Question 16:Produce the general solution of the dynamical system \({x_{k + 1}} = A{x_k}\) when \(A\) is the stochastic matrix for the Hertz Rent A Car model in Exercise 16 of Section 4.9.

Short Answer

Expert verified

The required general solution is \({\bf{x}}\left( t \right) = {c_1}{{\bf{v}}_1} + {c_2}{\left( {0.89} \right)^k}{{\bf{v}}_2} + {c_3}{\left( {0.81} \right)^k}{{\bf{v}}_3}\).

Step by step solution

01

Determine the eigenvalues and eigenvector of the matrix using MATLAB

From exercise 16, we have:

\(A = \left( {\begin{array}{*{20}{c}}{0.90}&{0.01}&{0.09}\\{0.01}&{0.90}&{0.01}\\{0.09}&{0.09}&{0.90}\end{array}} \right)\)

Enter this matrix in MATLAB as:

>> \(A = \left( {\begin{array}{*{20}{c}}{0.90\,\,0.01\,\,0.09;}&{0.01\,\,0.90\,\,0.01;}&{0.09\,\,0.09\,\,0.90}\end{array}} \right)\);

For eigenvalues, enter instruction as:

>> \(E = {\rm{eigs}}\left( A \right)\);

We get:

\(E = \left( {\begin{array}{*{20}{c}}{0.81}\\{0.89}\\{1.00}\end{array}} \right)\)

Now, for eigenvectors, enter instruction as:

>> \(\left( {\begin{array}{*{20}{c}}A&B\end{array}} \right) = {\rm{eigs}}\left( A \right)\);

So, we have:

\(\begin{array}{l}{v_1} = \left( {\begin{array}{*{20}{c}}{ - 0.6700}\\{ - 0.1399}\\{ - 0.7289}\end{array}} \right)\\{v_2} = \left( {\begin{array}{*{20}{c}}{0.7071}\\{ - 0.7071}\\{ - 0.0000}\end{array}} \right)\\{v_3} = \left( {\begin{array}{*{20}{c}}{ - 0.7071}\\{0.0000}\\{0.7071}\end{array}} \right)\end{array}\)

Thus, these are the required eigenvectors.

02

The General Solution to the system.

Now, the general solution can be given as:

\({\bf{x}}\left( t \right) = {c_1}{{\bf{v}}_1} + {c_2}{\left( {0.89} \right)^k}{{\bf{v}}_2} + {c_3}{\left( {0.81} \right)^k}{{\bf{v}}_3}\)

Hence, this is the required solution.

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Most popular questions from this chapter

Question: Is \(\lambda = - 2\) an eigenvalue of \(\left( {\begin{array}{*{20}{c}}7&3\\3&{ - 1}\end{array}} \right)\)? Why or why not?

Let\(B = \left\{ {{{\bf{b}}_{\bf{1}}},{{\bf{b}}_{\bf{2}}},{{\bf{b}}_{\bf{3}}}} \right\}\) and \(D = \left\{ {{{\bf{d}}_{\bf{1}}},{{\bf{d}}_{\bf{2}}}} \right\}\) be bases for vector space \(V\) and \(W\), respectively. Let \(T:V \to W\) be a linear transformation with the property that

\(T\left( {{{\bf{b}}_1}} \right) = 3{{\bf{d}}_1} - 5{{\bf{d}}_2}\), \(T\left( {{{\bf{b}}_2}} \right) = - {{\bf{d}}_1} + 6{{\bf{d}}_2}\), \(T\left( {{{\bf{b}}_3}} \right) = 4{{\bf{d}}_2}\)

Find the matrix for \(T\) relative to \(B\), and\(D\).

If \(p\left( t \right) = {c_0} + {c_1}t + {c_2}{t^2} + ...... + {c_n}{t^n}\), define \(p\left( A \right)\) to be the matrix formed by replacing each power of \(t\) in \(p\left( t \right)\)by the corresponding power of \(A\) (with \({A^0} = I\) ). That is,

\(p\left( t \right) = {c_0} + {c_1}I + {c_2}{I^2} + ...... + {c_n}{I^n}\)

Show that if \(\lambda \) is an eigenvalue of A, then one eigenvalue of \(p\left( A \right)\) is\(p\left( \lambda \right)\).

In Exercises \({\bf{3}}\) and \({\bf{4}}\), use the factorization \(A = PD{P^{ - {\bf{1}}}}\) to compute \({A^k}\) where \(k\) represents an arbitrary positive integer.

4. \(\left( {\begin{array}{*{20}{c}}{ - 2}&{12}\\{ - 1}&5\end{array}} \right) = \left( {\begin{array}{*{20}{c}}3&4\\1&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}2&0\\0&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}{ - 1}&4\\1&{ - 3}\end{array}} \right)\)

Let \(B = \left\{ {{{\bf{b}}_1},{{\bf{b}}_2},{{\bf{b}}_3}} \right\}\)be a basis for a vector space \(V\) and\(T:V \to {\mathbb{R}^2}\) be a linear transformation with the property that

\(T\left( {{x_1}{{\bf{b}}_1} + {x_2}{{\bf{b}}_2} + {x_3}{{\bf{b}}_3}} \right) = \left( {\begin{aligned}{2{x_1} - 4{x_2} + 5{x_3}}\\{ - {x_2} + 3{x_3}}\end{aligned}} \right)\)

Find the matrix for \(T\) relative to \(B\) and the standard basis for \({\mathbb{R}^2}\).

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