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The structure of the optimization algorithm with its constraints is defined as follows.
Constraints 11, 12, and 13 together define the structure of the optimization space.
The structure of the optimization problem is then exploited in a way that its solution can be found by solving a minimal set of nonlinear equations.
The structure of the optimization problem was analyzed to obtain a convenient separable arrangement and then the problem was solved by using Lagrangian relaxation.
Markov network-based EDAs [ 19- 21] could be an appropriate choice for applications where the structure of the optimization problem is known and can be easily represented using an undirected graphical model.
For instance, probabilistic models used by EDAs can be set up a priori in such a way that they represent previous knowledge about the structure of the optimization problem.
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By analyzing the structure of the optimal beamforming matrix, the optimization problem is simplified so that it can be directly solved using the genetic algorithm (GA).
For the intra-operator resource (spectrum and power) allocation problem, we propose a computationally efficient (if not, at least solvable) solution based on some linearization techniques (exact linearization or linear approximations) considering the structures of the optimization problem and the constraints.
In [10], a low-complexity optimal power allocation algorithm was derived by exploiting the structure of the considered optimization problem.
The structure of the overall optimization problem is investigated showing high complexity and nonlinearity particularly for switching energy management strategies.
The structure of the differential evolution optimization environment is simple and a convergence to optimal values realized here is very good in comparison to the convergence reported for other optimization algorithms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com