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Construct a suitable Liapunov function of the form \(a x^{2}+c y^{2}\) where \(a\) and \(c\) are to be determined. Then show that the critical point at the origin is of the indicated type. $$ d x / d t=x^{3}-y^{3}, \quad d y / d t=2 x y^{2}+4 x^{2} y+2 y^{3} ; \quad \text { unstable } $$

Short Answer

Expert verified
The Liapunov function proving that the origin is an unstable critical point for the given system of differential equations is \(V(x, y) = ax^2 + cy^2\), where \(a\) and \(c\) are positive constants.

Step by step solution

01

Find the critical point of the given system of ODEs

To find the critical point of the given system of ODEs, set both \(dx/dt\) and \(dy/dt\) to zero and solve the following equations. $$ x^3 - y^3 = 0 \quad (1) \\ 2 xy^2 + 4 x^2 y + 2 y^3 = 0 \quad (2) $$ From equation (1), we have \(x^3 = y^3,\) which implies that either \(x=y\) or \(x=-y.\) For the case \(x=y\), equation (2) becomes \(2y^3 + 4y^2 y + 2y^3 = 0 \Rightarrow y^3=0 \Rightarrow y=0.\) So, \(x=y=0\) is a critical point.
02

Choose a Liapunov function

Choose a Liapunov function of the form \(V(x, y) = ax^2 + cy^2\), where \(a\) and \(c\) are positive constants to be determined.
03

Calculate the time derivative of the Liapunov function

We will now find \(\frac{dV}{dt}\). Using the original ODEs: $$ \frac{dV}{dt} = \frac{\partial V}{\partial x} \frac{dx}{dt} + \frac{\partial V}{\partial y} \frac{dy}{dt} $$ Differentiate the Liapunov function \(V(x, y)\) with respect to \(x\) and \(y\): $$ \frac{\partial V}{\partial x} = 2ax, \quad \frac{\partial V}{\partial y} = 2cy $$ Substitute the derivatives and the given ODEs into the equation for \(\frac{dV}{dt}\): $$ \frac{dV}{dt} = 2ax(x^3 - y^3) + 2cy(2xy^2 + 4x^2y + 2y^3) $$
04

Determine the signs of \(a\) and \(c\)

In order for the Liapunov function to be valid as per the given conditions (unstable system), the following must hold: 1. \(V(0, 0) = 0\) 2. \(V(x, y) > 0\) for all \((x, y) \neq (0, 0)\) 3. \(\frac{dV}{dt} > 0\) for all \((x, y) \neq (0, 0)\) Since the Liapunov function is \(V(x, y) = ax^2 + cy^2\), the first two conditions are automatically satisfied for positive values of \(a\) and \(c\). Now we need to ensure that \(\frac{dV}{dt} > 0\) for all \((x, y) \neq (0, 0)\) to show the system is unstable. From the equation for \(\frac{dV}{dt}\): $$ \frac{dV}{dt} = 2ax(x^3 - y^3) + 2cy(2xy^2 + 4x^2y + 2y^3) $$ Both terms in \(\frac{dV}{dt}\) need to have the same sign and be non-zero to satisfy the condition. If we take \(a\) and \(c\) positive, this condition will hold true for any \(x, y \neq 0,\) hence making the origin an unstable critical point.
05

Show that the origin is an unstable critical point

We have determined the Liapunov function \(V(x, y) = ax^2 + cy^2\), where \(a\) and \(c\) are positive constants, ensuring that \(V(0, 0) = 0\) and \(V(x, y) > 0\) for all \((x, y) \neq (0, 0)\). We have also calculated the time derivative of the Liapunov function and found that \(\frac{dV}{dt} > 0\) for all \((x, y) \neq (0, 0)\) when \(a\) and \(c\) are positive. Thus, by the Liapunov criteria, the critical point at the origin is unstable for the given system of ODEs.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Differential Equations
Differential equations play a pivotal role in sciences and engineering to model various phenomena. A differential equation expresses the rate of change of a quantity as a function of the quantity itself and possibly other variables. For instance, when we describe the growth of a population over time, we might use a differential equation to represent the rate at which the population increases.

In the given exercise, the system of ordinary differential equations (ODEs) comprises two equations that define how two variables, x and y, change with respect to time. Solving these equations often involves finding functions of x and y that satisfy the given relationships. A unique aspect of differential equations is their ability to describe dynamical systems, which are systems that change over time. Here, the given equations suggest a complex interaction between x and y, with their rates of change depending on cubic powers and products of both variables.
Critical Point
In the landscape of a differential equation, a critical point (or equilibrium point) is where the system doesn't change — it's in equilibrium. Identifying critical points is crucial because they highlight where something interesting happens; for example, a predator-prey model might have an equilibrium where both species coexist in balance.

Locating these critical points typically involves setting the rates of change to zero and solving the resulting algebraic equations. In our exercise, when we solve the given system of ODEs by setting the derivatives equal to zero, we find that the origin \(x=y=0\) is a critical point. This step is often the starting point for analyzing the behavior of the system near these points. Establishing the nature of a critical point as stable or unstable gives us powerful insights into how small changes can dissipate over time or escalate into larger fluctuations.
Stability Analysis
Stability analysis involves determining whether small perturbations or changes to a system will grow over time, leading to instability, or if the system will return to its original state, reflecting stability. This concept is essential across various fields, for instance, in mechanical engineering, the stability of a structure is critical to its safety and performance.

One method used in stability analysis for ODEs is the Liapunov function, a clever mathematical tool that helps us understand the behavior around critical points without solving the entire differential equation. In our textbook problem, we construct a Liapunov function with a specific form and determine conditions that show the origin is an unstable critical point. By testing if the Liapunov function grows over time (positive derivative), we can conclude that any slight disturbance will not settle down, thereby characterizing the critical point as unstable, which was the goal of our exercise.

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Most popular questions from this chapter

The system \\[ x^{\prime}=-y, \quad y^{\prime}=-\gamma y-x(x-0.15)(x-2) \\] results from an approximation to the Hodgkin-Huxley \(^{6}\) equations, which model the transmission of neural impulses along an axon. a. Find the critical points, and classify them by investigating the approximate linear system near each one. b. Draw phase portraits for \(\gamma=0.8\) and for \(\gamma=1.5\) c. Consider the trajectory that leaves the critical point (2, 0). Find the value of \(\gamma\) for which this trajectory ultimately approaches the origin as \(t \rightarrow \infty .\) Draw a phase portrait for this value of \(\gamma\).

Consider the competition between bluegill and redear mentioned in Problem 6. Suppose that \(\epsilon_{2} / \alpha_{2}>\epsilon_{1} / \sigma_{1}\) and \(\epsilon_{1} / \alpha_{1}>\epsilon_{2} / \sigma_{2}\) so, as shown in the text, there is a stable equilibrium point at which both species can coexist. It is convenient to rewrite the equations of Problem 6 in terms of the carrying capacity of the pond for bluegill \(\left(B=\epsilon_{1} / \sigma_{1}\right)\) in the absence of redear and its carrying capacity for redear \(\left(R=\epsilon_{2} / \sigma_{2}\right)\) in the absence of bluegill. a. Show that the equations of Problem 6 take the form $$\frac{d x}{d t}=\epsilon_{1} x\left(1-\frac{1}{B} x-\frac{\gamma_{1}}{B} y\right), \frac{d y}{d t}=\epsilon_{2} y\left(1-\frac{1}{R} y-\frac{\gamma_{2}}{R} x\right)$$ where \(\gamma_{1}=\alpha_{1} / \sigma_{1}\) and \(\gamma_{2}=\alpha_{2} / \sigma_{2} .\) Determine the coexistence equilibrium point \((X, Y)\) in terms of \(B, R, \gamma_{1},\) and \(\gamma_{2}\) b. Now suppose that an angler fishes only for bluegill with the effect that \(B\) is reduced. What effect does this have on the equilibrium populations? Is it possible, by fishing, to reduce the population of bluegill to such a level that they will die out?

(a) By solving Eq. (9) numerically show that the real part of the complex roots changes sign when \(r \cong 24.737\). (b) Show that a cubic polynomial \(x^{3}+A x^{2}+B x+C\) has one real zero and two pure imaginary zeros only if \(A B=C\). (c) By applying the result of part (b) to Eq. (9) show that the real part of the complex roots changes sign when \(r=470 / 19\).

(a) Find an equation of the form \(H(x, y)=c\) satisfied by the trajectories. (b) Plot several level curves of the function \(H\). These are trajectories of the given system. Indicate the direction of motion on each trajectory. $$ d x / d t=2 y, \quad d y / d t=-8 x $$

The equation of motion of an undamped pendulum is \(d^{2} \theta / d t^{2}+\omega^{2} \sin \theta=0,\) where \(\omega^{2}=g / L .\) Let \(x=\theta, y=d \theta / d t\) to obtain the system of equations $$ d x / d t=y, \quad d y / d t=-\omega^{2} \sin x $$ (a) Show that the critical points are \((\pm n \pi, 0), n=0,1,2, \ldots,\) and that the system is almost lincar in the neighborhood of cach critical point. (b) Show that the critical point \((0,0)\) is a (stable) center of the corresponding linear system. Using Theorem 9.3.2 what can be said about the nonlinear system? The situation is similar at the critical points \((\pm 2 n \pi, 0), n=1,2,3, \ldots\) What is the physical interpretation of these critical points? (c) Show that the critical point \((\pi, 0)\) is an (unstable) saddle point of the corresponding linear system. What conclusion can you draw about the nonlinear system? The situation is similar at the critical points \([\pm(2 n-1) \pi, 0], n=1,2,3, \ldots\) What is the physical interpretation of these critical points? (d) Choose a value for \(\omega^{2}\) and plot a few trajectories of the nonlinear system in the neighborhood of the origin. Can you now draw any further conclusion about the nature of the critical point at \((0,0)\) for the nonlinear system? (e) Using the value of \(\omega^{2}\) from part (d) draw a phase portrait for the pendulum. Compare your plot with Figure 9.3 .5 for the damped pendulum.

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