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Stability is assessed by computing the eigenvalues of the Jacobian evaluated at the fixed point.
We show how one can achieve this goal by computing the eigenvalues of certain structured matrix pencils.
By computing the eigenvalues of the Jacobian matrix evaluated at (E^{pm }), we obtain the stationary solutions (E^{pm}) are always unstable, and it is not possible to predict the amplitude of the pattern at this order.
Subsequently, Li and Li [6] studied two sign-changing solutions of a class of second-order integral boundary value problems by computing the eigenvalues and the algebraic multiplicities of the corresponding linear problems.
The existence and location of any Hopf bifurcations in a model can easily be established by computing the eigenvalues of the system linearised about the equilibrium solutions; a Hopf bifurcation occurs generically when a pair of eigenvalues crosses the imaginary axis under parameter variation.
We performed a PCA analysis by computing the eigenvalues and eigenvectors for the correlation matrix.
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If we could continue in the same fashion, by computing the global eigenvalues considering the first three modes.
Finally, the channel impulse response matrix is obtained by computing the positive eigenvalues and the corresponding eigenvectors of this outer-product matrix.
Then the channel impulse response matrix can be obtained by computing the positive eigenvalues and the corresponding eigenvectors of the outer-product matrix HH∗.
Although from Theorem 1 we can verify the positive definiteness of an even-order symmetric tensor (mathcal{A}) (the positive definiteness of the mth-degree homogeneous polynomial (f(x))) by computing the H-eigenvalues of (mathcal{A}), it is difficult to compute all these H-eigenvalues when m and n are large.
Although from Lemma 1.1 we can verify the positive definiteness of an even-order symmetric tensor (mathcal{A}) (the positive definiteness of the mth-degree homogeneous polynomial (f(x))) by computing the H-eigenvalues of (mathcal{A}).
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