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First-order vs separable

<p>A <term>first-order differential equation</term><idx><h>differential equation</h><h>first-order linear</h></idx> is an equation of the form
<men xml:id="firstlook05-equation-first-order-linear-ode">\frac{dx}{dt} + p(t) x = q(t).</men>
This equation will not be separable if <m>p(t)</m> is not a constant. We shall have to find a new approach to solving such an equation. We could, of course, use a numerical algorithm to solve<nbsp /><xref ref="firstlook05-equation-first-order-linear-ode" />; however, we can always find an algebraic solution to a first-order linear differential equation. Moreover, the fact that we can obtain such a solution analytically will prove very useful when we investigate more complicated equations and systems of equations.</p>

As far as I can see, such an equation typically isn't separable even if p(t) is a constant. For instance, dx/dt + x = t isn't separable. I guess it would be separable if q(t) = c p(t) for some constant c (maybe that is necessary?)

Solving for r in the Spruce Budworm project

Solve the equation
<me>rx(1-x) - \frac{x^2}{0.01 + x^2} = 0</me>
and plot the result in the <m>xr</m>-plane for <m>0 \leq r \lt 1</m>.</p>

I don't think this step is really necessary to the problem. If the goal is to plot the bifurcation curve, we can just do it directly with Sage implicit_plot (or Desmos, etc) without needing to solve for one variable, nor reflect the curve as in the next step.

In fact, when I assigned this project, most students used implicit_plot to plot the reflected version anyway. But then they tended to think that the reflection was doing something mathematically very important, rather than just working around the restriction of only wanting to plot explicit functions, and so made an unnecessarily big deal about it.

Qualitative consequences of repeated eigenvalues

<p>If the eigenvalue is positive, we will have a nodal source. If it is negative, we will have a nodal sink. Notice that we have only given a recipe for finding a solution to <m>\mathbf x' = A \mathbf x</m>, where <m>A</m> has a repeated eigenvalue and any two eigenvectors are linearly dependent. We will justify our procedure in the next section (<xref ref="linear06-subsection-repeated-eigenvalues" />).</p>

I think this passage could use some more explanation. We describe the equilibrium at 0 as a "nodal source" (or sink), but what do we mean by that? In what ways is it like a node, and in what ways is it like a source? As it's still the case that all solutions diverge from the origin, why is it necessary to distinguish this case from an ordinary source?

Basically, this chapter discusses the algebraic issues around solving the system in the case of a repeated eigenvalue, and how the solution takes a special algebraic form. But I think it would help to have some discussion of how the behavior of such a system differs qualitatively from other cases.

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