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In all our designs, the gradient descent minimization algorithm converged rapidly to a stable solution.
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(b) Balancer output (algorithm converged).
The EM algorithm converged after 334 iterations.
The algorithm converged to this value already after 880 iterations.
The algorithm converged within three iterations for all data sets.
It is predictable that dogleg algorithm converges rapidly while the alternative minimization and Cauchy point has a long way to converge.
We prove that the transformed power minimization problem is convex and that our proposed SCA algorithm converges to a solution.
According to Fig. 3, it is easy to observe that the GA converges within 50 generations whereas the PSO algorithm converges within 160 generations yielding also the best MSE minimization.
Our aim is to prove that the iterative algorithm converges strongly to a common element of these sets, which also solves some hierarchical minimization.
The algorithm converges to the global optimum almost surely.
With the achieved results, the algorithm converges rapidly and efficiently.
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