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In mathematics, in the study of dynamical systems, the Hartman–Grobman theorem or linearisation theorem is a theorem about the local behaviour of dynamical systems in the
neighbourhood A neighbourhood (British English, Irish English, Australian English and Canadian English) or neighborhood (American English; American and British English spelling differences, see spelling differences) is a geographically localised community ...
of a
hyperbolic equilibrium point In the study of dynamical systems, a hyperbolic equilibrium point or hyperbolic fixed point is a fixed point that does not have any center manifolds. Near a hyperbolic point the orbits of a two-dimensional, non-dissipative system resemble hyperbo ...
. It asserts that linearisation—a natural simplification of the system—is effective in predicting qualitative patterns of behaviour. The theorem owes its name to Philip Hartman and David M. Grobman. The theorem states that the behaviour of a dynamical system in a domain near a hyperbolic equilibrium point is qualitatively the same as the behaviour of its
linearization In mathematics, linearization is finding the linear approximation to a function at a given point. The linear approximation of a function is the first order Taylor expansion around the point of interest. In the study of dynamical systems, lineari ...
near this equilibrium point, where hyperbolicity means that no eigenvalue of the linearization has real part equal to zero. Therefore, when dealing with such dynamical systems one can use the simpler linearization of the system to analyse its behaviour around equilibria.


Main theorem

Consider a system evolving in time with state u(t)\in\mathbb R^n that satisfies the differential equation du/dt=f(u) for some
smooth map In mathematical analysis, the smoothness of a function is a property measured by the number of continuous derivatives it has over some domain, called ''differentiability class''. At the very minimum, a function could be considered smooth if ...
f: \mathbb^n \to \mathbb^n. Suppose the map has a hyperbolic equilibrium state u^*\in\mathbb R^n: that is, f(u^*)=0 and the Jacobian matrix A= partial f_i/\partial x_j/math> of f at state u^* has no
eigenvalue In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted ...
with real part equal to zero. Then there exists a neighbourhood N of the equilibrium u^* and a
homeomorphism In the mathematical field of topology, a homeomorphism, topological isomorphism, or bicontinuous function is a bijective and continuous function between topological spaces that has a continuous inverse function. Homeomorphisms are the isomor ...
h : N \to \mathbb^n, such that h(u^*)=0 and such that in the neighbourhood N the flow of du/dt=f(u) is
topologically conjugate In mathematics, two functions are said to be topologically conjugate if there exists a homeomorphism that will conjugate the one into the other. Topological conjugacy, and related-but-distinct of flows, are important in the study of iterated func ...
by the continuous map U=h(u) to the flow of its linearisation dU/dt=AU. Even for infinitely differentiable maps f, the homeomorphism h need not to be smooth, nor even locally Lipschitz. However, it turns out to be
Hölder continuous Hölder: * ''Hölder, Hoelder'' as surname * Hölder condition * Hölder's inequality * Hölder mean In mathematics, generalized means (or power mean or Hölder mean from Otto Hölder) are a family of functions for aggregating sets of number ...
, with an exponent depending on the constant of hyperbolicity of A. The Hartman–Grobman theorem has been extended to infinite-dimensional Banach spaces, non-autonomous systems du/dt=f(u,t) (potentially stochastic), and to cater for the topological differences that occur when there are eigenvalues with zero or near-zero real-part.


Example

The algebra necessary for this example is easily carried out by a web service that computes normal form coordinate transforms of systems of differential equations, autonomous or non-autonomous, deterministic or stochastic. Consider the 2D system in variables u=(y,z) evolving according to the pair of coupled differential equations : \frac = -3y+yz\quad\text\quad \frac = z+y^2. By direct computation it can be seen that the only equilibrium of this system lies at the origin, that is u^*=0. The coordinate transform, u=h^(U) where U=(Y,Z), given by : \begin y & \approx Y+YZ+\dfrac1Y^3+\dfrac1 2Y Z^2 \\ ptz & \approx Z-\dfrac1 7Y^2-\dfrac1 3Y^2 Z \end is a smooth map between the original u=(y,z) and new U=(Y,Z) coordinates, at least near the equilibrium at the origin. In the new coordinates the dynamical system transforms to its linearisation : \frac=-3Y\quad\text\quad \frac = Z. That is, a distorted version of the linearisation gives the original dynamics in some finite neighbourhood.


See also

* Stable manifold theorem


References


Further reading

* * *


External links

* * * {{DEFAULTSORT:Hartman-Grobman Theorem Theorems in analysis Theorems in dynamical systems Approximations