The phrase "algorithm is performed on" is grammatically correct and can be used in written English, particularly in technical or scientific writing.
It is used to describe a process or action being carried out by an algorithm. Example: The sorting algorithm is performed on a dataset to arrange the values in ascending order.
The BP algorithm is performed on the new obtained matrix H ′. During one round of BP iteration, the LLR value of the ith bit is updated as: begin{array}{*{20}l} &gamma^(x_{i})=gamma(x_{i})+eta gamma_{text{ext}}(x_{i}),& end{array} (5).
Evaluation of the proposed algorithm is performed on a benchmark problem for HDD track following.
Objects or persons can be found because the template matching algorithm is performed on each image in the database.
The multi-objective optimization on the EAWT using Genetic Algorithm is performed on a response surface that is generated using CFD simulations in OpenFOAM.
The applied algorithm is performed on the basis of the Leverage mathematical approach, in which the statistical Hat matrix, Williams Plot, and the residuals of a selected correlation results lead to define the probable outliers.
The algorithm is performed on the basis of the Leverage approach, in which the statistical Hat matrix, Williams Plot, and the residuals of the model results lead to identify the probable outliers.
If multiple portions of the algorithm are performed on the FPGA without returning to the CPU, the additional speedup can be considerable.
Moreover, when the %MinMax algorithm was performed on +1 or −1 out-of-frame codons, the codon usage distribution was centered near average (%Min = %Max = 0) (Fig. 2B, dotted lines), indicating that the distribution reported for the in-frame ORFs is not simply a product of the algorithm itself, or a genomic artifact.
Unless stated otherwise, all steps of the algorithm are performed on corrected images.
For the cluster tree analysis, the k-means clustering algorithm was performed on all the samples.
Shrunken centroid algorithm was performed on the training set using Tibshirani's prediction analysis of microarrays (PAM) package.
