Nodes in V E are divided into two types, reference nodes and common nodes.
In a multiplex graph, the set of edges E is divided into pairwise disjoint classes (E = bigcup _{rin R} E_r), where R is the set of possible relations.
The distributed algorithm can be described as: 1. Nodes in V E are divided into two types, reference nodes and common nodes.
Therefore, the class of intermediate regularly varying solutions of (E) is divided into three types of subclasses: mbox{ntr-}mathrm{SV}, qquad operatorname{RV}(rho),quad rhoin biggl 0,1-frac{eta}{alpha} biggl 0,1-frac{eta}{alpha}biggr(1-frac{eta}{alpha} biggr).
Then consider the two halves in which E is divided by H: (Ecap H^+) and (Ecap H^-) and the sets obtained by adding to each half its symmetral with respect to H, i.e., begin{aligned} E^+:= Ecap H^+ cup r_H Ecap H^+),quad E^- = Ecap H^-)cup r_H(E^- = Ecap
As a consequence, when F was used instead of C to compute N e, on average, N e was divided by more than two in dogs.
Due to the complexity of the model, a sequential approach for parameter identification is proposed and implemented, i.e. the parameter set is divided into different subsets, and the parameter estimation is carried out sequentially in several steps and iterations.
The antibonding states with e symmetry are divided into three peaks in the fundamental bandgap region.
Construct the matrix ( {overline{mathbf{E}}}^Hoverline{mathbf{E}} ) and characterize it, we can get the feature matrixE, E can be divided into four P × P dimensional matrix.
(4) The covariance matrices are decomposed and two signal subspaces E 1 and E 2 are obtained, then combine them into a new matrix( overline{mathbf{E}}=left[{mathbf{E}}_{mathbf{1}}kern0.5em {mathbf{E}}_{mathbf{2}}right] ). (5) Construct the matrix ( {overline{mathbf{E}}}^Hoverline{mathbf{E}} ) and characterize it, we can get the feature matrixE, E can be divided into four P × P dimensional matrix.
