Exact(42)
The objective is determining an optimal step ξopt that solves the following one-dimensional optimization problem: (30) min ξ C T L = C T (x k + ξ d k ) Subject to ξ > 0, h i (x k + ξ d k ) ⩽ 0, i = 1 … N c, where C T L is the total costs function along the search direction.
The optimal step size for f RS depends on the satellite signal strength distribution.
Thus, the optimal step size is now {mu}_{mathrm{opt}}=frac{1}{R{left|hright|}^2}.
Finally, a power series that recovers an optimal step length is build in the neighborhood of bifurcation points.
The optimal step size for the FxLMS algorithm lies in the middle of stability interval [43, 44].
Thus, the optimal step size for the MFxLMS algorithm in a noiseless channel is given in Eq. (18).
Similar(18)
the optimal step-size, causing fastest convergence.
This optimal step-size depends on the time varying complex gain model.
The optimal step-size is obtained by minimizing the MSD at each iteration.
Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD).
We have shown that an optimal step-size for the complex gain estimation stage can be obtained.
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