functions and the constraints are monotonic with n over the entire range of processors.
This is because the objective functions, associated to diverse metrics, are usually in conflict, thus, a set of good solutions is generated, usually following Pareto dominance concepts (Pareto 1927).
The underlying theory (O'Brien and Salop (2000)) predicts that common ownership can be anticompetitive because it changes the objective function of firms, such that they no longer seek to simply maximize their own profits.
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