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15.Let \(A = \left( {\begin{aligned}{}{.4}&{}&0&{}&{.2}\\{.3}&{}&{.8}&{}&{.3}\\{.3}&{}&{.2}&{}&{.5}\end{aligned}} \right)\). The vector \({v_1} = \left( {\begin{aligned}{}{.1}\\{.6}\\{.3}\end{aligned}} \right)\) is an eigenvector for \(A\), and two eigenvalues are .5 and .2. Construct the solution of the dynamical system \({{\bf{x}}_{k + 1}} = A{{\bf{x}}_k}\) that satisfies \({x_0} = \left( {0,\,\,.3,\,\,.7} \right)\). What happens to \({x_k}\) as \(k \to \infty \)?

Short Answer

Expert verified

The solution to the dynamical system is:

\({x_k} = {v_1} + \left( {0.1} \right){\left( {0.5} \right)^k}{v_2} + \left( {0.3} \right){\left( {0.2} \right)^k}{v_3}\)

And, \({x_k}\) approach to \({v_1}\) as \(k \to \infty \).

Step by step solution

01

Discrete Dynamical System 

For anyDynamical Systemwith matrix \(A\), the eigenvalues of \(A\) will determine the system of:

\(\begin{aligned}{}{\rm{Greatest attraction }} \to {\rm{if }}\lambda < 1\\{\rm{Greatest repulsion }} \to {\rm{if }}\lambda > 1\end{aligned}\)

02

Evaluating Eigenvalue of given eigenvector

The given matrix and the eigenvector for the dynamic system \({x_{k + 1}} = A{x_k}\) are respectively:

\(\begin{aligned}{}A = \left( {\begin{aligned}{}{.4}&{}&0&{}&{.2}\\{.3}&{}&{.8}&{}&{.3}\\{.3}&{}&{.2}&{}&{.5}\end{aligned}} \right)\\{v_1} = \left( {\begin{aligned}{}{.1}\\{.6}\\{.3}\end{aligned}} \right)\end{aligned}\)

The two eigenvalues are given, that is, 0.5 and 0.2. Now, we have:

\(\begin{aligned}{c}A{v_1} &= \left( {\begin{aligned}{}{.4}&{}&0&{}&{.2}\\{.3}&{}&{.8}&{}&{.3}\\{.3}&{}&{.2}&{}&{.5}\end{aligned}} \right)\left( {\begin{aligned}{}{.1}\\{.6}\\{.3}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}{.1}\\{.6}\\{.3}\end{aligned}} \right)\\& = 1.{v_1}\end{aligned}\)

So, the eigenvalue found is \(\lambda = 1\).

03

Eigenvector for eigenvalue 0.5 

Now, for eigenvectors, we have:

At \(\lambda = 0.5\);

\(\begin{aligned}{}\left( {A - \left( {0.5} \right)I} \right){v_2} &= 0\\\left( {\begin{aligned}{}{0.4 - 0.5}&{}&0&{}&{0.2}\\{0.3}&{}&{0.8 - 0.5}&{}&{0.3}\\{0.3}&{}&{0.2}&{}&{0.5 - 0.5}\end{aligned}} \right)\left( {\begin{aligned}{}x\\\begin{aligned}{}y\\z\end{aligned}\end{aligned}} \right) &= 0\\\left( {\begin{aligned}{}{ - 0.1}&{}&0&{}&{0.2}\\{0.3}&{}&{0.3}&{}&{0.3}\\{0.3}&{}&{}&{0.2}&{}&0\end{aligned}} \right)\left( {\begin{aligned}{}x\\\begin{aligned}{l}y\\z\end{aligned}\end{aligned}} \right) &= 0\end{aligned}\)

Also, the system of equationwill be:

\(\begin{aligned}{c} - 0.1x + 0.2z = 0\\0.3x + 0.3y + 0.3z = 0\\0.3x + 0.2y = 0\end{aligned}\)

For this, the general solution, we have:

\(\begin{aligned}{}{v_2} &= \left( {\begin{aligned}{}x\\\begin{aligned}{l}y\\z\end{aligned}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}2\\\begin{aligned}{l} - 3\\1\end{aligned}\end{aligned}} \right)\end{aligned}\)

Here, the eigenvector is: \({v_2} = \left( {\begin{aligned}{}2\\\begin{aligned}{l} - 3\\1\end{aligned}\end{aligned}} \right)\).

04

Eigenvector for eigenvalue 0.2

Now, for eigenvectors, we have:

At \(\lambda = 0.2\).

\(\begin{aligned}{}\left( {A - \left( {0.2} \right)I} \right){v_3} &= 0\\\left( {\begin{aligned}{}{0.4 - 0.2}&{}&0&{}&{0.2}\\{0.3}&{}&{0.8 - 0.2}&{}&{0.3}\\{0.3}&{}&{0.2}&{}&{0.5 - 0.2}\end{aligned}} \right)\left( {\begin{aligned}{}x\\\begin{aligned}{l}y\\z\end{aligned}\end{aligned}} \right) &= 0\\\left( {\begin{aligned}{}{0.2}&{}&0&{}&{0.2}\\{0.3}&{}&{0.6}&{}&{0.3}\\{0.3}&{}&{0.2}&{}&{0.3}\end{aligned}} \right)\left( {\begin{aligned}{}x\\\begin{aligned}{l}y\\z\end{aligned}\end{aligned}} \right) &= 0\end{aligned}\)

Also, the system of equationwill be:

\(\begin{aligned}{}0.2x + 0.2z = 0\\0.3x + 0.6y + 0.3z = 0\\0.3x + 0.2y + 0.3z = 0\end{aligned}\)

For this, the general solution, we have:

\(\begin{aligned}{}{v_3} &= \left( {\begin{aligned}{}x\\\begin{aligned}{l}y\\z\end{aligned}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}{ - 1}\\\begin{aligned}{l}0\\1\end{aligned}\end{aligned}} \right)\end{aligned}\)

Here, the eigenvector is \({v_3} = \left( {\begin{aligned}{}{ - 1}\\\begin{aligned}{}0\\1\end{aligned}\end{aligned}} \right)\).

05

Constructing the Solution of the system

Now, according to the question, we have:

\({x_0} = \left( {0,\,\,.3,\,\,.7} \right)\)

We know:

\({x_0} = {c_1}{v_1} + {c_2}{v_2} + {c_3}{v_3}\)

So,

\(\begin{aligned}{}\left( {\begin{aligned}{}{{v_1}}&{{v_2}}&{{v_3}}&{{x_0}}\end{aligned}} \right) &= \left( {\begin{aligned}{}{0.1}&{}&2&{}&{ - 1}&{}&0\\{0.6}&{}&{ - 3}&{}&0&{}&{0.3}\\{0.3}&{}&1&{}&1&{}&{0.7}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{}1&{}&0&{}&0&{}&1\\0&{}&1&{}&0&{}&{0.1}\\0&{}&0&{}&1&{}&{0.3}\end{aligned}} \right)\end{aligned}\)

Thus, the general solution can be:

\(\begin{aligned}{}{x_0} = {v_1} + 0.1{v_2} + 0.3{v_3}\\{x_1} &= A{v_1} + 0.1A{v_2} + 0.3A{v_3}\\ = {v_1} + 0.1\left( {0.5} \right){v_2} + 0.3\left( {0.2} \right){v_3}\\{x_k} &= {v_1} + \left( {0.1} \right){\left( {0.5} \right)^k}{v_2} + \left( {0.3} \right){\left( {0.2} \right)^k}{v_3}\end{aligned}\)

Here, we see, \({x_k}\) is approaching to \({v_1}\) as the value of \(k\) is increasing.

Hence, the solution to the dynamical system is: \({x_k} = {v_1} + \left( {0.1} \right){\left( {0.5} \right)^k}{v_2} + \left( {0.3} \right){\left( {0.2} \right)^k}{v_3}\) and \({x_k}\) approach to \({v_1}\) as \(k \to \infty \).

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

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

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

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

12. \(\left( {\begin{array}{*{20}{c}}{\bf{4}}&{\bf{2}}&{\bf{2}}\\{\bf{2}}&{\bf{4}}&{\bf{2}}\\{\bf{2}}&{\bf{2}}&{\bf{4}}\end{array}} \right)\)

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).

Mark each statement as True or False. Justify each answer.

a. If \(A\) is invertible and 1 is an eigenvalue for \(A\), then \(1\) is also an eigenvalue of \({A^{ - 1}}\)

b. If \(A\) is row equivalent to the identity matrix \(I\), then \(A\) is diagonalizable.

c. If \(A\) contains a row or column of zeros, then 0 is an eigenvalue of \(A\)

d. Each eigenvalue of \(A\) is also an eigenvalue of \({A^2}\).

e. Each eigenvector of \(A\) is also an eigenvector of \({A^2}\)

f. Each eigenvector of an invertible matrix \(A\) is also an eigenvector of \({A^{ - 1}}\)

g. Eigenvalues must be nonzero scalars.

h. Eigenvectors must be nonzero vectors.

i. Two eigenvectors corresponding to the same eigenvalue are always linearly dependent.

j. Similar matrices always have exactly the same eigenvalues.

k. Similar matrices always have exactly the same eigenvectors.

I. The sum of two eigenvectors of a matrix \(A\) is also an eigenvector of \(A\).

m. The eigenvalues of an upper triangular matrix \(A\) are exactly the nonzero entries on the diagonal of \(A\).

n. The matrices \(A\) and \({A^T}\) have the same eigenvalues, counting multiplicities.

o. If a \(5 \times 5\) matrix \(A\) has fewer than 5 distinct eigenvalues, then \(A\) is not diagonalizable.

p. There exists a \(2 \times 2\) matrix that has no eigenvectors in \({A^2}\)

q. If \(A\) is diagonalizable, then the columns of \(A\) are linearly independent.

r. A nonzero vector cannot correspond to two different eigenvalues of \(A\).

s. A (square) matrix \(A\) is invertible if and only if there is a coordinate system in which the transformation \({\bf{x}} \mapsto A{\bf{x}}\) is represented by a diagonal matrix.

t. If each vector \({{\bf{e}}_j}\) in the standard basis for \({A^n}\) is an eigenvector of \(A\), then \(A\) is a diagonal matrix.

u. If \(A\) is similar to a diagonalizable matrix \(B\), then \(A\) is also diagonalizable.

v. If \(A\) and \(B\) are invertible \(n \times n\) matrices, then \(AB\)is similar to \ (BA\ )

w. An \(n \times n\) matrix with \(n\) linearly independent eigenvectors is invertible.

x. If \(A\) is an \(n \times n\) diagonalizable matrix, then each vector in \({A^n}\) can be written as a linear combination of eigenvectors of \(A\).

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