Question: Is \(\lambda = 4\) an eigenvalue of \(\left( {\begin{array}{*{20}{c}}3&0&{ - 1}\\2&3&1\\{ - 3}&4&5\end{array}} \right)\)? If so, find one corresponding eigenvector.

Short Answer

Expert verified

Yes, \(\lambda = 4\) is the eigenvalue of the given matrix \(\left( {\begin{array}{*{20}{c}}3&0&{ - 1}\\2&3&1\\{ - 3}&4&5\end{array}} \right)\) and the eigenvector is \(\left( {\begin{array}{*{20}{c}}{ - 1}\\{ - 1}\\1\end{array}} \right)\).

Step by step solution

01

Definition of eigenvalue

Let \(\lambda \) is a scaler, \(A\) is an \(n \times n\) matrix and \({\bf{x}}\) is an eigenvector corresponding to \(\lambda \), \(\lambda \) is said to be an eigenvalue of the matrix \(A\) if there exists a nontrivial solution \({\bf{x}}\) for \(A{\bf{x}} = \lambda {\bf{x}}\).

02

Determine whether \(\lambda  = 4\) is the eigenvalue of given matrix

Denote the given matrix by \(A\).

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

According to the definition of eigenvalue, \(\lambda = 4\) is the eigenvalue of the matrix \(A\), if satisfies the equation \(A{\bf{x}} = \lambda {\bf{x}}\).

Substitute \(\lambda = 4\) into \(A{\bf{x}} = \lambda {\bf{x}}\).

\(A{\bf{x}} = 4{\bf{x}}\)

The obtained equation equivalent to \(\left( {A - 4I} \right){\bf{x}} = 0\), which is a homogeneous equation.

So, first solve the matrix \(\left( {A - 4I} \right)\) by using \(A = \left( {\begin{array}{*{20}{c}}3&0&{ - 1}\\2&3&1\\{ - 3}&4&5\end{array}} \right)\).

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

It can be said that \(\lambda = 4\) will be the eigenvalue of the given matrix if \(\left( {A - 4I} \right)\) is invertible.

So, write the obtained matrix in the form of an augmented matrix, where for \(A{\bf{x}} = 0\), the augmented matrix given by \(\left( {\begin{array}{*{20}{c}}A&0\end{array}} \right)\).

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

03

Find the row reduce echelon form 

Now, reduce the obtained matrix into row echelon form by using the row operations.

\(\begin{gathered} \hfill \left( {\begin{array}{*{20}{c}} { - 1}&0&{ - 1}&0 \\ 2&{ - 1}&1&0 \\ { - 3}&4&1&0 \end{array}} \right)\underrightarrow {{R_1} \to - {R_1}}\left( {\begin{array}{*{20}{c}} 1&0&1&0 \\ 2&{ - 1}&1&0 \\ { - 3}&4&1&0 \end{array}} \right) \\ \hfill \underrightarrow {{R_2} \to {R_2} - 2{R_1}}\left( {\begin{array}{*{20}{c}} 1&0&1&0 \\ 0&{ - 1}&{ - 1}&0 \\ { - 3}&4&1&0 \end{array}} \right) \\ \hfill \underrightarrow {{R_3} \to {R_3} + 3{R_1}}\left( {\begin{array}{*{20}{c}} 1&0&1&0 \\ 0&{ - 1}&{ - 1}&0 \\ 0&4&4&0 \end{array}} \right) \\ \hfill \underrightarrow {{R_3} \to {R_3} + 4{R_2}}\left( {\begin{array}{*{20}{c}} 1&0&1&0 \\ 0&{ - 1}&{ - 1}&0 \\ 0&0&0&0 \end{array}} \right) \\ \end{gathered} \)

From the obtained matrix, it can be seen that \(\left( {A - 4I} \right){\bf{x}} = 0\) has a nontrivial solution, which does not need to find. Still, according to the definition of eigenvalue, if \(\left( {A - \lambda I} \right){\bf{x}} = 0\) has a nontrivial solution, then scaler \(\lambda \) is the eigenvalue of the matrix \(A\). So \(\lambda = 4\) is the eigenvalue of the given matrix.

04

Find the eigenvector 

Write the obtained matrix in the form of equations.

\(\begin{array}{c}{x_1} + {x_3} = 0\\ - {x_2} + {x_3} = 0\\{x_3},{\rm{ free variable}}\end{array}\)

As \({x_3}\) is a free variable, let \({x_3} = 1\). Then find \({x_1}\), and \({x_2}\) by using \({x_3} = 1\).

\(\begin{array}{c}{x_1} = - 1\\{x_2} = - 1\end{array}\)

The eigenvector is given by \({\bf{x}} = \left( {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{array}} \right)\). So, write the eigenvector by using the obtained values.

\({\bf{x}} = \left( {\begin{array}{*{20}{c}}{ - 1}\\{ - 1}\\1\end{array}} \right)\)

Hence, one of the corresponding eigenvectors is \({\bf{x}} = \left( {\begin{array}{*{20}{c}}{ - 1}\\{ - 1}\\1\end{array}} \right)\).

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Most popular questions from this chapter

Let\(D = \left\{ {{{\bf{d}}_1},{{\bf{d}}_2}} \right\}\) and \(B = \left\{ {{{\bf{b}}_1},{{\bf{b}}_2}} \right\}\) be bases for vector space \(V\) and \(W\), respectively. Let \(T:V \to W\) be a linear transformation with the property that

\(T\left( {{{\bf{d}}_1}} \right) = 2{{\bf{b}}_1} - 3{{\bf{b}}_2}\), \(T\left( {{{\bf{d}}_2}} \right) = - 4{{\bf{b}}_1} + 5{{\bf{b}}_2}\)

Find the matrix for \(T\) relative to \(D\), and\(B\).

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.

12. \(\left[ {\begin{array}{*{20}{c}}- 1&0&1\\- 3&4&1\\0&0&2\end{array}} \right]\)

Let\(\varepsilon = \left\{ {{{\bf{e}}_1},{{\bf{e}}_2},{{\bf{e}}_3}} \right\}\) be the standard basis for \({\mathbb{R}^3}\),\(B = \left\{ {{{\bf{b}}_1},{{\bf{b}}_2},{{\bf{b}}_3}} \right\}\) be a basis for a vector space \(V\) and\(T:{\mathbb{R}^3} \to V\) be a linear transformation with the property that

\(T\left( {{x_1},{x_2},{x_3}} \right) = \left( {{x_3} - {x_2}} \right){{\bf{b}}_1} - \left( {{x_1} - {x_3}} \right){{\bf{b}}_2} + \left( {{x_1} - {x_2}} \right){{\bf{b}}_3}\)

  1. Compute\(T\left( {{{\bf{e}}_1}} \right)\), \(T\left( {{{\bf{e}}_2}} \right)\) and \(T\left( {{{\bf{e}}_3}} \right)\).
  2. Compute \({\left( {T\left( {{{\bf{e}}_1}} \right)} \right)_B}\), \({\left( {T\left( {{{\bf{e}}_2}} \right)} \right)_B}\) and \({\left( {T\left( {{{\bf{e}}_3}} \right)} \right)_B}\).
  3. Find the matrix for \(T\) relative to \(\varepsilon \), and\(B\).

Let \({\bf{u}}\) be an eigenvector of \(A\) corresponding to an eigenvalue \(\lambda \), and let \(H\) be the line in \({\mathbb{R}^{\bf{n}}}\) through \({\bf{u}}\) and the origin.

  1. Explain why \(H\) is invariant under \(A\) in the sense that \(A{\bf{x}}\) is in \(H\) whenever \({\bf{x}}\) is in \(H\).
  2. Let \(K\) be a one-dimensional subspace of \({\mathbb{R}^{\bf{n}}}\) that is invariant under \(A\). Explain why \(K\) contains an eigenvector of \(A\).

In Exercises \({\bf{3}}\) and \({\bf{4}}\), use the factorization \(A = PD{P^{ - {\bf{1}}}}\) to compute \({A^k}\) where \(k\) represents an arbitrary positive integer.

4. \(\left( {\begin{array}{*{20}{c}}{ - 2}&{12}\\{ - 1}&5\end{array}} \right) = \left( {\begin{array}{*{20}{c}}3&4\\1&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}2&0\\0&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}{ - 1}&4\\1&{ - 3}\end{array}} \right)\)

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