Examine the singular points of the equation y'=(y-2x)/(y)
Examine the singular points of the equation $$y'=\frac{y-2x}{y}.$$
Explanation of Method
The singular points are located by solving P(x,y) = 0 and Q(x,y) = 0 simultaneously. The associated planar autonomous system is formed (linearising about the singular point where necessary), its coefficient/Jacobian matrix is written down, and the point is classified from the sign and nature of that matrix's eigenvalues, using the standard classification of planar linear systems into nodes, saddles, foci, and centres.
Step 1 · Symmetric form
Write the equation as $$P(x,y)\,dx - Q(x,y)\,dy = 0,$$ with $$P(x,y)=y-2x,\qquad Q(x,y)=y.$$
Step 2 · Locating the singular point
Set $P=0$ and $Q=0$ simultaneously: $$\begin{cases} y-2x=0,\\ y=0. \end{cases}$$ From the second equation $y=0$, substituting into the first gives $-2x=0$, hence $x=0$. The unique singular point is $$(x^*,y^*)=(0,0).$$
Step 3 · Associated linear autonomous system
Since $P$ and $Q$ are homogeneous of degree 1, write the equivalent planar system $$\frac{dx}{dt}=P(x,y)=y-2x,\qquad \frac{dy}{dt}=Q(x,y)=y,$$ with coefficient matrix $$A=\begin{pmatrix}-2&1\\0&1\end{pmatrix}.$$
Step 4 · Eigenvalues of $A$
The characteristic equation is $$\det(A-\lambda I)=0 \implies (-2-\lambda)(1-\lambda)-0=0 \implies (\lambda+2)(\lambda-1)=0.$$ Thus $$\lambda_1=-2,\qquad \lambda_2=1.$$
Step 5 · Classification of the singular point
The two eigenvalues are real, distinct, and of opposite sign: $$\lambda_1=-2<0<\lambda_2=1.$$ By the standard classification of linear autonomous systems, the origin is a saddle point.
Step 6 · Eigenvectors and separatrices
For $\lambda_1=-2$: $$(A+2I)\mathbf{v}=0 \implies \begin{pmatrix}0&1\\0&3\end{pmatrix}\mathbf{v}=0.$$ Both rows require $v_2=0$, so $$\mathbf{v}^{(1)}=\begin{pmatrix}1\\0\end{pmatrix}.$$ The corresponding separatrix is tangent to the $x$-axis at the origin. Since $\lambda_1<0$, trajectories on this manifold are attracted to the origin (stable separatrix). For $\lambda_2=1$: $$(A-I)\mathbf{v}=0 \implies \begin{pmatrix}-3&1\\0&0\end{pmatrix}\mathbf{v}=0.$$ The first row gives $-3v_1+v_2=0$, so $$\mathbf{v}^{(2)}=\begin{pmatrix}1\\3\end{pmatrix}.$$ The corresponding separatrix is tangent to the line $y=3x$ at the origin. Since $\lambda_2>0$, trajectories on this manifold depart from the origin (unstable separatrix).
Step 7 · General solution of the linear system
$$\begin{pmatrix}x(t)\\y(t)\end{pmatrix} =C_1 e^{-2t}\begin{pmatrix}1\\0\end{pmatrix} +C_2 e^{t}\begin{pmatrix}1\\3\end{pmatrix}.$$ In components: $$x(t)=C_1 e^{-2t}+C_2 e^{t},\qquad y(t)=3C_2 e^{t}.$$
Step 8 · Behaviour of integral curves near the origin
The saddle point has two invariant straight-line trajectories passing through the origin:
- Stable manifold (along $\mathbf{v}^{(1)}$, the $x$-axis, $y=0$): trajectories approach the origin as $t\to+\infty$ from both sides along the positive and negative $x$-axis.
- Unstable manifold (along $\mathbf{v}^{(2)}$, the line $y=3x$): trajectories depart from the origin as $t\to+\infty$ along $y=3x$.
Step 9 · Verification via the original ODE
Along $y=0$ (and $x\neq 0$) the right-hand side $\tfrac{y-2x}{y}$ is undefined, confirming that $y=0$ is a singular line of the ODE, consistent with the stable separatrix lying along the $x$-axis.
Step 10 · Conclusion
$$\boxed{ (x^*,y^*)=(0,0)\text{ is the unique singular point; it is a \textbf{saddle point}.} }$$ The eigenvalues $\lambda_1=-2$ and $\lambda_2=1$ are real with opposite signs. The stable separatrix is tangent to the $x$-axis and the unstable separatrix is tangent to the line $y=3x$ at the origin. All other trajectories exhibit the characteristic hyperbolic divergence of a saddle.
Summary Table
| Quantity | Value |
|---|---|
| Eigenvalues | $\det(A-\lambda I)=0 \implies (-2-\lambda)(1-\lambda)-0=0 \implies (\lambda+2)(\lambda-1)=0.$ |
| Classification | saddle point |
Additional Notes
- The classification follows from the standard theory of planar linear (or linearised) autonomous systems, based on the sign and nature of the eigenvalues of the coefficient/Jacobian matrix.
Result
The final classification is: