Let \(A = \left( {\begin{aligned}{*{20}{c}}{.4}&{ - .3}\\{.4}&{1.2}\end{aligned}} \right)\). Explain why \({A^k}\) approaches \(\left( {\begin{aligned}{*{20}{c}}{ - .5}&{ - .75}\\1&{1.5}\end{aligned}} \right)\) as \(k \to \infty \).

Short Answer

Expert verified

This implies that \(\mathop {\lim }\limits_{k \to \infty } {A^k} \to \left( {\begin{aligned}{*{20}{c}}{ - .5}&{ - .75}\\1&{1.5}\end{aligned}} \right)\).

Step by step solution

01

Step 1: Find the characteristic polynomial

Consider the matrices\(A = \left( {\begin{aligned}{*{20}{c}}{.4}&{ - .3}\\{.4}&{1.2}\end{aligned}} \right)\).

\(\begin{aligned}{c}\det \left( {A - \lambda I} \right) &= \det \left( {\begin{aligned}{*{20}{c}}{.4 - \lambda }&{ - .3}\\{.4}&{1.2 - \lambda }\end{aligned}} \right)\\ &= \left( {.4 - \lambda } \right)\left( {1.2 - \lambda } \right) + 1.2\\ &= {\lambda ^2} - 1.6\lambda + .6\\ &= \left( {\lambda - 1} \right)\left( {\lambda - .6} \right)\end{aligned}\)

Thus, the eigenvalues are \(1\) and \(0.6\).

02

Find the Eigenvector for \(\lambda  = {\bf{1}}\)

\(\begin{aligned}{c}\left( {A - \lambda I} \right) &= \left( {\begin{aligned}{*{20}{c}}{ - .6}&{ - .3}\\{.4}&{.2}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{*{20}{c}}2&1\\2&1\end{aligned}} \right)\;\left\{ {{R_1} \to \frac{1}{{ - 3}}{R_1},\;{R_2} \to \frac{1}{2}{R_2}} \right\}\\ &= \left( {\begin{aligned}{*{20}{c}}2&1\\0&0\end{aligned}} \right)\;\left\{ {{R_1} \to {R_2} - {R_1}} \right\}\end{aligned}\)

The general solution is as shown below:

\(\left( {\begin{aligned}{*{20}{c}}{{x_1}}\\{{x_2}}\end{aligned}} \right) = {x_1}\left( {\begin{aligned}{*{20}{c}}1\\{ - 2}\end{aligned}} \right)\)

Therefore, the eigenvector for \(\lambda = 1\) is \({v_1} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\2\end{aligned}} \right)\).

03

Find the Eigenvector for \(\lambda  = {\bf{0}}{\bf{.6}}\)

\(\begin{aligned}{c}\left( {A - .6I} \right) &= \left( {\begin{aligned}{*{20}{c}}{ - .2}&{ - .3}\\{.4}&{.6}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{*{20}{c}}{ - .2}&{ - .3}\\{.2}&{.3}\end{aligned}} \right)\;\left\{ {{R_2} \to \frac{1}{2}{R_2}} \right\}\\ &= \left( {\begin{aligned}{*{20}{c}}2&3\\0&0\end{aligned}} \right)\;\left\{ {{R_2} \to {R_2} + {R_1},{R_1} \to \frac{1}{{ - .1}}{R_1}} \right\}\end{aligned}\)

The general solution is as shown below:

\(\left( {\begin{aligned}{*{20}{c}}{{x_1}}\\{{x_2}}\end{aligned}} \right) = {x_1}\left( {\begin{aligned}{*{20}{c}}1\\{ - \frac{2}{3}}\end{aligned}} \right)\)

Therefore, the eigenvector for \(\lambda = - 3\) is \({v_2} = \left( {\begin{aligned}{*{20}{c}}{ - 3}\\2\end{aligned}} \right)\).

04

Find the matrix \(P\)

The matrix\(P\)is formed by eigenvectors,

\(P = \left( {\begin{aligned}{*{20}{c}}{ - 1}&{ - 3}\\2&2\end{aligned}} \right)\)

Now find the inverse of the matrix\(P\).

\(\begin{aligned}{c}{P^{ - 1}} = \frac{1}{{\left( { - 1} \right)\left( 2 \right) - \left( { - 3} \right)\left( 2 \right)}}\left( {\begin{aligned}{*{20}{c}}2&3\\{ - 2}&{ - 1}\end{aligned}} \right)\\ = \frac{1}{4}\left( {\begin{aligned}{*{20}{c}}2&3\\{ - 2}&{ - 1}\end{aligned}} \right)\end{aligned}\)

Therefore, matrix\(A\)is:

\(A = \left( {\begin{aligned}{*{20}{c}}{ - 1}&{ - 3}\\2&2\end{aligned}} \right)\left( {\begin{aligned}{*{20}{c}}1&0\\0&{.6}\end{aligned}} \right)\frac{1}{4}\left( {\begin{aligned}{*{20}{c}}2&3\\{ - 2}&{ - 1}\end{aligned}} \right)\)

05

Find the matrix \({A^k}\)

\(\begin{aligned}{c}{A^k} &= \frac{1}{4}\left( {\begin{aligned}{*{20}{c}}{ - 1}&{ - 3}\\2&2\end{aligned}} \right)\left( {\begin{aligned}{*{20}{c}}{{1^k}}&0\\0&{{{.6}^k}}\end{aligned}} \right)\left( {\begin{aligned}{*{20}{c}}2&3\\{ - 2}&{ - 1}\end{aligned}} \right)\\ &= \frac{1}{4}\left( {\begin{aligned}{*{20}{c}}{ - 1}&{ - 3}\\2&2\end{aligned}} \right)\left( {\left( {\begin{aligned}{*{20}{c}}{{1^k}}&0\\0&{{{.6}^k}}\end{aligned}} \right)\left( {\begin{aligned}{*{20}{c}}2&3\\{ - 2}&{ - 1}\end{aligned}} \right)} \right)\\ &= \frac{1}{4}\left( {\begin{aligned}{*{20}{c}}{ - 1}&{ - 3}\\2&2\end{aligned}} \right)\left( {\begin{aligned}{*{20}{c}}2&3\\{ - 2{{\left( {.6} \right)}^k}}&{ - {{\left( {.6} \right)}^k}}\end{aligned}} \right)\\ &= \mathop {\lim }\limits_{k \to \infty } \frac{1}{4}\left( {\begin{aligned}{*{20}{c}}{ - 2 + 6{{\left( {.6} \right)}^k}}&{ - 3 + 3{{\left( {.6} \right)}^k}}\\{4 - 4{{\left( {.6} \right)}^k}}&{6 - 2{{\left( {.6} \right)}^k}}\end{aligned}} \right)\\ &= \frac{1}{4}\left( {\begin{aligned}{*{20}{c}}{ - 2}&{ - 3}\\4&6\end{aligned}} \right)\\ &= \left( {\begin{aligned}{*{20}{c}}{ - .5}&{ - .75}\\1&{1.5}\end{aligned}} \right)\end{aligned}\)

Thus, \(\mathop {\lim }\limits_{k \to \infty } {A^k} \to \left( {\begin{aligned}{*{20}{c}}{ - .5}&{ - .75}\\1&{1.5}\end{aligned}} \right)\).

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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 \(\left( {{\bf{ - 2,}}\;{\bf{1,}}\;{\bf{2}}} \right)\).

16. \(\left( {\begin{array}{*{20}{c}}{\bf{0}}&{{\bf{ - 4}}}&{{\bf{ - 6}}}\\{{\bf{ - 1}}}&{\bf{0}}&{{\bf{ - 3}}}\\{\bf{1}}&{\bf{2}}&{\bf{5}}\end{array}} \right)\)

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: 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.

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

Suppose \(A\) is diagonalizable and \(p\left( t \right)\) is the characteristic polynomial of \(A\). Define \(p\left( A \right)\) as in Exercise 5, and show that \(p\left( A \right)\) is the zero matrix. This fact, which is also true for any square matrix, is called the Cayley-Hamilton theorem.

Exercises 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 \(p\): \({C_p} = \left( {\begin{aligned}{*{20}{c}}{\bf{0}}&{\bf{1}}&{\bf{0}}&{...}&{\bf{0}}\\{\bf{0}}&{\bf{0}}&{\bf{1}}&{}&{\bf{0}}\\:&{}&{}&{}&:\\{\bf{0}}&{\bf{0}}&{\bf{0}}&{}&{\bf{1}}\\{ - {a_{\bf{0}}}}&{ - {a_{\bf{1}}}}&{ - {a_{\bf{2}}}}&{...}&{ - {a_{n - {\bf{1}}}}}\end{aligned}} \right)\).

19. Write the companion matrix \({C_p}\) for \(p\left( t \right) = {\bf{6}} - {\bf{5}}t + {t^{\bf{2}}}\), and then find the characteristic polynomial of \({C_p}\).

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