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.

3. Determine the evolution of the dynamical system in Example 1 when the predation parameter p is .2 in equation (3). (Give a formula for \({x_k}\).) Does the owl population grow or decline? What about the wood rat population?

\(\)

Short Answer

Expert verified

The general solution is \({{\rm{x}}_k} = {c_1}{\left( {.9} \right)^k}{{\rm{v}}_1} + {c_2}{\left( {.7} \right)^k}{{\rm{v}}_2}\). For every value of \({c_1}\) and \({c_2}\), the owl population and wood rat population decline over time.

Step by step solution

01

Given term for the vector and a matrix 

The owl and wood rat populations at time k are described by \({{\rm{x}}_k} = \left( {\begin{aligned}{}{{O_k}}\\{{R_k}}\end{aligned}} \right)\), where k is the number of months in a year, and the number of owls in the study area is \({O_k}\), while the number of rats is \({R_k}\) (measured in thousands). Because owls consume rats, the population of one species should have an impact on the other.

The changes in these populations can be described by the equations:

\(\)\(\begin{aligned}{}{O_{k + 1}} = \left( {0.5} \right){O_k} + \left( {0.4} \right){R_k}\\{R_{k + 1}} = \left( { - p} \right){O_k} + \left( {1.1} \right){R_k}\end{aligned}\)

Where p is a positive parameter to be specified.

In the matrix form

\({{\rm{x}}_{k + 1}} = \left( {\begin{aligned}{}{0.5}&{}&{0.4}\\{ - p}&{}&{1.1}\end{aligned}} \right){{\rm{x}}_k}\)

Put the value of p in the above matrix.

\(A = \left( {\begin{aligned}{}{0.5}&{}&{0.4}\\{ - 0.2}&{}&{1.1}\end{aligned}} \right)\)

02

Find the eigenvalue

For finding eigenvalue:

\(\begin{aligned}{}\det \left( {a - \lambda I} \right) &= \left( {.5 - \lambda } \right)\left( {1.1 - \lambda } \right) + 0.08\\ &= {\lambda ^2} - 1.6\lambda + .63\\ &= (\lambda - .9)\left( {\lambda - .7} \right)\end{aligned}\)

So, the eigenvalues are \(.9\) and \(.7\) .

If \({{\rm{v}}_1}\) and \({{\rm{v}}_2}\) are the eigenvector and if \({{\rm{x}}_0} = {c_1}{{\rm{v}}_1} + {c_2}{{\rm{v}}_2}\).

Then,

\(\begin{aligned}{}{{\rm{x}}_1} &= A\left( {{c_1}{{\rm{v}}_1} + {c_2}{{\rm{v}}_2}} \right)\\{{\rm{x}}_1} &= A{c_1}{{\rm{v}}_1} + A{c_2}{{\rm{v}}_2}\\{{\rm{x}}_1} &= {c_1}\left( {.9} \right){{\rm{v}}_1} + {c_2}\left( {.7} \right){{\rm{v}}_2}\end{aligned}\)

This implies that the general solution is \({{\rm{x}}_k} = {c_1}{\left( {.9} \right)^k}{{\rm{v}}_1} + {c_2}{\left( {.7} \right)^k}{{\rm{v}}_2}\).

Both the owl and wood rat populations fall over time for any of the \({c_1}\) and \({c_2}\) options.

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Most popular questions from this chapter

Show that if \(A\) is diagonalizable, with all eigenvalues less than 1 in magnitude, then \({A^k}\) tends to the zero matrix as \(k \to \infty \). (Hint: Consider \({A^k}x\) where \(x\) represents any one of the columns of \(I\).)

19–23 concern the polynomial \(p\left( t \right) = {a_{\bf{0}}} + {a_{\bf{1}}}t + ... + {a_{n - {\bf{1}}}}{t^{n - {\bf{1}}}} + {t^n}\) and \(n \times n\) matrix \({C_p}\) called the companion matrix of \(p\): \({C_p} = \left( {\begin{aligned}{*{20}{c}}{\bf{0}}&{\bf{1}}&{\bf{0}}&{...}&{\bf{0}}\\{\bf{0}}&{\bf{0}}&{\bf{1}}&{}&{\bf{0}}\\:&{}&{}&{}&:\\{\bf{0}}&{\bf{0}}&{\bf{0}}&{}&{\bf{1}}\\{ - {a_{\bf{0}}}}&{ - {a_{\bf{1}}}}&{ - {a_{\bf{2}}}}&{...}&{ - {a_{n - {\bf{1}}}}}\end{aligned}} \right)\).

23. Let \(p\) be the polynomial in Exercise \({\bf{22}}\), and suppose the equation \(p\left( t \right) = {\bf{0}}\) has distinct roots \({\lambda _{\bf{1}}},{\lambda _{\bf{2}}},{\lambda _{\bf{3}}}\). Let \(V\) be the Vandermonde matrix

\(V{\bf{ = }}\left( {\begin{aligned}{*{20}{c}}{\bf{1}}&{\bf{1}}&{\bf{1}}\\{{\lambda _{\bf{1}}}}&{{\lambda _{\bf{2}}}}&{{\lambda _{\bf{3}}}}\\{\lambda _{\bf{1}}^{\bf{2}}}&{\lambda _{\bf{2}}^{\bf{2}}}&{\lambda _{\bf{3}}^{\bf{2}}}\end{aligned}} \right)\)

(The transpose of \(V\) was considered in Supplementary Exercise \({\bf{11}}\) in Chapter \({\bf{2}}\).) Use Exercise \({\bf{22}}\) and a theorem from this chapter to deduce that \(V\) is invertible (but do not compute \({V^{{\bf{ - 1}}}}\)). Then explain why \({V^{{\bf{ - 1}}}}{C_p}V\) is a diagonal matrix.

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

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

Question: Is \(\lambda = 2\) an eigenvalue of \(\left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right)\)? Why or why not?

Assume the mapping\(T:{{\rm P}_2} \to {{\rm P}_{\bf{2}}}\)defined by \(T\left( {{a_0} + {a_1}t + {a_2}{t^2}} \right) = 3{a_0} + \left( {5{a_0} - 2{a_1}} \right)t + \left( {4{a_1} + {a_2}} \right){t^2}\) is linear. Find the matrix representation of\(T\) relative to the bases \(B = \left\{ {1,t,{t^2}} \right\}\).

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