The TLISMNI method has desirable features that include avoiding numerical differentiation of the forces, allowing for an efficient sparse matrix implementation, and ensuring that the kinematic constraint equations are satisfied at the position, velocity and acceleration levels.
We also show that our method has desirable properties (i.e., efficiency and robustness).
These single-stage model-based methods often have desirable statistical properties, but because they typically explore large spaces of possibilities rather than algorithmically constructing estimated trees, they can be computationally intensive and applicable only to smaller datasets.
Their method can have desirable frequentist properties under certain types of priors.
These methods have two desirable characteristics, namely, robust numerical convergence and efficient parallelizability.
Compatible Galerkin methods have many desirable properties, including energy conservation, steady geostrophic modes and the absence of spurious stationary modes, such as pressure modes.
Convolution methods have many desirable properties, including automatic capture of topological change, production of curvature motion without explicit computation of curvature, natural extension to the motion of triple-point junctions, and fast, accurate implementation.
The PAC method is also shown to have desirable numerical damping characteristics due to the α parameter introduced in the corrector.
An early detection of HIV infection is desirable, and thus highly effective bioanalytical methods have to be developed.
However, wrapper methods have the drawback of high computational load, making them less desirable as the dimensionality increases.
