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(M) 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.

39. \(\left( {\begin{aligned}{{}}{.{\bf{31}}}&{.{\bf{58}}}&{.{\bf{08}}}&{.{\bf{44}}}\\{.{\bf{58}}}&{ - .{\bf{56}}}&{.{\bf{44}}}&{ - .{\bf{58}}}\\{.{\bf{08}}}&{.{\bf{44}}}&{.{\bf{19}}}&{ - .{\bf{08}}}\\{ - .{\bf{44}}}&{ - .{\bf{58}}}&{ - .{\bf{08}}}&{.{\bf{31}}}\end{aligned}} \right)\)

Short Answer

Expert verified

\(P = \left( {\begin{aligned}{{}}{\frac{1}{{\sqrt 2 }}}&{\frac{3}{{\sqrt {50} }}}&{ - \frac{2}{5}}&{ - \frac{2}{5}}\\0&{\frac{4}{{\sqrt {50} }}}&{ - \frac{1}{5}}&{\frac{4}{5}}\\0&{\frac{4}{{\sqrt {50} }}}&{\frac{4}{5}}&{ - \frac{1}{5}}\\{\frac{1}{{\sqrt 2 }}}&{ - \frac{3}{{\sqrt {50} }}}&{\frac{2}{5}}&{\frac{2}{5}}\end{aligned}} \right)\), \(D = \left( {\begin{aligned}{{}}{ - 1.25}&0&0&0\\0&{0.75}&0&0\\0&0&{0.75}&0\\0&0&0&0\end{aligned}} \right)\)

Step by step solution

01

Step 1:Findthe eigenvalues of the matrix

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

\(\begin{aligned}{} > > A = \left( \begin{aligned}{}\begin{aligned}{{}}{.31}&{.58}&{.08}&{.44}\end{aligned};\,\begin{aligned}{{}}{\,.58}&{ - .56}&{.44}&{ - .58}\end{aligned};\,\begin{aligned}{{}}{\,.08}&{.44}&{.19}&{ - .08}\end{aligned};\\\,\begin{aligned}{{}}{ - .44}&{.58}&{ - .08}&{.31}\end{aligned}\end{aligned} \right);\\ > > \left( {\begin{aligned}{{}}{\rm{E}}&{\rm{V}}\end{aligned}} \right) = {\rm{eigs}}\left( A \right);\end{aligned}\)

So, the eigenvalues are\(E = \left( {\begin{aligned}{{}}{ - 1.25}\\{0.75}\\{0.75}\\0\end{aligned}} \right)\).

02

Step 2:Find the eigenvectors of the matrix

Use the following MATLAB code to find eigenvectors.

\( > > {v_i} = {\rm{nullbasis}}\left( {A - E\left( i \right)*{\rm{eye}}\left( 4 \right)} \right)\)

Following are the eigenvectors of A.

\({v_1} = \left( {\begin{aligned}{{}}1\\0\\0\\1\end{aligned}} \right)\), \({v_2} = \left( {\begin{aligned}{{}}3\\2\\2\\0\end{aligned}} \right)\), \({v_3} = \left( {\begin{aligned}{{}}1\\0\\0\\1\end{aligned}} \right)\), and \({v_4} = \left( {\begin{aligned}{{}}3\\4\\4\\{ - 3}\end{aligned}} \right)\)

03

Step 3:Find the orthogonal projection

The orthogonal projections can be calculated as follows:

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

And,

\(\begin{aligned}{}{{\bf{u}}_2} &= \frac{1}{{\left\| {{v_2}} \right\|}}{v_2}\\ &= \left( {\begin{aligned}{{}}{\frac{3}{{\sqrt {50} }}}\\{\frac{4}{{\sqrt {50} }}}\\{\frac{4}{{\sqrt {50} }}}\\{ - \frac{3}{{\sqrt {50} }}}\end{aligned}} \right)\end{aligned}\)

And,

\(\begin{aligned}{}{{\bf{u}}_3} &= \frac{1}{{\left\| {{v_3}} \right\|}}{v_3}\\ &= \left( {\begin{aligned}{{}}{ - \frac{2}{5}}\\{ - \frac{1}{5}}\\{\frac{4}{5}}\\{\frac{2}{5}}\end{aligned}} \right)\end{aligned}\)

And,

\(\begin{aligned}{}{{\bf{u}}_4} &= \frac{1}{{\left\| {{v_4}} \right\|}}{v_4}\\ &= \left( {\begin{aligned}{{}}{ - \frac{2}{5}}\\{\frac{4}{5}}\\{ - \frac{1}{5}}\\{\frac{2}{5}}\end{aligned}} \right)\end{aligned}\)

04

Step 4:Find the matrix P and D 

The matrix P can be written using orthogonal projections as:

\(P = \left( {\begin{aligned}{{}}{\frac{1}{{\sqrt 2 }}}&{\frac{3}{{\sqrt {50} }}}&{ - \frac{2}{5}}&{ - \frac{2}{5}}\\0&{\frac{4}{{\sqrt {50} }}}&{ - \frac{1}{5}}&{\frac{4}{5}}\\0&{\frac{4}{{\sqrt {50} }}}&{\frac{4}{5}}&{ - \frac{1}{5}}\\{\frac{1}{{\sqrt 2 }}}&{ - \frac{3}{{\sqrt {50} }}}&{\frac{2}{5}}&{\frac{2}{5}}\end{aligned}} \right)\)

The diagonalized matrix can be written as\(D = \left( {\begin{aligned}{{}}{ - 1.25}&0&0&0\\0&{0.75}&0&0\\0&0&{0.75}&0\\0&0&0&0\end{aligned}} \right)\).

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Most popular questions from this chapter

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

26.

  1. There are symmetric matrices that are not orthogonally diagonizable.
  2. b. If \(B = PD{P^T}\), where \({P^T} = {P^{ - {\bf{1}}}}\) and D is a diagonal matrix, then B is a symmetric matrix.
  3. c. An orthogonal matrix is orthogonally diagonizable.
  4. d. The dimension of an eigenspace of a symmetric matrix is sometimes less than the multiplicity of the corresponding eigenvalue.

Question: 6. Let A be an \(n \times n\) symmetric matrix. Use Exercise 5 and an eigenvector basis for \({\mathbb{R}^n}\) to give a second proof of the decomposition in Exercise 4(b).

Orthogonally diagonalize the matrices in Exercises 13โ€“22, giving an orthogonal matrix\(P\)and a diagonal matrix\(D\). To save you time, the eigenvalues in Exercises 17โ€“22 are: (17)\( - {\bf{4}}\), 4, 7; (18)\( - {\bf{3}}\),\( - {\bf{6}}\), 9; (19)\( - {\bf{2}}\), 7; (20)\( - {\bf{3}}\), 15; (21) 1, 5, 9; (22) 3, 5.

17. \(\left( {\begin{aligned}{{}}1&{ - 6}&4\\{ - 6}&2&{ - 2}\\4&{ - 2}&{ - 3}\end{aligned}} \right)\)

Orthogonally diagonalize the matrices in Exercises 13โ€“22, giving an orthogonal matrix \(P\) and a diagonal matrix \(D\). To save you time, the eigenvalues in Exercises 17โ€“22 are: (17) \( - {\bf{4}}\), 4, 7; (18) \( - {\bf{3}}\), \( - {\bf{6}}\), 9; (19) \( - {\bf{2}}\), 7; (20) \( - {\bf{3}}\), 15; (21) 1, 5, 9; (22) 3, 5.

20. \(\left( {\begin{aligned}{{}}5&8&{ - 4}\\8&5&{ - 4}\\{ - 4}&{ - 4}&{ - 1}\end{aligned}} \right)\)

Question: In Exercises 15 and 16, construct the pseudo-inverse of \(A\). Begin by using a matrix program to produce the SVD of \(A\), or, if that is not available, begin with an orthogonal diagonalization of \({A^T}A\). Use the pseudo-inverse to solve \(A{\rm{x}} = {\rm{b}}\), for \({\rm{b}} = \left( {6, - 1, - 4,6} \right)\) and let \(\mathop {\rm{x}}\limits^\^ \)be the solution. Make a calculation to verify that \(\mathop {\rm{x}}\limits^\^ \) is in Row \(A\). Find a nonzero vector \({\rm{u}}\) in Nul\(A\), and verify that \(\left\| {\mathop {\rm{x}}\limits^\^ } \right\| < \left\| {\mathop {\rm{x}}\limits^\^ + {\rm{u}}} \right\|\), which must be true by Exercise 13(c).

15. \(A = \left[ {\begin{array}{*{20}{c}}{ - 3}&{ - 3}&{ - 6}&6&{\,\,1}\\{ - 1}&{ - 1}&{ - 1}&1&{ - 2}\\{\,\,\,0}&{\,\,0}&{ - 1}&1&{ - 1}\\{\,\,\,0}&{\,\,0}&{ - 1}&1&{ - 1}\end{array}} \right]\)

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