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Question: Is \(\lambda = 2\) an eigenvalue of \(\left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right)\)? Why or why not?

Short Answer

Expert verified

Yes, \(\lambda = 2\) is the eigenvalue of the given matrix \(\left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right)\), because there exists a nontrivial solution of \(A{\bf{x}} = 2{\bf{x}}\) and columns of the matrix \(\left( {A - 2I} \right)\) are linearly dependent.

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 an eigenvalue of the matrix \(A\) if there exists a nontrivial solution \({\bf{x}}\) of \(A{\bf{x}} = \lambda {\bf{x}}\).

02

Determine whether \(\lambda  = 2\) is the eigenvalue of the given matrix

Denote the given matrix by \(A\).

\(A = \left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right)\)

According to the definition of eigenvalue, \(\lambda = 2\) is the eigenvalue of the matrix \(A\), if satisfies the equation \(A{\bf{x}} = \lambda {\bf{x}}\).

Substitute \(\lambda = 2\) into \(A{\bf{x}} = \lambda {\bf{x}}\).

\(A{\bf{x}} = 2{\bf{x}}\)

The obtained equation is equivalent to \(\left( {A - 2I} \right){\bf{x}} = 0\), which is a homogeneous equation.

So, first, solve the matrix \(\left( {A - 2I} \right)\) by using \(A = \left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right)\).

\(\begin{array}{c}\left( {A - 2I} \right) = \left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right) - 2\left( {\begin{array}{*{20}{c}}1&0\\0&1\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right) - \left( {\begin{array}{*{20}{c}}2&0\\0&2\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}1&2\\3&6\end{array}} \right)\end{array}\)

It can be observed that the columns of the obtained matrix are linearly dependent, where elements of the second column twice the multiple of elements of the first column, which can be written as:

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

Hence, \(\left( {A - 2I} \right){\bf{x}} = 0\) has a nontrivial solution, so \(\lambda = 2\) is an eigenvalue of the given matrix \(\left( {\begin{array}{*{20}{c}}3&2\\3&8\end{array}} \right)\).

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Most popular questions from this chapter

Question: Repeat Exercise 35, assuming u and v are eigenvectors of A that correspond to eigenvalues -1 and 3, respectively.

Question: In Exercises \({\bf{5}}\) and \({\bf{6}}\), the matrix \(A\) is factored in the form \(PD{P^{ - {\bf{1}}}}\). Use the Diagonalization Theorem to find the eigenvalues of \(A\) and a basis for each eigenspace.

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

In Exercises 9–16, find a basis for the eigenspace corresponding to each listed eigenvalue.

11. \(A = \left( {\begin{array}{*{20}{c}}4&{ - 2}\\{ - 3}&9\end{array}} \right)\), \(\lambda = 10\)

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.)

Question: Consider an \(n \times n\) matrix A with the property that the row sums all equal the same number s. Show that s is an eigenvalue of A. (Hint: Find an eigenvector.)

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