By comparing Table 1 and Table 2, we find that Newton-SOR has a much shorter CPU time, much less iterations, and much smaller error than Newton iterative method does.
As can clearly be observed, our approach results in much smaller error than that of [11] for the whole considered range of SNR in the three examples.
The thermal conductivity values of graphene, whose length and width are 3.6 and 5.52 μm, respectively, were measured as 2430 ± 190, 2150 ± 170, and 2100 ± 160 W/mK at the peak temperatures of 335, 361, and 366 K, respectively, with much smaller error range compared to the previously reported values.
In the other 53 runs, GD finds solutions with small gradients (mostly <0.05) and much smaller error (average 7.24, median 3.07).
Recently, however, Fan et al. (2011) and Yang et al. (2011) proposed new experimental techniques for whole-chromosome haplotyping, which promise to achieve much smaller error rates than current methods.
In particular, when some data are missing at random the BRMA is likely to produce a much smaller standard error and mean-square error of pooled estimates than URMA, even for moderate correlations.
An average of many polls will have a much smaller standard error.
At point 17,21 and 24, our hybrid method gets much smaller errors than GPS.
The optimized simulator has errors of less than 16% compared with direct program measurement in all the cases we studied, and typically much smaller errors.
First, it is shown that the proposed CPD estimation methods (IPD-based and RPD-based) produce much smaller errors in CPD1 than the conventional method used in PS.
