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14.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.

\(A = \left( {\begin{aligned}{}{1.7}&{}&{.6}\\{ - .4}&{}&{.7}\end{aligned}} \right)\)

Short Answer

Expert verified

The origin is a repellor as both the eigenvalues are greater than one.

The direction of greatest repulsion is through the origin and eigenvector: \({v_1} = \left( {\begin{aligned}{}{ - 3}\\2\end{aligned}} \right)\).

Step by step solution

01

Discrete Dynamical System

For anyDynamical Systemwith matrix \(A\), the eigenvalues of \(A\) will determine the system of:

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

02

Evaluating Eigenvalues of a given matrix

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

\(A = \left( {\begin{aligned}{}{1.7}&{}&{.6}\\{ - .4}&{}&{.7}\end{aligned}} \right)\)

For eigenvalues, we have:

\(\begin{aligned}{}\det \left( {A - \lambda I} \right) = 0\\\left| {\begin{aligned}{}{1.7 - \lambda }&{}&{0.6}\\{ - 0.4}&{}&{0.7 - \lambda }\end{aligned}} \right| = 0\\{\lambda ^2} - 2.4\lambda + 1.43 = 0\\\lambda = 1.1,1.3\end{aligned}\)

Here, both the eigenvalues are greater than 1.

Hence, the origin is a repellor.

03

Eigenvector for eigenvalue 1.3

Now, for eigenvectors, we have:

At \(\lambda = 1.3\).

\(\begin{aligned}{}\left( {A - \left( {1.3} \right)I} \right){v_1} &= 0\\\left( {\begin{aligned}{}{1.7 - 1.3}&{}&{0.6}\\{ - 0.4}&{}&{0.7 - 1.3}\end{aligned}} \right)\left( {\begin{aligned}{}x\\y\end{aligned}} \right) &= 0\\\left( {\begin{aligned}{}{0.4}&{}&{0.6}\\{ - 0.4}&{}&{ - 0.6}\end{aligned}} \right)\left( {\begin{aligned}{}x\\y\end{aligned}} \right) &= 0\end{aligned}\)

Also, the system of equationwill be:

\(\begin{aligned}{}0.4x + 0.6y = 0\\ - 0.4x - 0.6y = 0\end{aligned}\)

For this, the general solution, we have:

\(\begin{aligned}{}{v_1} &= \left( {\begin{aligned}{}x\\y\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}{ - \frac{6}{4}y}\\y\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}{ - 6}\\4\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}{ - 3}\\2\end{aligned}} \right)\end{aligned}\)

Here, the eigenvector is: \({v_1} = \left( {\begin{aligned}{}{ - 3}\\2\end{aligned}} \right)\).

Hence, the direction of greatest repulsion is through the origin and eigenvector: \({v_1} = \left( {\begin{aligned}{}{ - 3}\\2\end{aligned}} \right)\).

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Most popular questions from this chapter

Question: Diagonalize the matrices in Exercises \({\bf{7--20}}\), if possible. The eigenvalues for Exercises \({\bf{11--16}}\) are as follows:\(\left( {{\bf{11}}} \right)\lambda {\bf{ = 1,2,3}}\); \(\left( {{\bf{12}}} \right)\lambda {\bf{ = 2,8}}\); \(\left( {{\bf{13}}} \right)\lambda {\bf{ = 5,1}}\); \(\left( {{\bf{14}}} \right)\lambda {\bf{ = 5,4}}\); \(\left( {{\bf{15}}} \right)\lambda {\bf{ = 3,1}}\); \(\left( {{\bf{16}}} \right)\lambda {\bf{ = 2,1}}\). For exercise \({\bf{18}}\), one eigenvalue is \(\lambda {\bf{ = 5}}\) and one eigenvector is \(\left( {{\bf{ - 2,}}\;{\bf{1,}}\;{\bf{2}}} \right)\).

8. \(\left( {\begin{array}{*{20}{c}}{\bf{5}}&{\bf{1}}\\{\bf{0}}&{\bf{5}}\end{array}} \right)\)

Let \(J\) be the \(n \times n\) matrix of all \({\bf{1}}\)’s and consider \(A = \left( {a - b} \right)I + bJ\) that is,

\(A = \left( {\begin{aligned}{*{20}{c}}a&b&b&{...}&b\\b&a&b&{...}&b\\b&b&a&{...}&b\\:&:&:&:&:\\b&b&b&{...}&a\end{aligned}} \right)\)

Use the results of Exercise \({\bf{16}}\) in the Supplementary Exercises for Chapter \({\bf{3}}\) to show that the eigenvalues of \(A\) are \(a - b\) and \(a + \left( {n - {\bf{1}}} \right)b\). What are the multiplicities of these eigenvalues?

Question: In Exercises 21 and 22, \(A\) and \(B\) are \(n \times n\) matrices. Mark each statement True or False. Justify each answer.

  1. The determinant of \(A\) is the product of the diagonal entries in \(A\).
  2. An elementary row operation on \(A\) does not change the determinant.
  3. \(\left( {\det A} \right)\left( {\det B} \right) = \det AB\)
  4. If \(\lambda + 5\) is a factor of the characteristic polynomial of \(A\), then 5 is an eigenvalue of \(A\).

Question: Let \(A = \left( {\begin{array}{*{20}{c}}{.5}&{.2}&{.3}\\{.3}&{.8}&{.3}\\{.2}&0&{.4}\end{array}} \right)\), \({{\rm{v}}_1} = \left( {\begin{array}{*{20}{c}}{.3}\\{.6}\\{.1}\end{array}} \right)\), \({{\rm{v}}_2} = \left( {\begin{array}{*{20}{c}}1\\{ - 3}\\2\end{array}} \right)\), \({{\rm{v}}_3} = \left( {\begin{array}{*{20}{c}}{ - 1}\\0\\1\end{array}} \right)\) and \({\rm{w}} = \left( {\begin{array}{*{20}{c}}1\\1\\1\end{array}} \right)\).

  1. Show that \({{\rm{v}}_1}\), \({{\rm{v}}_2}\), and \({{\rm{v}}_3}\) are eigenvectors of \(A\). (Note: \(A\) is the stochastic matrix studied in Example 3 of Section 4.9.)
  2. Let \({{\rm{x}}_0}\) be any vector in \({\mathbb{R}^3}\) with non-negative entries whose sum is 1. (In section 4.9, \({{\rm{x}}_0}\) was called a probability vector.) Explain why there are constants \({c_1}\), \({c_2}\), and \({c_3}\) such that \({{\rm{x}}_0} = {c_1}{{\rm{v}}_1} + {c_2}{{\rm{v}}_2} + {c_3}{{\rm{v}}_3}\). Compute \({{\rm{w}}^T}{{\rm{x}}_0}\), and deduce that \({c_1} = 1\).
  3. For \(k = 1,2, \ldots ,\) define \({{\rm{x}}_k} = {A^k}{{\rm{x}}_0}\), with \({{\rm{x}}_0}\) as in part (b). Show that \({{\rm{x}}_k} \to {{\rm{v}}_1}\) as \(k\) increases.

Question: In Exercises \({\bf{1}}\) and \({\bf{2}}\), let \(A = PD{P^{ - {\bf{1}}}}\) and compute \({A^{\bf{4}}}\).

2. \(P{\bf{ = }}\left( {\begin{array}{*{20}{c}}2&{ - 3}\\{ - 3}&5\end{array}} \right)\), \(D{\bf{ = }}\left( {\begin{array}{*{20}{c}}{\bf{1}}&{\bf{0}}\\{\bf{0}}&{\frac{{\bf{1}}}{{\bf{2}}}}\end{array}} \right)\)

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