In Exercises 7 and 8, make a change of variable that decouples the equation\(x' = Ax\). Write the equation\({\bf{x}}\left( t \right) = P{\bf{y}}\left( t \right)\)and show the calculation that leads to the uncoupled system\({\bf{y}}' = D{\bf{y}}\), specifying\(P\)and\(D\).

7. \(A\)as in exercise 5

Short Answer

Expert verified

The required equation is:

\(\left( {\begin{array}{ {20}{l}}{{y_1}^\prime (t)}\\{{y_1}^\prime (t)}\end{array}} \right) = \left( {\begin{array}{ {20}{l}}4&0\\0&6\end{array}} \right)\left( {\begin{array}{ {20}{l}}{{y_1}(t)}\\{{y_1}(t)}\end{array}} \right)\)

Step by step solution

01

System of Differential Equations

The general solutionfor any system of differential equations withthe eigenvalues\({\lambda _1}\)and\({\lambda _2}\)with the respective eigenvectors\({v_1}\)and\({v_2}\)is given by:

\(x(t) = {c_1}{v_1}{e^{{\lambda _1}t}} + {c_2}{v_2}{e^{{\lambda _2}t}}\)

Here,\({c_1}\)and\({c_2}\)are the constants from the initial condition.

02

Calculation for decoupled system

From Exercise 5,

\(\begin{array}{l}A = \left( {\begin{array}{ {20}{c}}7&{ - 1}\\3&3\end{array}} \right)\\{v_1} = \left( {\begin{array}{ {20}{l}}1\\3\end{array}} \right){\rm{ and }}{{\rm{v}}_2} = \left( {\begin{array}{ {20}{l}}1\\1\end{array}} \right)\end{array}\)

With eigenvalues 4 and 6.

In order to decouple the equation\(x' = Ax\), such that,\(A = PD{P^{ - 1}}\)and\(D = {P^{ - 1}}AP\), consider\(P = \left( {\begin{array}{ {20}{l}}{{v_1}}&{{v_2}}\end{array}} \right) = \left( {\begin{array}{ {20}{l}}1&1\\3&1\end{array}} \right)\).

Also, consider\(D = \left( {\begin{array}{ {20}{l}}4&0\\0&6\end{array}} \right)\).

Substituting\(x(t) = Py(t)\)into\(x' = Ax\), we have:

\(\begin{array}{c}\frac{d}{{dt}}(Py) = A(Py)\\ = PD{P^{ - 1}}(Py)\\ = PDy\end{array}\)

Here,\(P\)have constant entries, so:

\(\frac{d}{{dt}}(Py) = P\frac{d}{{dt}}(y)\)

From the above two solutions, we have:

\(P\frac{d}{{dt}}(y) = PDy\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{\rm{as }}{P^{ - 1}}{\rm{ yields}} \to y' = Dy} \right\}\)

Hence, the solution is \(\left( {\begin{array}{ {20}{l}}{{y_1}^\prime (t)}\\{{y_1}^\prime (t)}\end{array}} \right) = \left( {\begin{array}{ {20}{l}}4&0\\0&6\end{array}} \right)\left( {\begin{array}{ {20}{l}}{{y_1}(t)}\\{{y_1}(t)}\end{array}} \right)\).

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Most popular questions from this chapter

A common misconception is that if \(A\) has a strictly dominant eigenvalue, then, for any sufficiently large value of \(k\), the vector \({A^k}{\bf{x}}\) is approximately equal to an eigenvector of \(A\). For the three matrices below, study what happens to \({A^k}{\bf{x}}\) when \({\bf{x = }}\left( {{\bf{.5,}}{\bf{.5}}} \right)\), and try to draw general conclusions (for a \({\bf{2 \times 2}}\) matrix).

a. \(A{\bf{ = }}\left( {\begin{aligned}{ {20}{c}}{{\bf{.8}}}&{\bf{0}}\\{\bf{0}}&{{\bf{.2}}}\end{aligned}} \right)\) b. \(A{\bf{ = }}\left( {\begin{aligned}{ {20}{c}}{\bf{1}}&{\bf{0}}\\{\bf{0}}&{{\bf{.8}}}\end{aligned}} \right)\) c. \(A{\bf{ = }}\left( {\begin{aligned}{ {20}{c}}{\bf{8}}&{\bf{0}}\\{\bf{0}}&{\bf{2}}\end{aligned}} \right)\)

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

20. Let \(p\left( t \right){\bf{ = }}\left( {t{\bf{ - 2}}} \right)\left( {t{\bf{ - 3}}} \right)\left( {t{\bf{ - 4}}} \right){\bf{ = - 24 + 26}}t{\bf{ - 9}}{t^{\bf{2}}}{\bf{ + }}{t^{\bf{3}}}\). Write the companion matrix for \(p\left( t \right)\), and use techniques from chapter \({\bf{3}}\) to find the characteristic polynomial.

Apply the results of Exercise \({\bf{15}}\) to find the eigenvalues of the matrices \(\left( {\begin{aligned}{*{20}{c}}{\bf{1}}&{\bf{2}}&{\bf{2}}\\{\bf{2}}&{\bf{1}}&{\bf{2}}\\{\bf{2}}&{\bf{2}}&{\bf{1}}\end{aligned}} \right)\) and \(\left( {\begin{aligned}{*{20}{c}}{\bf{7}}&{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{3}}\\{\bf{3}}&{\bf{7}}&{\bf{3}}&{\bf{3}}&{\bf{3}}\\{\bf{3}}&{\bf{3}}&{\bf{7}}&{\bf{3}}&{\bf{3}}\\{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{7}}&{\bf{3}}\\{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{3}}&{\bf{7}}\end{aligned}} \right)\).

Let \(J\) be the \(n \times n\) matrix of all \({\bf{1}}\)’s and consider \(A = \left( {a - b} \right)I + bJ\) that is,

\(A = \left( {\begin{aligned}{*{20}{c}}a&b&b&{...}&b\\b&a&b&{...}&b\\b&b&a&{...}&b\\:&:&:&:&:\\b&b&b&{...}&a\end{aligned}} \right)\)

Use the results of Exercise \({\bf{16}}\) in the Supplementary Exercises for Chapter \({\bf{3}}\) to show that the eigenvalues of \(A\) are \(a - b\) and \(a + \left( {n - {\bf{1}}} \right)b\). What are the multiplicities of these eigenvalues?

Question 19: Let \(A\) be an \(n \times n\) matrix, and suppose A has \(n\) real eigenvalues, \({\lambda _1},...,{\lambda _n}\), repeated according to multiplicities, so that \(\det \left( {A - \lambda I} \right) = \left( {{\lambda _1} - \lambda } \right)\left( {{\lambda _2} - \lambda } \right) \ldots \left( {{\lambda _n} - \lambda } \right)\) . Explain why \(\det A\) is the product of the n eigenvalues of A. (This result is true for any square matrix when complex eigenvalues are considered.)

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