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Computation efficiency can be improved through multigrid inversion [ 33, 40, 41] and parallel computing, allowing for finer discretization and higher resolution in a limited computation time.
The results in this paper show that the proposed OSSO can perform excellently in a limited computation time.
While computation times of the exact algorithm increase drastically with problem size, the proposed meta-heuristic algorithm provides near-optimal solutions within limited computation time for all instances.
The computation results show that the proposed method is able to get optimal solutions for small scale problems and near optimal solutions within limited computation time for large scale problems.
These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives.
It does however, when correctly parametrised, have a high probability of finding all important conformers while using relatively limited computation time.
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Numerical results indicate that the heuristic approach is very attractive and leads to better solutions than those provided by state-of-the-art integer programming codes in limited computation times, with solution times significantly smaller.
The number of SNPs was limited to 420, to reduce total computation time, and this limited number of SNPs was sufficient to compare the relative computation time of the different algorithms because total CPU time scales linearly with the number of SNPs.
Hence a modeling approach which is good for nonlinearly separable data with the advantage of limited data storage space and computation time requirement for analysis has greater utility.
In this article, we present BWBBLE, an aligner that handles genetic variants and thus avoids the inherent bias induced by mapping to a specific genome, while providing reasonable computation time and limited memory consumption for large collections of genomes.
This limitation helps to avoid excessive computation time.
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