Generally, one real eigenvalue crossing the imaginary axis is called saddle-node bifurcation. A pair of complex eigenvalues crossing the imaginary axis is referred to as Hopf bifuraction.
This is true, however, some additional conditions have to be met to identify the bifurcation as saddle-node or Hopf. This is less important with Hopf bifurcation since the appearance of two purely imaginary eigenvalues in vast majority cases implies appearance (or disappearance) of a unique limit cycle, but in the case of simple real eigenvalue crossing the imaginary axis there are at least two more cases which are often met in applications: transcritical bifurcation and pitchfork bifurcation. At a qualitative level:
- saddle-node bifurcation corresponds to the appearance "out of nowhere" two steady states, one is stable, another is unstable
- transcritical bifurcation corresponds to the case when two steady states exchange, upon meeting each other, their stabilities
- pitchfork bifurcation corresponds when from one, say stable, equilibrium, two more stable equilibria appear and the original one becomes unstable.
The exact conditions which type you encounter depend on the type of the normal form of the equation you consider. See, e.g, this book.
Are these two types of bifurcations exactly the ones that occur for the PDE (while the steady state remains stable for the ODE) in what is generally referred to as Turing bifurcation / Turing instability?
Yes, in the spatially explicit system all these four types can appear in a similar way how they appear in local systems described by ODEs.
If so, what are the qualitative differences, if any, between the patterns formed past the Turing bifurcation?
In the case of simple zero eigenvalue what is usually expected is an appearance of a spatially non-homogeneous steady state (or more than one). This steady state can be stable generating spatially non-homogeneous patterns, which are used in various problems of pattern formation and morphogenesis. A lot of details are given, e.g., in James Murray's book Mathematical Biology, together with some technical details. Hopf bifurcation yields not only spatially non-homogeneous solutions, but also temporally periodic oscillations.
P.S. And final remark. The name "Hopf bifurcation" is quite unfortunate. The fact that periodic solutions appear under parameter change when two eigenvalues cross imaginary axis
was known to the father of qualitative analysis of dynamical systems -- Poincaré. The exact statements of the theorem in the planar case, together with proofs, were given by Andronov. The main contribution of Hopf is generalization of this situation to $n$-dimensional case. So the correct name would be Poincaré-Andronov-Hopf bifurcation, or, in my opinion, a much better option is the bifurcation of the birth of limit cycle :)
Your analyses are spot on.
The saddle-node bifurcation requires that $f(x) = 1 + rx + x^2$ has two identical solutions, so the discriminant $∆ = r^2 − 4 = 0$. Therefore, $r = \pm2$ and $x^∗ = \mp1$.
If we draw a phase portrait, it would look like this for $r = -2$
However, I think they want the streamline, which is (compare the phase portrait for stable vs. unstable flow)
For $r = 2$, we have
The streamline is
I also think they are looking for the bifurcation diagram (blue is stable, orange is unstable)
Update I used Mathematica to draw all of the figures.
- To draw the streamline, I use the code here.
- To draw the phase portrait, I use the code here, the Manipulate and StreamPlot commands snippet.
- To do the bifurcation diagram, I just use the Plot command in Mathematica.
Best Answer
The equilibrium points are at
$$ \left[ \begin{array}{ccc} n & u & v \\ 1 & 1 & 0 \\ 2 & \frac{a+\sqrt{a \left(-4 a^2-8 k a+a-4 k^2\right)}}{2 a} & \frac{a-\sqrt{a \left(a-4 (a+k)^2\right)}}{2 (a+k)} \\ 3 & \frac{a-\sqrt{a \left(-4 a^2-8 k a+a-4 k^2\right)}}{2 a} & \frac{a+\sqrt{a \left(a-4 (a+k)^2\right)}}{2 (a+k)} \\ \end{array} \right] $$
The equilibrium points $\{2,3\}$ collapse (have the same coordinates) at $k = -a \pm\frac{\sqrt{a}}{2}$: thus for $k = -a+\frac{\sqrt{a}}{2}$ they collapse in $\{\frac 12,\sqrt{a} \}$ and for $k = -a-\frac{\sqrt{a}}{2}$ in $\{\frac 12,-\sqrt{a} \}$