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Several examples illustrate the behavior of the algorithms in unconstrained and constrained minimax problems.
Formal analysis using parallel performance models allows one to depict the underlying behavior of the algorithms and their communication complexities, with the aims of estimating their cost and improving their performance.
The bounds characterize the behavior of the algorithms over any sequence of trials, where each trial consists of an example and a desired outcome interval (any value in the interval is an acceptable outcome).
The input specification of the control system includes (i) the constraints on the location of its components, which are imposed by the plant, (ii) the communication requirements among the components, and (iii) an estimation of the real-time constraints for the correct behavior of the algorithms implementing the control law.
This approach allows us to continuously monitor the behavior of the algorithms.
This section presents the behavior of the algorithms when subject to different fault loads.
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We study the asymptotic behavior of the algorithm under the framework of learning theory.
This leads to a more FA-like behavior of the algorithm.
We study analytically the limiting behavior of the algorithm as the number of hidden variables increases indefinitely.
In this case, the bank can curb the discriminatory behavior of the algorithm by adjusting the decision-making threshold based on demographic criteria so as to bring the two acceptance rates into alignment.
The behavior of the algorithm for the linear and nonlinear case is presented with the analysis of the theory and operational importance of these techniques.
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CEO of Professional Science Editing for Scientists @ prosciediting.com