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With advances in sequencing technologies over the past few years, next-generation sequencing technologies have become widely available and have dramatically accelerated biological research [ 42], making identification of R gene homologs and members of other related gene families by data-mining methods much more efficient.
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Among the four methods, the two model decomposition-based methods were much more efficient than the other two methods.
Large-update methods are much more efficient than small-update methods in practice [1], but have a worst-case iteration bound.
In practice, such symmetric coercive optimization problems, the SOR methods are much more efficient than the Jacobi and Gauss-Seidel methods.
Compared to the elastic plastic finite element method, the present method is much more efficient.
Therefore, our method is much more efficient than the MM method or other variable duplication methods.
Therefore, our method is much more efficient than the MM method.
It is obvious that our method is much more efficient than the MM method since our time complexity is just the same as one-step iteration in the MM method due to the explicit shrinkage formula.
The new method is much more efficient than previously derived RKN methods for some subclasses of problems.
Obviously, our method is much more efficient than the Chan-Vese method.
We show that our method is much more efficient than the so-called all-Mach methods involving a single pressure, since large time steps can be used while retaining time accuracy.
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