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Orhtogonally diagonalize the matrices in Exercises 37-40. To practice the methods of this section, do not use an eigenvector routine from your matrix program. Instead, use the program to find the eigenvalues, and for each eigenvalue \(\lambda \), find an orthogonal basis for \({\bf{Nul}}\left( {A - \lambda I} \right)\), as in Examples 2 and 3.

40. \(\left( {\begin{aligned}{{}}{\bf{8}}&{\bf{2}}&{\bf{2}}&{ - {\bf{6}}}&{\bf{9}}\\{\bf{2}}&{\bf{8}}&{\bf{2}}&{ - {\bf{6}}}&{\bf{9}}\\{\bf{2}}&{\bf{2}}&{\bf{8}}&{ - {\bf{6}}}&{\bf{9}}\\{ - {\bf{6}}}&{ - {\bf{6}}}&{ - {\bf{6}}}&{{\bf{24}}}&{\bf{9}}\\{\bf{9}}&{\bf{9}}&{\bf{9}}&{\bf{9}}&{ - {\bf{21}}}\end{aligned}} \right)\)

Short Answer

Expert verified

\(P = \left( {\begin{aligned}{{}}{ - \frac{1}{{2\sqrt 3 }}}&{ - \frac{1}{{\sqrt 2 }}}&{ - \frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\{ - \frac{1}{{2\sqrt 3 }}}&{\frac{1}{{\sqrt 2 }}}&0&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\{ - \frac{1}{{2\sqrt 3 }}}&0&{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\{\frac{3}{{2\sqrt 3 }}}&0&0&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\0&0&0&{\frac{1}{{\sqrt 5 }}}&{\frac{2}{{\sqrt 5 }}}\end{aligned}} \right)\), \(D = \left( {\begin{aligned}{{}}{30}&0&0&0&0\\0&6&0&0&0\\0&0&6&0&0\\0&0&0&{15}&0\\0&0&0&0&{ - 30}\end{aligned}} \right)\)

Step by step solution

01

Step 1:Find the eigenvalues of the matrix

Use the following MATLAB code to find the eigenvalues of the given matrix:

\(\begin{aligned}{} > > A &= \left( \begin{aligned}{}\begin{aligned}{{}}8&2&2&{ - 6}&{9;\,\,\begin{aligned}{{}}2&8&2&{ - 6}&9\end{aligned}}\end{aligned};\,\begin{aligned}{{}}2&2&8&{ - 6}&9\end{aligned};\,\,\begin{aligned}{{}}{ - 6}&{ - 6}&{ - 6}&{24}&9\end{aligned};\\\begin{aligned}{{}}9&9&9&9&{ - 21}\end{aligned}\end{aligned} \right);\\ > > \left( {\begin{aligned}{{}}{\rm{E}}&{\rm{V}}\end{aligned}} \right) &= {\rm{eigs}}\left( A \right);\end{aligned}\)

So, the eigenvalues are\(E = \left( {\begin{aligned}{{}}{30}\\6\\6\\{15}\\{ - 30}\end{aligned}} \right)\).

02

Step 2:Find the eigenvectors of the matrix

Use the following MATLAB code to find eigenvectors.

\( > > {v_i} = {\rm{nullbasis}}\left( {A - E\left( i \right)*{\rm{eye}}\left( 5 \right)} \right)\)

Following are the eigenvectors of A.

\({v_1} = \left( {\begin{aligned}{{}}{ - \frac{1}{3}}\\{ - \frac{1}{3}}\\{ - \frac{1}{3}}\\1\\0\end{aligned}} \right)\), \({v_2} = \left( {\begin{aligned}{{}}{ - 1}\\1\\0\\0\\0\end{aligned}} \right)\), \({v_3} = \left( {\begin{aligned}{{}}{ - 1}\\0\\1\\0\\0\end{aligned}} \right)\), \({v_4} = \left( {\begin{aligned}{{}}1\\1\\1\\1\\1\end{aligned}} \right)\), and \({v_5} = \left( {\begin{aligned}{{}}{ - \frac{1}{4}}\\{ - \frac{1}{4}}\\{ - \frac{1}{4}}\\{ - \frac{1}{4}}\\1\end{aligned}} \right)\)

03

Step 3:Find the orthogonal projection

The orthogonal projections can be calculated as follows:

\(\begin{aligned}{}{{\bf{u}}_1} = \frac{1}{{\left\| {{v_1}} \right\|}}{v_1}\\ = \left( {\begin{aligned}{{}}{ - \frac{1}{{2\sqrt 3 }}}\\{ - \frac{1}{{2\sqrt 3 }}}\\{ - \frac{1}{{2\sqrt 3 }}}\\{\frac{3}{{2\sqrt 3 }}}\\0\end{aligned}} \right)\end{aligned}\)

And,

\(\begin{aligned}{}{{\bf{u}}_2} = \frac{1}{{\left\| {{v_2}} \right\|}}{v_2}\\ = \left( {\begin{aligned}{{}}{ - \frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\\0\\0\\0\end{aligned}} \right)\end{aligned}\)

And,

\(\begin{aligned}{}{{\bf{u}}_3} = \frac{1}{{\left\| {{v_3}} \right\|}}{v_3}\\ = \left( {\begin{aligned}{{}}{ - \frac{1}{{\sqrt 2 }}}\\0\\{\frac{1}{{\sqrt 2 }}}\\0\\0\end{aligned}} \right)\end{aligned}\)

And,

\(\begin{aligned}{}{{\bf{u}}_4} = \frac{1}{{\left\| {{v_4}} \right\|}}{v_4}\\ = \left( {\begin{aligned}{{}}{\frac{1}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\end{aligned}} \right)\end{aligned}\)

And,

\(\begin{aligned}{}{{\bf{u}}_5} = \frac{1}{{\left\| {{v_5}} \right\|}}{v_5}\\ = \left( {\begin{aligned}{{}}{ - \frac{1}{{2\sqrt 5 }}}\\{ - \frac{1}{{2\sqrt 5 }}}\\{ - \frac{1}{{2\sqrt 5 }}}\\{ - \frac{1}{{2\sqrt 5 }}}\\{\frac{2}{{\sqrt 5 }}}\end{aligned}} \right)\end{aligned}\)

04

Step 4:Find the matrix P and D

The matrix P can be written using orthogonal projections.

\(P = \left( {\begin{aligned}{{}}{ - \frac{1}{{2\sqrt 3 }}}&{ - \frac{1}{{\sqrt 2 }}}&{ - \frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\{ - \frac{1}{{2\sqrt 3 }}}&{\frac{1}{{\sqrt 2 }}}&0&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\{ - \frac{1}{{2\sqrt 3 }}}&0&{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\{\frac{3}{{2\sqrt 3 }}}&0&0&{\frac{1}{{\sqrt 5 }}}&{ - \frac{1}{{2\sqrt 5 }}}\\0&0&0&{\frac{1}{{\sqrt 5 }}}&{\frac{2}{{\sqrt 5 }}}\end{aligned}} \right)\)

The diagonalized matrix can be written as\(D = \left( {\begin{aligned}{{}}{30}&0&0&0&0\\0&6&0&0&0\\0&0&6&0&0\\0&0&0&{15}&0\\0&0&0&0&{ - 30}\end{aligned}} \right)\).

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Most popular questions from this chapter

Determine which of the matrices in Exercises 7โ€“12 are orthogonal. If orthogonal, find the inverse.

12. \(P = \left( {\begin{aligned}{{}}{.5}&{.5}&{ - .5}&{ - .5}\\{.5}&{.5}&{.5}&{.5}\\{.5}&{ - .5}&{ - .5}&{.5}\\{.5}&{ - .5}&{.5}&{ - .5}\end{aligned}} \right)\)

Orhtogonally diagonalize the matrices in Exercises 37-40. To practice the methods of this section, do not use an eigenvector routine from your matrix program. Instead, use the program to find the eigenvalues, and for each eigenvalue \(\lambda \), find an orthogonal basis for \({\bf{Nul}}\left( {A - \lambda I} \right)\), as in Examples 2 and 3.

37. \(\left( {\begin{aligned}{{}}{\bf{6}}&{\bf{2}}&{\bf{9}}&{ - {\bf{6}}}\\{\bf{2}}&{\bf{6}}&{ - {\bf{6}}}&{\bf{9}}\\{\bf{9}}&{ - {\bf{6}}}&{\bf{6}}&{\bf{2}}\\{\bf{6}}&{\bf{9}}&{\bf{2}}&{\bf{6}}\end{aligned}} \right)\)

Question: 3. Let A be an \(n \times n\) symmetric matrix of rank r. Explain why the spectral decomposition of A represents A as the sum of r rank 1 matrices.

Question: In Exercises 1 and 2, convert the matrix of observations to mean deviation form, and construct the sample covariance matrix.

\(2.\,\,\left( {\begin{array}{*{20}{c}}1&5&2&6&7&3\\3&{11}&6&8&{15}&{11}\end{array}} \right)\)

In Exercises 25 and 26, mark each statement True or False. Justify each answer.

a. An\(n \times n\)matrix that is orthogonally diagonalizable must be symmetric.

b. If\({A^T} = A\)and if vectors\({\rm{u}}\)and\({\rm{v}}\)satisfy\(A{\rm{u}} = {\rm{3u}}\)and\(A{\rm{v}} = {\rm{3v}}\), then\({\rm{u}} \cdot {\rm{v}} = {\rm{0}}\).

c. An\(n \times n\)symmetric matrix has n distinct real eigenvalues.

d. For a nonzero \({\rm{v}}\) in \({\mathbb{R}^n}\) , the matrix \({\rm{v}}{{\rm{v}}^T}\) is called a projection matrix.

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