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In Exercises 9–14, classify the origin as an attractor, repeller, or saddle point of the dynamical system \({{\bf{x}}_{k + 1}} = A{{\bf{x}}_k}\). Find the directions of greatest attraction and/or repulsion.

\(A = \left( {\begin{aligned}{}{.4}&{}&{.5}\\{ - .4}&{}&{1.3}\end{aligned}} \right)\)

Short Answer

Expert verified

The origin is an attractor as two eigenvalues obtained, that is, \(0.8{\rm{ and }}0.9\) are less than 1. The direction of the greatest attraction passes through the origin, which is \({v_1} = \left( {\begin{aligned}{}5\\4\end{aligned}} \right)\).

Step by step solution

01

Discrete Dynamical System 

For any Dynamical System with matrix \(A\), the eigenvalues of \(A\) will determine the system of:

\(\begin{aligned}{}{\rm{Greatest attraction }} \to {\rm{if }}\lambda < 1\\{\rm{Greatest repulsion }} \to {\rm{if }}\lambda > 1\end{aligned}\)

02

Evaluating Eigenvalues of the given matrix 

The given matrix for the dynamic system \({{\bf{x}}_{k + 1}} = A{{\bf{x}}_k}\) is:

\(A = \left( {\begin{aligned}{}{.4}&{}{.5}\\{ - .4}&{}&{1.3}\end{aligned}} \right)\)

For eigenvalues, we have:

\(\begin{aligned}{}\det \left( {A - \lambda I} \right) = 0\\\left| {\begin{aligned}{}{0.4 - \lambda }&{}&{0.5}\\{ - 0.4}&{}&{1.3 - \lambda }\end{aligned}} \right| = 0\\\left( {0.4 - \lambda } \right)\left( {1.3 - \lambda } \right) + 0.2 = 0\\{\lambda ^2} - 1.7\lambda + 0.72 = 0\\\lambda = 0.8,0.9\end{aligned}\)

Here, both eigenvalues are less than 1.

Hence, the origin is an attractor for the given system

03

Eigenvector for eigenvalue 0.8

Now, for eigenvectors, we have:

At \(\lambda = 0.8\).

\(\begin{aligned}{}\left( {A - \left( {0.8} \right)I} \right){v_1} = 0\\\left( {\begin{aligned}{}{0.4 - 0.8}&{}&{0.5}\\{ - 0.4}&{}&{1.3 - 0.8}\end{aligned}} \right)\left( {\begin{aligned}{}x\\y\end{aligned}} \right) = 0\\\left( {\begin{aligned}{}{ - 0.4}&{}&{0.5}\\{ - 0.4}&{}&{0.5}\end{aligned}} \right)\left( {\begin{aligned}{}x\\y\end{aligned}} \right) = 0\end{aligned}\)

Also, the system of equation will be:

\(\begin{aligned}{} - 0.4x + 0.5y = 0\\ - 0.4x + 0.5y = 0\end{aligned}\)

For this, the general solution, we have:

\(\begin{aligned}{}{v_1} &= \left( {\begin{aligned}{}x\\y\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}{\frac{5}{4}y}\\y\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}5\\4\end{aligned}} \right)\end{aligned}\)

Here, the eigenvector is: \({v_1} = \left( {\begin{aligned}{}5\\4\end{aligned}} \right)\).

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Most popular questions from this chapter

Let \(A{\bf{ = }}\left( {\begin{aligned}{*{20}{c}}{{a_{{\bf{11}}}}}&{{a_{{\bf{12}}}}}\\{{a_{{\bf{21}}}}}&{{a_{{\bf{22}}}}}\end{aligned}} \right)\). Recall from Exercise \({\bf{25}}\) in Section \({\bf{5}}{\bf{.4}}\) that \({\rm{tr}}\;A\) (the trace of \(A\)) is the sum of the diagonal entries in \(A\). Show that the characteristic polynomial of \(A\) is \({\lambda ^2} - \left( {{\rm{tr}}A} \right)\lambda + \det A\). Then show that the eigenvalues of a \({\bf{2 \times 2}}\) matrix \(A\) are both real if and only if \(\det A \le {\left( {\frac{{{\rm{tr}}A}}{2}} \right)^2}\).

Define\(T:{{\rm P}_3} \to {\mathbb{R}^4}\)by\(T\left( {\bf{p}} \right) = \left( {\begin{aligned}{{\bf{p}}\left( { - 3} \right)}\\{{\bf{p}}\left( { - 1} \right)}\\{{\bf{p}}\left( 1 \right)}\\{{\bf{p}}\left( 3 \right)}\end{aligned}} \right)\).

  1. Show that \(T\) is a linear transformation.
  2. Find the matrix for \(T\) relative to the basis \(\left\{ {1,t,{t^2},{t^3}} \right\}\)for \({{\rm P}_3}\)and the standard basis for \({\mathbb{R}^4}\).

[M]Repeat Exercise 25 for \[A{\bf{ = }}\left[ {\begin{array}{*{20}{c}}{{\bf{ - 8}}}&{\bf{5}}&{{\bf{ - 2}}}&{\bf{0}}\\{{\bf{ - 5}}}&{\bf{2}}&{\bf{1}}&{{\bf{ - 2}}}\\{{\bf{10}}}&{{\bf{ - 8}}}&{\bf{6}}&{{\bf{ - 3}}}\\{\bf{3}}&{{\bf{ - 2}}}&{\bf{1}}&{\bf{0}}\end{array}} \right]\].

Question: Is \(\left( {\begin{array}{*{20}{c}}1\\{ - 2}\\1\end{array}} \right)\) an eigenvector of\(\left){\begin{array}{*{20}{c}}3&6&7\\3&3&7\\5&6&5\end{array}} \right)\)? If so, find the eigenvalue.

Compute the quantities in Exercises 1-8 using the vectors

\({\mathop{\rm u}\nolimits} = \left( {\begin{array}{*{20}{c}}{ - 1}\\2\end{array}} \right),{\rm{ }}{\mathop{\rm v}\nolimits} = \left( {\begin{array}{*{20}{c}}4\\6\end{array}} \right),{\rm{ }}{\mathop{\rm w}\nolimits} = \left( {\begin{array}{*{20}{c}}3\\{ - 1}\\{ - 5}\end{array}} \right),{\rm{ }}{\mathop{\rm x}\nolimits} = \left( {\begin{array}{*{20}{c}}6\\{ - 2}\\3\end{array}} \right)\)

3. \(\frac{1}{{{\mathop{\rm w}\nolimits} \cdot {\mathop{\rm w}\nolimits} }}{\mathop{\rm w}\nolimits} \)

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