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The method is formulated as an iterative optimization problem where the maximum passband1 magnitude response error is minimized and the microphone positions are optimized while ensuring that the minimum stopband attenuation is above a prescribed level.
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As for the D2D link, an iterative optimization algorithm was used to find the optimal solution.
They are next used as a starting guess for an iterative optimization procedure based on gradient descent where all parameters in the descriptors are allowed to vary.
Both systems are optimized by an iterative optimization procedure.
Eddy and Durbin (1994) also presented an iterative optimization procedure that iterates between alignment and structure, taking an optimization approach instead of the sampling approach we describe here.
It contains an iterative optimization such as Gauss-Newton that searches along the gradient direction for an improved parameter vector.
However, an iterative optimization is required as shown in Sections 3.3.2 and 4.3, leading to a much higher computation complexity.
Furthermore, if the analysis is the basis for an iterative optimization procedure such as a gradient-based acoustical topology optimization, this approach imposes prohibitively high computational costs.
In view of the structure of the problem, we reformulate it as an integer linear optimization model and propose an iterative optimization approach.
Iterative reconstruction methods are based on modeling the experiment as a linear system of equations, which is solved by an iterative optimization method.
The K-means clustering algorithm is an iterative optimization algorithm.
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