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This improves convergence to the trim condition.
With this pole, a region of convergence to the left.
For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for "low noise" datasets.
Fast convergence to the actual optimal solution has been shown.
One of them would be a region of convergence to the right of this pole.
Convergence to the stationary distribution and sufficient sampling were checked using Tracer v1.5 (http://tree.bio.ed.ac.uk/software/tracer)41.
However, when there are multiple local minima, there is no guarantee of convergence to the true global minimizer.
Now, finally we can tie together the region of convergence to the convergence of the Fourier transform.
Results show a convergence to the true optimum in two and three dimensions.
Then, a switched controller that guarantees convergence to the sliding surface is developed.
However, these researches mainly have given fast convergence to the original cGA.
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