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Question: Exercises 9–14 require techniques from 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 Exercises 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.)

10. \(\left( {\begin{array}{*{20}{c}}0&3&1\\3&0&2\\1&2&0\end{array}} \right)\)

Short Answer

Expert verified

Characteristic polynomial is \( - {\lambda ^3} + 14\lambda + 12\).

Step by step solution

01

Formulate the matrix \(A - \lambda I\) 

If \(A\) is an \(n \times n\) matrix, then \(det\left( {A - \lambda I} \right)\), which is a polynomial of degree \(n\), is called the characteristic polynomial of \(A\).

It is given that\(A = \left( {\begin{array}{*{20}{c}}0&3&1\\3&0&2\\1&2&0\end{array}} \right)\)and\(I = \left( {\begin{array}{*{20}{c}}1&0&0\\0&1&0\\0&0&1\end{array}} \right)\)is identity matrix. Find the matrix\(\left( {A - \lambda I} \right)\)as shown below:

\(\begin{array}{c}A - \lambda I = \left( {\begin{array}{*{20}{c}}0&3&1\\3&0&2\\1&2&0\end{array}} \right) - \lambda \left( {\begin{array}{*{20}{c}}1&0&0\\0&1&0\\0&0&1\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{ - \lambda }&3&1\\3&{ - \lambda }&2\\1&2&{ - \lambda }\end{array}} \right)\end{array}\)

02

Find the determinant of the matrix \(A - \lambda I\)

For \(n \ge 2\) the determinant of an \(n \times n\) matrix \(A = [{a_{ij}}]\) is the sum of \(n\)terms of the form \( \pm {a_{1j}}\det {A_{1j}},\) with plus and minus signs alternating, where the entries \({a_{11}},{a_{12}}, \ldots ,{a_{1n}}\) are from the first row of \(A\). In symbols,

\(\begin{gathered} {\text{det}}A = {a_{11}}\det {A_{11}} - {a_{12}}\det {A_{12}} + \ldots + {\left( { - 1} \right)^{1 + n}}{a_{1n}}\det {A_{1n}} \\ = \mathop \sum \limits_{j = 1}^n {\left( { - 1} \right)^{1 + j}}{a_{1j}}\det {A_{1j}} \\ \end{gathered} \)

With the help of above defined formula, the \(\det A\) is calculated as follows:

\(\begin{array}{c}det\left( {A - \lambda I} \right) = det\left( {\begin{array}{*{20}{c}}{ - \lambda }&3&1\\3&{ - \lambda }&2\\1&2&{ - \lambda }\end{array}} \right)\\ = \left( { - \lambda } \right)\left| {\begin{array}{*{20}{c}}{ - \lambda }&2\\2&{ - \lambda }\end{array}} \right| - 3\left| {\begin{array}{*{20}{c}}3&2\\1&{ - \lambda }\end{array}} \right| + 1\left| {\begin{array}{*{20}{c}}3&{ - \lambda }\\1&2\end{array}} \right|\\ = \left( { - \lambda } \right)\left( {\left( { - \lambda } \right)\left( { - \lambda } \right) - \left( 2 \right)\left( 2 \right)} \right) - 3\left( {\left( 3 \right)\left( { - \lambda } \right) - \left( 2 \right)\left( 1 \right)} \right) + 1\left( {\left( 2 \right)\left( 3 \right) - \left( { - \lambda } \right)\left( 1 \right)} \right)\\ = 4\lambda - {\lambda ^3} + 6 + 9\lambda + 6 + \lambda \\ = - {\lambda ^3} + 14\lambda + 12\end{array}\)

Thus, the characteristic polynomial is \( - {\lambda ^3} + 14\lambda + 12\).

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Most popular questions from this chapter

Question: Is \(\left( {\begin{array}{*{20}{c}}1\\4\end{array}} \right)\) an eigenvalue of \(\left( {\begin{array}{*{20}{c}}{ - 3}&1\\{ - 3}&8\end{array}} \right)\)? If so, find the eigenvalue.

Define\(T:{{\rm P}_3} \to {\mathbb{R}^4}\)by\(T\left( {\bf{p}} \right) = \left( {\begin{aligned}{{\bf{p}}\left( { - 3} \right)}\\{{\bf{p}}\left( { - 1} \right)}\\{{\bf{p}}\left( 1 \right)}\\{{\bf{p}}\left( 3 \right)}\end{aligned}} \right)\).

  1. Show that \(T\) is a linear transformation.
  2. Find the matrix for \(T\) relative to the basis \(\left\{ {1,t,{t^2},{t^3}} \right\}\)for \({{\rm P}_3}\)and the standard basis for \({\mathbb{R}^4}\).

Question: Let \(A = \left( {\begin{array}{*{20}{c}}{.6}&{.3}\\{.4}&{.7}\end{array}} \right)\), \({v_1} = \left( {\begin{array}{*{20}{c}}{3/7}\\{4/7}\end{array}} \right)\), \({x_0} = \left( {\begin{array}{*{20}{c}}{.5}\\{.5}\end{array}} \right)\). (Note: \(A\) is the stochastic matrix studied in Example 5 of Section 4.9.)

  1. Find a basic for \({\mathbb{R}^2}\) consisting of \({{\rm{v}}_1}\) and anther eigenvector \({{\rm{v}}_2}\) of \(A\).
  2. Verify that \({{\rm{x}}_0}\) may be written in the form \({{\rm{x}}_0} = {{\rm{v}}_1} + c{{\rm{v}}_2}\).
  3. For \(k = 1,2, \ldots \), define \({x_k} = {A^k}{x_0}\). Compute \({x_1}\) and \({x_2}\), and write a formula for \({x_k}\). Then show that \({{\bf{x}}_k} \to {{\bf{v}}_1}\) as \(k\) increases.

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 \({\mathbb{R}^2}\).

For the Matrices A find real closed formulas for the trajectory x(t+1)=Ax(t)where x(0)=[01]

A=[43-34]

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