What does it mean for a linear approximation to be reliable?

Click For Summary
SUMMARY

The reliability of a linear approximation in describing the long-term behavior of a nonlinear system around an equilibrium point is determined by the linear stability of the system. Linearized equations are considered reliable when they do not diverge from the nonlinear solution, which is assessed through the eigenvalues of the Jacobian matrix. If the real part of the eigenvalue, denoted as Re{λ}, is greater than zero, the system is linearly unstable. However, linear stability alone is insufficient for determining overall stability, as other perturbations can influence the system's behavior over time.

PREREQUISITES
  • Understanding of Jacobian matrix in differential equations
  • Knowledge of linear stability analysis techniques
  • Familiarity with eigenvalues and their implications for stability
  • Basic concepts of nonlinear dynamics and perturbation theory
NEXT STEPS
  • Study the Lyapunov stability criteria for nonlinear systems
  • Learn about Hamiltonian dynamics and its relation to stability
  • Explore energetic and dynamical stability concepts in depth
  • Review linear stability analysis methodologies through academic notes or textbooks
USEFUL FOR

Students and researchers in applied mathematics, physicists studying nonlinear systems, and engineers involved in stability analysis of dynamic systems.

Dusty912
Messages
149
Reaction score
1

Homework Statement


In regards to linearization of a nonlinear system in differential equations. What does it mean for a linear approximation to be reliable to describe the long term behavior of the non-linear system around the equilibrium point?

Homework Equations


jacobian matrix

The Attempt at a Solution


General question.
 
Physics news on Phys.org
Linearized equations are "reliable" when the equations are linearly stable, i.e. the time dependent solution of the linearized system do not diverge from the nonlinear solution. The linearized solution won't capture all features of the nonlinear solution but at least it gives you a rough idea about the time evolution. This is equivalent to saying that the equations are linearly stable.

To study the linear stability you replace, roughly speaking, the nonlinear solution as following \Phi(x)\rightarrow\Phi_0(x) + \delta(x)e^{\lambda t} where ##\Phi_0(x)## is a time independent solution of the nonlinear equation. After plugging ##\Phi_0(x)+\delta(x)e^{\lambda t}## into the nonlinear equation one has to determine the eigenvalues ##\lambda##. If ##Re\{\lambda\}>0## then perturbation ##\delta## will grow with time and the solution ##\Phi_0(x)## it is said to be linearly unstable.
Very important: keep in mind that the linear stability depends on the (is associated with a) time independent solution of the nonlinear equation. It may happened that a solution may be linearly stable while others not.
See for instance the wikipedia page.

However, the linear stability is a weak criteria when deciding whether a system is stable or not. This means that even if solution is linearly stable don't imply that it will follow the long time behavior of the nonlinear equation. Aside from the linear perturbations there are other types of perturbations which may set in and affect the time development. The stability chain is as following Energetic\: stability \Rightarrow Dynamical\: stability\Rightarrow Linear\: stability The linear stability is used to rule out the stability, is the system is not linearly stable then it won't be neither dynamical nor energetic stable. The energetic and dynamical stabilities are in general cumbersome to undertake, one should study the Hamiltonian structure and, something like, the Lyapunov stability (related directly to the time dependent evolution of the solution). They are performed only for simple nonlinear systems and solutions.

LE: You can follow, for instance, this notes as guide on linear stability analysis.
 
Last edited:

Similar threads

  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 2 ·
Replies
2
Views
1K
  • · Replies 22 ·
Replies
22
Views
2K
  • · Replies 13 ·
Replies
13
Views
3K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K