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Hence such a solution represents the running time of computing of the algorithm under consideration in each case.
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In this paper, it is always assumed that the neural model (3) and (4) admits a solution represented by x ( n, ϕ ) or simply x ( n ).
A putative solution represents a given position of each sample in two dimensions, and the cost of this solution is the Sammon score described above.
Utilizing a new characterization of Pareto optimality and presuming a finite decision space, we derive a program whose solution represents an optimal reduction.
This optimal solution represents an a priori prediction of all cellular metabolic fluxes.
The presence of more than one competing objective provides a realistic design setting where every solution represents an optimal trade-off between different criteria.
Implementing any large-scale technological solution represents a risk; therefore veering away from the status quo is a decision that cannot be taken lightly.
A set of Pareto-optimal solutions is found where each solution represents an optimal feasible path that cannot be outrun by any other path considering all four criteria.
Each model run consisted of 100 iterations to ensure that the solution represents a global minimum.
Each solution represents a particular performance trade-off between the objectives and can be considered optimal.
Each solution represents a compromise between the entropy production of reactions, heat transfer and frictional flow (pressure drop).
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CEO of Professional Science Editing for Scientists @ prosciediting.com