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We applied both variants of the R&G algorithm to our data set.
The empirical work represents by far the most extensive set of experiments on variants of algorithms for EGCC.
(ii) We design 1.5-approximation algorithms for both variants of local modifications.
For both variants of this use case, the EMVC algorithm would be used to optimize a gene set collection for a large number of individual datasets.
Variants of Algorithm (mathcal{N}_{J} for the GS and the SOR can be formulated similarly as for the linear obstacle problem; they would be denoted respectively by (mathcal{N}_{mathrm{GS}}) and (mathcal{N}_{mathrm{SOR}}) ((omega)).
The decentralized variant of algorithms has a better worst-case performance for the best strategy.
Compared with various state-of-the-art algorithms, including traditional PSO algorithms and representative variants of PSO algorithms, the performance of FCPSO is promising.
We implement several efficient variants of both algorithms, each one with a different initialization scheme, and then compare the resulting quantizers on a diverse set of images.
Fast and exact variants of both algorithms with several initialization schemes were compared on a diverse set of publicly available test images.
Moreover, different variants of KF algorithms and other linear estimation algorithms should be investigated along with the proposed SSLM algorithms family and compared to get more insightful outcome.
The variant of Algorithm 3.2 is presented as Algorithm 3.4 below.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com