LSEVLow Speed Electric Vehicle
LSEVLaboratoire des Substances Explosives de Vemeuil
LSEVLunar Surface Exploration Vehicle
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Furthermore, these LSEVs give a lower bound on the multiplicity of the eigenvalue [lambda] =1, denoted mult([lambda] = 1, [sigma] (L)).
Let Z be a collection of LSEVs of L that have been identified and let Q be a sparse matrix that spans the orthogonal complement of Z.
The implications of these theorems are: (i) for a slightly-weighted graph G, the knowledge of LSEVs of the unweighted version of G is useful for obtaining accurate initial approximations to the eigenvectors of the graph Laplacian and (ii) for a graph G with time-dependent edge weights that vary slightly, [w.
We conclude this section with an interesting property of the LSEVs associated with the combinatorial graph Laplacian that is not shared by the other common graph-associated matrices.
3) containing the LSEVs of L corresponding to shared leaves or hanging cliques over a collection of local subsets, [{[S.
The LSEVs of L have changed, yet the global eigenvectors have not.
We focus on employing LSEVs to aid in the calculation of commute time, a distance measure for pairs of vertices, due to recent interest in a wide range of application areas.
k] may be quite slow and this bound is not useful unless the LSEVs are detected and all known eigenvectors are used to improve the truncation error bound.
Let K be the set of indices of all known eigenpairs (the LSEVs detected and eigenpairs that have been computed).
Below we give an example of a common practical situation where the truncation error is known to be zero for certain pairs of vertices by using only LSEVs as known eigenvectors.
Additionally, bounds on C (i, j) are easily obtained by the detection of LSEVs.
For (i), note that all the LSEVs in Z are zero-valued at i and j.