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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

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: 13. The sample covariance matrix is a generalization of a formula for the variance of a sample of \(N\) scalar measurements, say \({t_1},................,{t_N}\). If \(m\) is the average of \({t_1},................,{t_N}\), then the sample variance is given by

\(\frac{1}{{N - 1}}\sum\limits_{k = 1}^n {{{\left( {{t_k} - m} \right)}^2}} \)

Show how the sample covariance matrix, \(S\), defined prior to Example 3, may be written in a form similar to (1). (Hint: Use partitioned matrix multiplication to write \(S\) as \(\frac{1}{{N - 1}}\) times the sum of \(N\) matrices of size \(p \times p\). For \(1 \le k \le N\), write \({X_k} - M\) in place of \({\hat X_k}\).)

In Exercises 17โ€“24, \(A\) is an \(m \times n\) matrix with a singular value decomposition \(A = U\Sigma {V^T}\) ,

\(\)

where \(U\) is an \(m \times m\) orthogonal matrix, \({\bf{\Sigma }}\) is an \(m \times n\) โ€œdiagonalโ€ matrix with \(r\) positive entries and no negative entries, and \(V\) is an \(n \times n\) orthogonal matrix. Justify each answer.

18. Suppose \(A\) is square and invertible. Find a singular value decomposition of \({A^{ - 1}}\)

Question: Compute the singular values of the \({\bf{4 \times 4}}\) matrix in Exercise 9 in Section 2.3, and compute the condition number \(\frac{{{\sigma _1}}}{{{\sigma _4}}}\).

Question: Find the principal components of the data for Exercise 2.

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