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In Exercises 3–6, assume that any initial vector \({x_0}\) has an eigenvector decomposition such that the coefficient \({c_1}\) in equation (1) of this section is positive.

5. In old-growth forests of Douglas fir, the spotted owl dines mainly on flying squirrels. Suppose the predator–prey matrix for these two populations is \(A = \left( {\begin{aligned}{}{.4}&{}&{.3}\\{ - p}&{}&{1.2}\end{aligned}} \right)\).Show that if the predation parameter p is .325, both populations grow. Estimate the long-term growth rate and the eventual ratio of owls to flying squirrels.

Short Answer

Expert verified

There will be around 6 spotted owls for every 13 (thousand) flying squirrels in the future.

Step by step solution

01

Find the eigenvalue

Given the value is \(A = \left( {\begin{aligned}{}{.4}&{}&{.3}\\{ - .325}&{}&{1.2}\end{aligned}} \right)\).

For finding eigenvalue,

\(\det \left( {A - \lambda I} \right) = \left( {\begin{aligned}{}{0.4 - \lambda }&{0.3}\\{ - 0.125}&{1.2 - \lambda }\end{aligned}} \right)\)

So, the characteristics equation is,

\(\)

\(\begin{aligned}{}0 &= \left( {0.4 - \lambda } \right)\left( {1.2 - \lambda } \right) - \left( {0.3} \right)\left( { - 0.125} \right)\\0 &= {\lambda ^2} - 1.6\lambda + .5775\end{aligned}\)

Solve the roots.

\(\begin{aligned}{}\lambda &= \frac{{1.6 \pm \sqrt {{{1.6}^2} - 4\left( {.5775} \right)} }}{2}\\\lambda &= \frac{{1.6 \pm \sqrt {.25} }}{2}\\\lambda &= 1.05,0.55\end{aligned}\)

Because one of the eigenvalues is greater than one, both populations expand. The entries in the eigenvector correspond to \(1.05\) define their respective sizes at the end.

02

Find the eigenvector 

So, for \(\lambda = 1.05\), find eigenvector as:

\(\begin{aligned}{}\left( {A - 1.05I} \right) &= 0\\{E_1} &= \left( {\begin{aligned}{}{ - 0.65}&{}&{0.3}&{}&0\\{ - 0.325}&{}&{.15}&{}&0\end{aligned}} \right)\\{E_1} &= \left( {\begin{aligned}{}{ - 13}&{}&6&{}&0\\0&{}&0&{}&0\end{aligned}} \right)\end{aligned}\)

An eigenvector is \({\rm{v}} = \left( {\begin{aligned}{}6\\{13}\end{aligned}} \right)\).

There will be around 6 spotted owls for every 13 (thousand) flying squirrels in the future.

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Most popular questions from this chapter

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

15. \(\left[ {\begin{array}{*{20}{c}}4&- 7&0&2\\0&3&- 4&6\\0&0&3&{ - 8}\\0&0&0&1\end{array}} \right]\)

Consider an invertible n × n matrix A such that the zero state is a stable equilibrium of the dynamical system x(t+1)=Ax(t)What can you say about the stability of the systems

x(t+1)=-Ax(t)

(M)Use a matrix program to diagonalize

\(A = \left( {\begin{aligned}{*{20}{c}}{ - 3}&{ - 2}&0\\{14}&7&{ - 1}\\{ - 6}&{ - 3}&1\end{aligned}} \right)\)

If possible. Use the eigenvalue command to create the diagonal matrix \(D\). If the program has a command that produces eigenvectors, use it to create an invertible matrix \(P\). Then compute \(AP - PD\) and \(PD{P^{{\bf{ - 1}}}}\). Discuss your results.

Question: Construct a random integer-valued \(4 \times 4\) matrix \(A\).

  1. Reduce \(A\) to echelon form \(U\) with no row scaling, and use \(U\) in formula (1) (before Example 2) to compute \(\det A\). (If \(A\) happens to be singular, start over with a new random matrix.)
  2. Compute the eigenvalues of \(A\) and the product of these eigenvalues (as accurately as possible).
  3. List the matrix \(A\), and, to four decimal places, list the pivots in \(U\) and the eigenvalues of \(A\). Compute \(\det A\) with your matrix program, and compare it with the products you found in (a) and (b).

Question: Is \(\left( {\begin{array}{*{20}{c}}1\\4\end{array}} \right)\) an eigenvalue of \(\left( {\begin{array}{*{20}{c}}{ - 3}&1\\{ - 3}&8\end{array}} \right)\)? If so, find the eigenvalue.

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