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

10. \(A = \left( {\begin{aligned}{}{.3}&{}&{.4}\\{ - .3}&{}&{1.1}\end{aligned}} \right)\).

\(\)

Short Answer

Expert verified

The direction of greatest attraction and or repulsion is eigenvector \({v_1}\)and.

Here, \({{\rm{v}}_1} = \left( {\begin{aligned}{}2\\1\end{aligned}} \right)\).

Step by step solution

01

Find eigenvalue

\(A = \left( {\begin{aligned}{}{.3}&{}&{ - .4}\\{ - .3}&{}&{1.1}\end{aligned}} \right)\)

For finding eigenvalue,

\(\begin{aligned}{}det\left( {{\rm{A - }}\lambda {\rm{I }}} \right){\rm{ = }}\left( {\left( {{\rm{.3 - }}\lambda } \right)\left( {1.1 - \lambda } \right)} \right) - \left( {\left( { - .3} \right)\left( { - .4} \right)} \right)\\0 = {\lambda ^2}{\rm{ - 1}}{\rm{.4}}\lambda {\rm{ + }}{\rm{.45 }}\end{aligned}\)

From this characteristic equation,

\(\begin{aligned}{}\lambda &= \frac{{1.4 \pm \sqrt {{{1.4}^2} - 4\left( {.45} \right)} }}{2}\\ &= \frac{{1.4 \pm \sqrt {1.6} }}{2}\\ &= \frac{{1.4 \pm .4}}{2}\\ &= 0.5,0.9\end{aligned}\)

The origin is an attractor because both eigenvalues are less than 1 in magnitude.

02

Find the eigenvector 

The direction of the greatest repulsion is through the origin and the eigenvector \({{\rm{v}}_1}\).

\(\begin{aligned}{}\left( {A - 2I} \right)x = 0\\\left( {\begin{aligned}{}{ - .2}&{}&{ - .4}&{}&0\\{ - .3}&{}&{ - .6}&{}&0\end{aligned}} \right) \sim \left( {\begin{aligned}{}1&{}&{ - 2}&{}&0\\0&{}&0&{}&0\end{aligned}} \right)\end{aligned}\)

So, \({x_1} = 2{x_2}\).

So, \({x_2}\) is free.

Hence eigenvector \({{\rm{v}}_1} = \left( {\begin{aligned}{}2\\1\end{aligned}} \right)\).

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Most popular questions from this chapter

[M] In Exercises 19 and 20, find (a) the largest eigenvalue and (b) the eigenvalue closest to zero. In each case, set \[{{\bf{x}}_{\bf{0}}}{\bf{ = }}\left( {{\bf{1,0,0,0}}} \right)\] and carry out approximations until the approximating sequence seems accurate to four decimal places. Include the approximate eigenvector.

20. \[A{\bf{ = }}\left[ {\begin{array}{*{20}{c}}{\bf{1}}&{\bf{2}}&{\bf{3}}&{\bf{2}}\\{\bf{2}}&{{\bf{12}}}&{{\bf{13}}}&{{\bf{11}}}\\{{\bf{ - 2}}}&{\bf{3}}&{\bf{0}}&{\bf{2}}\\{\bf{4}}&{\bf{5}}&{\bf{7}}&{\bf{2}}\end{array}} \right]\]

Suppose \({\bf{x}}\) is an eigenvector of \(A\) corresponding to an eigenvalue \(\lambda \).

a. Show that \(x\) is an eigenvector of \(5I - A\). What is the corresponding eigenvalue?

b. Show that \(x\) is an eigenvector of \(5I - 3A + {A^2}\). What is the corresponding eigenvalue?

Question: For the matrices in Exercises 15-17, list the eigenvalues, repeated according to their multiplicities.

17. \(\left[ {\begin{array}{*{20}{c}}3&0&0&0&0\\- 5&1&0&0&0\\3&8&0&0&0\\0&- 7&2&1&0\\- 4&1&9&- 2&3\end{array}} \right]\)

Question: Find the characteristic polynomial and the eigenvalues of the matrices in Exercises 1-8.

6. \(\left[ {\begin{array}{*{20}{c}}3&- 4\\4&8\end{array}} \right]\)

Question: In Exercises \({\bf{5}}\) and \({\bf{6}}\), the matrix \(A\) is factored in the form \(PD{P^{ - {\bf{1}}}}\). Use the Diagonalization Theorem to find the eigenvalues of \(A\) and a basis for each eigenspace.

6. \(\left( {\begin{array}{*{20}{c}}{\bf{4}}&{\bf{0}}&{{\bf{ - 2}}}\\{\bf{2}}&{\bf{5}}&{\bf{4}}\\{\bf{0}}&{\bf{0}}&{\bf{5}}\end{array}} \right){\bf{ = }}\left( {\begin{array}{*{20}{c}}{{\bf{ - 2}}}&{\bf{0}}&{{\bf{ - 1}}}\\{\bf{0}}&{\bf{1}}&{\bf{2}}\\{\bf{1}}&{\bf{0}}&{\bf{0}}\end{array}} \right)\left( {\begin{array}{*{20}{c}}{\bf{5}}&{\bf{0}}&{\bf{0}}\\{\bf{0}}&{\bf{5}}&{\bf{0}}\\{\bf{0}}&{\bf{0}}&4\end{array}} \right)\left( {\begin{array}{*{20}{c}}{\bf{0}}&{\bf{0}}&{\bf{1}}\\{\bf{2}}&{\bf{1}}&{\bf{4}}\\{{\bf{ - 1}}}&{\bf{0}}&{{\bf{ - 2}}}\end{array}} \right)\)

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