/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 16 Determine the critical point \(\... [FREE SOLUTION] | 91Ó°ÊÓ

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Determine the critical point \(\mathbf{x}=\mathbf{x}^{0},\) and then classify its type and examine its stability by making the transformation \(\mathbf{x}=\mathbf{x}^{0}+\mathbf{u} .\) \(\frac{d \mathbf{x}}{d t}=\left(\begin{array}{rr}{0} & {-\beta} \\ {\delta} & {0}\end{array}\right) \mathbf{x}+\left(\begin{array}{r}{\alpha} \\\ {-\gamma}\end{array}\right) ; \quad \alpha, \beta, \gamma, \delta>0\)

Short Answer

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Question: Determine the critical point of a linear system of first-order differential equations, classify its type, and examine its stability given the following differential equation: \( \frac{d\mathbf{x}}{dt} = \left(\begin{array}{rr}{0} & {-\beta} \\ {\delta} &{0}\end{array}\right) \mathbf{x}+\left(\begin{array}{r}{\alpha} \\ {-\gamma}\end{array}\right) \) Answer: The critical point is \(\mathbf{x}^0 = \left(\begin{array}{r}{\dfrac{\gamma}{\delta}} \\ {\dfrac{\alpha}{\beta}}\end{array}\right)\) and it is a center with neutral stability.

Step by step solution

01

Find the critical point \(\mathbf{x}^0\)

To determine the critical point \(\mathbf{x}^0\), we set the rate of change \(\frac{d \mathbf{x}}{d t}\) to zero and solve for \(\mathbf{x}\): $$\frac{d \mathbf{x}}{d t}=\left(\begin{array}{rr}{0} & {-\beta} \\\ {\delta} &{0}\end{array}\right) \mathbf{x}+\left(\begin{array}{r}{\alpha} \\\ {-\gamma}\end{array}\right) = \mathbf{0}$$ To find \(\mathbf{x}^0\), we can solve the linear system: $$\left(\begin{array}{rr}{0} & {-\beta} \\\ {\delta} &{0}\end{array}\right) \mathbf{x}^0 = -\left(\begin{array}{r}{\alpha} \\\ {-\gamma}\end{array}\right)$$ This yields the following system of linear equations: $$\begin{cases} -\beta x_2^0 = -\alpha \\ \delta x_1^0 = \gamma \end{cases}$$ From this system, we can solve for \(x_1^0\) and \(x_2^0\) as follows: $$x_1^0 = \frac{\gamma}{\delta}, \quad x_2^0 = \frac{\alpha}{\beta}$$ Therefore, the critical point is: $$\mathbf{x}^0 = \left(\begin{array}{r}{\dfrac{\gamma}{\delta}} \\\ {\dfrac{\alpha}{\beta}}\end{array}\right)$$
02

Perform the transformation \(\mathbf{x} = \mathbf{x}^0 + \mathbf{u}\)

Now, we will make the transformation \(\mathbf{x} = \mathbf{x}^0 + \mathbf{u}\) and find the resulting system in terms of \(\mathbf{u}\): $$\frac{d (\mathbf{x}^0 + \mathbf{u})}{d t} = \left(\begin{array}{rr}{0} & {-\beta} \\\ {\delta} &{0}\end{array}\right) (\mathbf{x}^0 + \mathbf{u})+\left(\begin{array}{r}{\alpha} \\\ {-\gamma}\end{array}\right)$$ Since \(\frac{d \mathbf{x}^0}{d t} = \mathbf{0}\), we have \(\frac{d \mathbf{u}}{d t} = \frac{d (\mathbf{x}^0 + \mathbf{u})}{d t}\) and rewrite the system in terms of \(\mathbf{u}\): $$\frac{d \mathbf{u}}{d t} = \left(\begin{array}{rr}{0} & {-\beta} \\\ {\delta} &{0}\end{array}\right) \mathbf{u}$$
03

Analyze eigenvalues and classify the critical point

To examine the stability and type of the critical point, we can analyze the eigenvalues of the system matrix. We first need to find the characteristic equation of the matrix: $$\begin{vmatrix}{-\lambda} & {-\beta} \\\ {\delta} & {-\lambda}\end{vmatrix} = \lambda^2 + \beta \delta = 0$$ The eigenvalues \(\lambda\) are given by the following equation: $$\lambda^2 = -\beta \delta$$ or $$\lambda = \pm \sqrt{-\beta \delta} i$$ Since the eigenvalues are purely imaginary and have zero real parts, the critical point \(\mathbf{x}^0\) is a center, meaning that trajectories near the critical point are periodic and closed. The system is neutrally stable, as perturbations neither grow nor decay in amplitude. In summary, the critical point \(\mathbf{x}^0 = \left(\begin{array}{r}{\dfrac{\gamma}{\delta}} \\\ {\dfrac{\alpha}{\beta}}\end{array}\right)\) is a center with neutral stability.

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Key Concepts

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

Stability Analysis
Stability analysis in differential equations involves assessing if a system returns to equilibrium after a small disturbance. This is crucial to determine whether solutions to the system are predictable in the long term. When we explore stability, we analyze the behavior of solutions in the vicinity of a critical point, which in our case is \(\mathbf{x}^0 = \left(\begin{array}{r}{\dfrac{\gamma}{\delta}} \ {\dfrac{\alpha}{\beta}}\end{array}\right)\).
  • If perturbations decay over time, the system is stable.
  • If they grow, it is unstable.
  • If they neither grow nor decay, as in this case, the system is neutrally stable.
This means minor disturbances do not lead to changes in amplitude but are periodic and closed around the critical point. This neutral behavior is an important attribute in physical systems, indicating that like a pendulum, the system maintains its oscillatory nature over time without losing energy.
Linear Systems
Linear systems can be expressed in terms of matrices and vectors. In our exercise, the system is given by a matrix \[ \left(\begin{array}{rr}{0} & {-\beta} \ {\delta} & {0}\end{array}\right) \].

The governing differential equation, \( \frac{d \mathbf{x}}{dt} = \mathbf{A}\mathbf{x} + \mathbf{b} \), where \( \mathbf{A} \) is the coefficient matrix and \( \mathbf{b} \) is a constant vector, shows us how the variables change with time.
  • For linear systems, solutions can often be found using straightforward algebraic techniques.
  • This allows for a more predictable understanding of system behavior.
Understanding linear systems provides us with insight into more complex, nonlinear behaviors encountered in real-world phenomena.
The linear approximation often serves to simplify the stability analysis, as it highlights key behaviors without complicating calculations.
Eigenvalues
Eigenvalues are key components in understanding the behavior of a system. They tell us about the nature of equilibrium points and system stability. Here, we calculated the eigenvalues by solving the characteristic equation:
\[ \lambda^2 + \beta \delta = 0 \].
The resulting eigenvalues are purely imaginary, \( \lambda = \pm \sqrt{-\beta \delta} i \).
This finding is pivotal because
  • it indicates oscillatory behavior around the critical point.
  • The lack of a real part in the eigenvalues signifies that disturbances do not amplify or diminish over time, thereby asserting neutral stability.
Moreover, eigenvalues classified the critical point as a "center," a specific type of equilibrium that indicates perpetual cycles or orbits.
The system's behavior can hence be likened to circular motion, without any spiraling inward or outward movements.
Phase Space Analysis
Phase space is a crucial concept in analyzing dynamic systems by representing all possible states of the system. Each point in this space represents a unique state, defined here through the variables \( x_1 \) and \( x_2 \). In our example, the transformation \( \mathbf{x} = \mathbf{x}^0 + \mathbf{u} \) helps in simplifying our analysis to focus on deviations from the critical point.

Phase space analysis visualizes how systems evolve over time. It provides a comprehensive picture of the system's behavior and stability by showing trajectories of states.

Features of phase space include:
  • Trajectories illustrate how starting at one point will take you to another, highlighting paths of future states.
  • In our case, the closed, circular trajectories indicate a neutrally stable center.
Through phase space, complex movements in a system are represented graphically, which aids in qualitatively understanding the dynamics without solving complex equations.

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Most popular questions from this chapter

Can be interpreted as describing the interaction of two species with population densities \(x\) and \(y .\) In each of these problems carry out the following steps. (a) Draw a direction field and describe how solutions seem to behave. (b) Find the critical points. (c) For each critical point find the corresponding linear system. Find the eigenvalues and eigenvectors of the linear system; classify each critical point as to type, and determine whether it is asymptotically stable, or unstable. (d) Sketch the trajectories in the neighborhood of each critical point. (e) Draw a phase portrait for the system. (f) Determine the limiting behavior of \(x\) and \(y\) as \(t \rightarrow \infty\) and interpret the results in terms of the populations of the two species. $$ \begin{array}{l}{d x / d t=x(1-0.5 y)} \\ {d y / d t=y(-0.25+0.5 x)}\end{array} $$

(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. $$ d x / d t=2 y, \quad d y / d t=8 x $$

Verify that \((0,0)\) is a critical point, show that the system is almost linear, and discuss the type and stability of the critical point \((0,0)\) by examining the corresponding linear system. $$ d x / d t=x+y^{2}, \quad d y / d t=x+y $$

(a) Find all the critical points (equilibrium solutions). (b) Use a computer to draw a direction field and portrait for the system. (c) From the plot(s) in part (b) determine whether each critical point is asymptotically stable, stable, or unstable, and classify it as to type. $$ d x / d t=y(2-x-y), \quad d y / d t=-x-y-2 x y $$

(a) Find the eigenvalues and eigenvectors. (b) Classify the critical point \((0,0)\) as to type and determine whether it is stable, asymptotically stable, or unstable. (c) Sketch several trajectories in the phase plane and also sketch some typical graphs of \(x_{1}\) versus \(t .\) (d) Use a computer to plot accurately the curves requested in part (c). \(\frac{d \mathbf{x}}{d t}=\left(\begin{array}{ll}{3} & {-4} \\ {1} & {-1}\end{array}\right) \mathbf{x}\)

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