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In Exercises 9-18, construct the general solution of \(x' = Ax\) involving complex eigenfunctions and then obtain the general real solution. Describe the shapes of typical trajectories.

18. (M) \(A = \left( {\begin{aligned}{ {20}{c}}{53}&{ - 30}&{ - 2}\\{90}&{ - 52}&{ - 3}\\{20}&{ - 10}&2\end{aligned}} \right)\)

Short Answer

Expert verified

The general complex solution of \(x' = Ax\) is \(x\left( t \right) = {c_1}\left( {\begin{aligned}{ {20}{c}}1\\2\\0\end{aligned}} \right){e^{ - 7t}} + {c_2}\left( {\begin{aligned}{ {20}{c}}{6 + 2i}\\{9 + 3i}\\{10}\end{aligned}} \right){e^{\left( {5 + i} \right)t}} + {c_3}\left( {\begin{aligned}{ {20}{c}}{6 - 2i}\\{9 - 3i}\\{10}\end{aligned}} \right){e^{\left( {5 - i} \right)t}}\). The real general solution is of the form

\(x\left( t \right) = {c_1}\left( {\begin{aligned}{ {20}{c}}1\\2\\0\end{aligned}} \right){e^{ - 7t}} + {c_2}\left( {\begin{aligned}{ {20}{c}}{6\cos t - 2\sin t}\\{9\cos t - 3\sin t}\\{10\cos t}\end{aligned}} \right){e^{5t}} + {c_3}\left( {\begin{aligned}{ {20}{c}}{6\sin t + 2\cos t}\\{9\sin t + 3\cos t}\\{10\cos t}\end{aligned}} \right){e^{5t}}\). If \({c_2} = {c_3} = 0\), then trajectories tend toward the origin, whereas in the other case, the trajectories spiral away from the origin.

Step by step solution

01

Determine the eigenvalues and eigenvector of the matrix

The given matrix is \(A = \left( {\begin{aligned}{ {20}{c}}{53}&{ - 30}&{ - 2}\\{90}&{ - 52}&{ - 3}\\{20}&{ - 10}&2\end{aligned}} \right)\).

Use the MATLAB code to compute the eigenvalues of the matrix \(A\) as shown below:

\(\begin{aligned}{l} > > {\mathop{\rm A}\nolimits} = \left( {53\,\,\, - 30\,\,\, - 2;\,90\,\,\, - 52\,\,\, - 3;\,20\,\,\, - 10\,\,\,2} \right)\\ > > {\mathop{\rm ev}\nolimits} = {\mathop{\rm eig}\nolimits} \left( {\mathop{\rm A}\nolimits} \right)\end{aligned}\)

\({\mathop{\rm ev}\nolimits} = \left( {\begin{aligned}{ {20}{c}}{ - 7.0000}&{}&{}\\{5.0000}& + &{1.0000{\mathop{\rm i}\nolimits} }\\{5.0000}& - &{1.0000{\mathop{\rm i}\nolimits} }\end{aligned}} \right)\)

Use the MATLAB code to compute the eigenvector of the matrix A as shown below:

\( > > {\mathop{\rm nulbasis}\nolimits} \left( {{\mathop{\rm A}\nolimits} - {\mathop{\rm ev}\nolimits} \left( 1 \right) {\mathop{\rm eye}\nolimits} \left( 3 \right)} \right)\)

\(\left( {\begin{aligned}{ {20}{c}}{0.5000}\\{1.0000}\\{0.0000}\end{aligned}} \right)\). Therefore, \({{\mathop{\rm v}\nolimits} _1} = \left( {\begin{aligned}{ {20}{c}}1\\2\\0\end{aligned}} \right)\).

\( > > {\mathop{\rm nulbasis}\nolimits} \left( {{\mathop{\rm A}\nolimits} - {\mathop{\rm ev}\nolimits} \left( 2 \right) {\mathop{\rm eye}\nolimits} \left( 3 \right)} \right)\)

\(\left( {\begin{aligned}{ {20}{c}}{0.6000}& + &{0.2000{\mathop{\rm i}\nolimits} }\\{0.9000}& + &{0.3000{\mathop{\rm i}\nolimits} }\\{1.0000}&{}&{}\end{aligned}} \right)\). Therefore, \({{\mathop{\rm v}\nolimits} _2} = \left( {\begin{aligned}{ {20}{c}}{6 + 2{\mathop{\rm i}\nolimits} }\\{9 + 3{\mathop{\rm i}\nolimits} }\\{10}\end{aligned}} \right)\).

\( > > {\mathop{\rm nulbasis}\nolimits} \left( {{\mathop{\rm A}\nolimits} - {\mathop{\rm ev}\nolimits} \left( 3 \right) {\mathop{\rm eye}\nolimits} \left( 3 \right)} \right)\)

\(\left( {\begin{aligned}{ {20}{c}}{0.6000}& - &{0.2000{\mathop{\rm i}\nolimits} }\\{0.9000}& - &{0.3000{\mathop{\rm i}\nolimits} }\\{1.0000}&{}&{}\end{aligned}} \right)\). Therefore, \({{\mathop{\rm v}\nolimits} _3} = \left( {\begin{aligned}{ {20}{c}}{6 - 2{\mathop{\rm i}\nolimits} }\\{9 - 3{\mathop{\rm i}\nolimits} }\\{10}\end{aligned}} \right)\).

02

Construct the general solution of \(x' = Ax\)

Therefore, the general complex solution of \(x' = Ax\) is \(x\left( t \right) = {c_1}\left( {\begin{aligned}{ {20}{c}}1\\2\\0\end{aligned}} \right){e^{ - 7t}} + {c_2}\left( {\begin{aligned}{ {20}{c}}{6 + 2i}\\{9 + 3i}\\{10}\end{aligned}} \right){e^{\left( {5 + i} \right)t}} + {c_3}\left( {\begin{aligned}{ {20}{c}}{6 - 2i}\\{9 - 3i}\\{10}\end{aligned}} \right){e^{\left( {5 - i} \right)t}}\), with \({c_1}\) , and \({c_2}\) are arbitrary complex numbers.

03

Determine the real general solution and describe the shape of the trajectories

Rewrite the second eigenfunction of the matrix as shown below:

\(\left( {\begin{aligned}{ {20}{c}}{6 + 2i}\\{9 + 3i}\\{10}\end{aligned}} \right){e^{5t}}\left( {\cos t + i\sin t} \right) = \left( {\begin{aligned}{ {20}{c}}{6\cos t - 2\sin t}\\{9\cos t - 3\sin t}\\{10\cos t}\end{aligned}} \right){e^{5t}} + i\left( {\begin{aligned}{ {20}{c}}{6\sin t + 2\cos t}\\{9\sin t - 3\cos t}\\{10\sin t}\end{aligned}} \right){e^{5t}}\)

Therefore, the real general solution is of the form as shown below:

\(x\left( t \right) = {c_1}\left( {\begin{aligned}{ {20}{c}}1\\2\\0\end{aligned}} \right){e^{ - 7t}} + {c_2}\left( {\begin{aligned}{ {20}{c}}{6\cos t - 2\sin t}\\{9\cos t - 3\sin t}\\{10\cos t}\end{aligned}} \right){e^{5t}} + {c_3}\left( {\begin{aligned}{ {20}{c}}{6\sin t + 2\cos t}\\{9\sin t + 3\cos t}\\{10\cos t}\end{aligned}} \right){e^{5t}}\),

Where \({c_1},{c_2},\) and \({c_2}\) are real numbers.

It is observed that the if \({c_2} = {c_3} = 0\), then trajectories tend toward the origin, while in some cases, the trajectories spiral away from the origin.

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Most popular questions from this chapter

Question: Exercises 9-14 require techniques section 3.1. Find the characteristic polynomial of each matrix, using either a cofactor expansion or the special formula for \(3 \times 3\) determinants described prior to Exercise 15-18 in Section 3.1. [Note: Finding the characteristic polynomial of a \(3 \times 3\) matrix is not easy to do with just row operations, because the variable \(\lambda \) is involved.

13. \(\left[ {\begin{array}{*{20}{c}}6&- 2&0\\- 2&9&0\\5&8&3\end{array}} \right]\)

Use Exercise 12 to find the eigenvalues of the matrices in Exercises 13 and 14.

14. \(A{\bf{ = }}\left( {\begin{array}{*{20}{c}}{\bf{1}}&{\bf{5}}&{{\bf{ - 6}}}&{{\bf{ - 7}}}\\{\bf{2}}&{\bf{4}}&{\bf{5}}&{\bf{2}}\\{\bf{0}}&{\bf{0}}&{{\bf{ - 7}}}&{{\bf{ - 4}}}\\{\bf{0}}&{\bf{0}}&{\bf{3}}&{\bf{1}}\end{array}} \right)\)

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 \(A\).

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 \(I\).)

[M]Repeat Exercise 25 for \[A{\bf{ = }}\left[ {\begin{array}{*{20}{c}}{{\bf{ - 8}}}&{\bf{5}}&{{\bf{ - 2}}}&{\bf{0}}\\{{\bf{ - 5}}}&{\bf{2}}&{\bf{1}}&{{\bf{ - 2}}}\\{{\bf{10}}}&{{\bf{ - 8}}}&{\bf{6}}&{{\bf{ - 3}}}\\{\bf{3}}&{{\bf{ - 2}}}&{\bf{1}}&{\bf{0}}\end{array}} \right]\].

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