Stability and phase portraits
Why this is asked: Classify a 2×2 linear system from trace and determinant alone; for nonlinear systems linearise and use Hartman–Grobman — but remember the centre case is the one linearisation cannot decide.
Classification of ẋ = Ax in the plane
Let T = trace .
| Condition | Type | Stability |
|---|---|---|
| D < 0 | saddle | unstable |
| stable node | asymptotically stable | |
| unstable node | unstable | |
| stable spiral | asymptotically stable | |
| unstable spiral | unstable | |
| D > 0, T = 0 | centre | stable, not asymptotically |
| D = 0 | degenerate (a line of equilibria) | — |
Summary: asymptotically stable ⇔ T < 0 and D > 0 ⇔ both eigenvalues have negative real part.
Nonlinear systems
Linearise at an equilibrium: J = Df(x*).
- Hartman–Grobman: if no eigenvalue of J has zero real part (a hyperbolic equilibrium), the nonlinear phase portrait is locally equivalent to the linear one.
- The centre case is undecidable by linearisation: ẋ , ẏ has a linear centre but is actually asymptotically stable, while flipping the cubic signs makes it unstable.
Lyapunov's direct method
Find V with V(x*) = 0, V > 0 nearby:
- V̇ ≤ 0 ⇒ stable; V̇ < 0 ⇒ asymptotically stable; V̇ > 0 ⇒ unstable.
Works where linearisation fails. For a conservative system, the energy is the natural V.
Key takeaways
- T < 0 and D > 0 ⇔ asymptotically stable; D < 0 ⇔ saddle.
- Hyperbolic equilibria are settled by linearisation; centres are not.
- Lyapunov functions handle the non-hyperbolic cases.
See it move
The trap here
“Linearisation determines stability at every equilibrium” — false
ẋ , ẏ at the origin
The linearisation is a centre (eigenvalues ±i), but gives V̇ : asymptotically stable. Non-hyperbolic equilibria escape Hartman–Grobman.
Check yourself
For the system ẋ = Ax with A = [[−2, 1], [1, −2]], the origin is
Next: First-order PDE: Lagrange, Charpit, characteristics
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