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These features make the method practical for large-scale applications.
This would make the method natural, so that it reflects nature, just as the arts do.
To make the method more efficient and practical, an approximate version of the algorithm was proposed.
Theoretically, one can make the method be a polynomial-time algorithm when the dimension is fixed.
This capability will make the method to be free of communication issues.
Thus, we manage to significantly reduce computational load and make the method applicable.
Six control parameters of the common sharing method can make the method possess high adjusting freedom.
A number of recent reports on NLM denoising focused on shortcuts to make the method computationally practical [11 14].
Both of these two graphs are unweighted and undirected in order to make the method comparisons fair.
Moreover, other possibilities that have been explored make the method of generalized quasilinearization universally useful in applications [2 8].
To avoid significant problems it is advised that SPOCs are being paid, which can make the method costly.
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