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A constituted matrix with a smaller condition number responds better to design targets.
In doing so, the matrix to be inverted has a smaller condition number than that of the conventional LLS approach.
Besides, it also yields smaller condition number of the resulting impedance matrix than conventional displacement-based approaches in laterally varying complex media.
It is shown that shorter reshaping filters than conventionally used yield a smaller condition number, i.e., a higher robustness against RIR perturbations.
We show that shorter reshaping filters than conventionally used yield a smaller condition number, i.e., a higher robustness against RIR perturbations.
However, as can be seen in Table 1, the Gaussian basis still has a much smaller condition number than the other two bases.
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The additive has shown positive results under limited laboratory short-term and small-scale conditions.
Therefore, we chose D/24 as the smallest condition.
It can be seen that the proposed preconditioning method has the smallest condition number.
Under a small condition on the initial value, we prove the global existence and optimal decay estimate of solutions.
Remark 1 (1) The small condition on initial data u 0 in Theorem 2.1 and Theorem 2.2 can be removed if λ > 0 is sufficiently small.
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