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(Gammainmathbb{R}^{ntimes n}) describes the inner-coupling positive definite matrix between the subsystems.
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In a simple way, a kernel function can be considered as a positive definite matrix that measures the similarities between each pair of input data.
This system is characterized by a symmetric positive definite matrix.
However, Knight and Fu (2000) assume that on the nullspace of the limit term, the difference between the sample variance and the singular matrix converges in probability to a positive definite matrix when multiplied by an appropriate rate.
Let M be a positive definite matrix.
where B is a positive definite matrix.
Let be a positive definite matrix.
(S0 Given, and a positive definite matrix.
Let H be a positive definite matrix.
Let G be a positive definite matrix.
B is a negative definite matrix.
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