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When the size of a matrix is large special numerical methods are necessary for obtaining eigenvalues and eigenvectors.
The size of a matrix is defined by the number of rows and columns that it contains.
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For a formulation from the network (global) point of view and arbitrary size of matrix A, see Section 4.
Since the size of such a matrix is exponential with respect to n, the time and space complexity of this method is exponential too.
Since the number of variables in our recurrence relations is (|mathcal {V}|p(G)), the size of a square matrix constructed from the recurrence relation is (|mathcal {V}|p(G)).
Understanding this shift in mobility and how it relates to changes in entropy and the degree of interaction between the DNA and the channel wall provide a means to tune the size of a fabricated matrix around particular DNA applications involving separating a specific range of fragment sizes.
q is an odd number, which indicates the size of a sub-matrix.
This program maximally packs each matrix representing a network, and then calculates an isocline of perfect nestedness for the given size and shape of a matrix.
Parameter n is equal to the dimension of matrix A. The computational complexity of the linear and non-linear models depends on the size of matrix A, which is m × m (i.e. ridge regression BLUP) or n × n (i.e. GBLUP) for the linear models and n × n for the non-linear models implemented in our study, which means that the complexities are either o(m) or o(n).
However, the size of the matrix A is very large, making it difficult to obtain its inverse matrix.
The size of an ENT character matrix is the number of hosts times the number of characters in the original associate matrix.
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