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Furthermore, we finally transform the optimization problem into an easily solvable problem and derive the optimal dynamic double thresholds with very low complexity.
The sigmoidal functions were used to transform the optimization criteria, resolution and analysis time, into the desirability values.
We first transform the optimization described in (8) into a minimization problem by simply using the reciprocal of the objective function.
In order to transform the optimization problem in (9) into a more convenient form, we introduce the change of variables, Furthermore, we will denote by.
First, we transform the optimization problem in (11) into a root searching problem which attempts to solve for the relay processor T using the system of nonlinear equations defined in (18).
First, we transform the optimization problem in (14) with the help of slack variables s i to its corresponding standard form which is given by x * = arg min x c T ⋅ x s.t.
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The incorporation of the target PSNR transforms the optimization to a constrained procedure to ensure a minimum of image quality that must be acquired.
The IWF approach in [13] treats interference as a channel noise component which transforms the optimization problem into a convex one.
As the formulated optimization problem is a nonlinear integer optimization problem which cannot be solved conveniently using traditional optimization tools, we transform the original optimization problem equivalently into three convex subproblems by applying Lagrange partial relaxation and McCormick envelopes, and then propose an iterative algorithm.
Second, under the assumption of known bandwidth allocation, we transform the original optimization problem into an equivalent convex optimization problem and obtain the optimal solution via fractional programming.
The Lagrange multiplier (alpha_{i}) is used to transform the constrained optimization problem into the dual optimization problem.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com