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Phase Portrait Behavior [1] Eigenvalue, Trace, Determinant Phase Portrait Shape λ 1 & λ 2 are real and of opposite sign; Determinant < 0 Saddle (unstable) λ 1 & λ 2 are real and of the same sign, and λ 1 ≠ λ 2; 0 < determinant < (trace 2 / 4) Node (stable if trace < 0, unstable if trace > 0) λ 1 & λ 2 have both a real and imaginary ...
The signs of the eigenvalues indicate the phase plane's behaviour: If the signs are opposite, the intersection of the eigenvectors is a saddle point . If the signs are both positive, the eigenvectors represent stable situations that the system diverges away from, and the intersection is an unstable node .
The phase portrait of the pendulum equation x ″ + sin x = 0.The highlighted curve shows the heteroclinic orbit from (x, x′) = (–π, 0) to (x, x′) = (π, 0).This orbit corresponds with the (rigid) pendulum starting upright, making one revolution through its lowest position, and ending upright again.
Phase portrait showing saddle-node bifurcation. Bifurcation theory is the mathematical study of changes in the qualitative or topological structure of a given family of curves, such as the integral curves of a family of vector fields, and the solutions of a family of differential equations.
In quantum computing, the quantum phase estimation algorithm is a quantum algorithm to estimate the phase corresponding to an eigenvalue of a given unitary operator.Because the eigenvalues of a unitary operator always have unit modulus, they are characterized by their phase, and therefore the algorithm can be equivalently described as retrieving either the phase or the eigenvalue itself.
As can be seen by the animation obtained by plotting phase portraits by varying the parameter , When α {\displaystyle \alpha } is negative, there are no equilibrium points. When α = 0 {\displaystyle \alpha =0} , there is a saddle-node point.
The Hartman–Grobman theorem has been extended to infinite-dimensional Banach spaces, non-autonomous systems / = (,) (potentially stochastic), and to cater for the topological differences that occur when there are eigenvalues with zero or near-zero real-part. [10] [8] [14] [15] [16] [17]
Let A be a square n × n matrix with n linearly independent eigenvectors q i (where i = 1, ..., n).Then A can be factored as = where Q is the square n × n matrix whose i th column is the eigenvector q i of A, and Λ is the diagonal matrix whose diagonal elements are the corresponding eigenvalues, Λ ii = λ i.