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Using the Jacobian from 7 (b) we find the linearization of the system in the critical points.
Critical point
- This matrix is diagonal and so the diagonal elements are the eigenvalues
- This matrix is positive definite since its two eigenvalues are positive. All positive eigenvalues indicate that the critical point is an unstable node.
- The eigenvector for the eigenvalue is .
- The eigenvector for the eigenvalue is .
- The linearized critical point in the phase space is on the following figure:
Critical point
- The eigenvalues are , because a triangular matrix has its eigenvalues on the diagonal. The eigenvalues are both negative and hence, the matrix is negative definite and this critical point is a stable node.
- The eigenvector for the eigenvalue is because of the triangular shape of the matrix.
- For the other eigenvector we need to work a little more and calculate the null space of :
Hence,
- We find the the eigenvector for is .
- The linearized critical point in the phase space is on the following figure:
Critical point
- For the eigenvalues we calculate
- The eigenvalue for is the null space of
so that the eigenvector is .
- The eigenvalue for is the null space of
and the eigenvector is .
- Because one eigenvalue is positive, the other negative, the critical point is not asymptotically stable. Along the eigenvector for , the solution moves away from the critical point, along the eigenvector for , the solution moves towards the critical point. Since the eigenvalues do not have the same sign, the matrix is indefinite and the equilibrium is a saddle point.
- See the following figure:
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