Sentence examples for residual update from inspiring English sources
The phrase "residual update" is correct and usable in written English
It can be used in contexts related to data analysis, statistics, or software updates where changes or adjustments are made based on remaining or leftover data. Example: "After analyzing the initial results, we implemented a residual update to refine our model's accuracy."
As shown in our Table, the OMP algorithm performs the support identification followed by the residual update in each iteration and these operations are repeated usually K times.
And in the residual update step, it is required to calculate (mathcal {S}_{nz}times _{1}boldsymbol {B}_{1}times _{2}boldsymbol {B}_{2}times _{3}ldots times _{N}boldsymbol {B}_{N}) and subtract it from (mathcal {Y}), which needs P N (2k+1) operations for the present algorithm, whereas the requirement for OMP algorithm is P N (2k N +1).
On the Windows workstation, for the dataset with 95 500 animals and 50 000 SNPs, the time to transform the SNP data into the (integer) coding, required 954 s and 1217 s, respectively, for the residual updating and the RHS-updating algorithm.
As shown in Figure 7, for a fixed number of SNPs, the RAM use of the residual updating and the RHS-updating algorithms were linearly related to the number of animals included.
This implies that the relative benefit of using the RHS-updating algorithm compared to the residual updating algorithm is not affected by the number of SNPs included.
Using such libraries may have a larger impact on CPU time for the residual updating algorithms than for the RHS-updating algorithm, since the former involves many more multiplications.
The second alternative algorithm, here termed "RHS-updating", extends the idea of improved residual updating across multiple SNPs.
Applying equations (9) and (10) for locus j and j + 1, finalizes the RHS-updating step, just like equation (5) finalizes the residual updating step.
Compared to the residual updating schemes, the reduction in required RAM for RHS-updating ranged from 13.1 to 66.4%.
In fact, the array storing the genotypes for the residual updating algorithm or the group codes for the RHS-updating algorithm are the largest arrays used in those algorithms, and therefore largely determine the total amount of RAM used.
The developed bivariate Bayesian stochastic search variable selection model allowed for an unbalanced design by imputing residuals in the residual updating scheme for all missing records.
