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Classify the quadratic forms in Exercises 9–18. Then make a change of variable, \({\bf{x}} = P{\bf{y}}\), that transforms the quadratic form into one with no cross-product term. Write the new quadratic form. Construct \(P\) using the methods of Section 7.1.

17. \({\bf{11}}x_{\bf{1}}^{\bf{2}}{\bf{ + 11}}x_{\bf{2}}^{\bf{2}}{\bf{ + 11}}x_{\bf{3}}^{\bf{2}}{\bf{ + 11}}x_{\bf{4}}^{\bf{2}}{\bf{ + 16}}{x_{\bf{1}}}{x_{\bf{2}}}{\bf{ - 12}}{x_{\bf{1}}}{x_{\bf{4}}}{\bf{ + 12}}{x_{\bf{2}}}{x_{\bf{3}}}{\bf{ + 16}}{x_{\bf{3}}}{x_{\bf{4}}}\)

Short Answer

Expert verified

The new quadratic form is \(Q\left( {\rm{y}} \right) = 21y_1^2 + 21y_2^2 + y_3^2 + y_4^2\).

Step by step solution

01

Step 1: Find the coefficient matrix of the quadratic form

Consider \(11x_1^2 + 11x_2^2 + 11x_3^2 + 11x_4^2 + 16{x_1}{x_2} - 12{x_1}{x_4} + 12{x_2}{x_3} + 16{x_3}{x_4}\).

As the quadratic form is:

\({{\rm{x}}^T}A{\rm{x}} = \left( {\begin{aligned}{{}}{{x_1}}&{{x_2}}&{{x_3}}&{{x_4}}\end{aligned}} \right)\left( {\begin{aligned}{{}}{11}&8&0&{ - 6}\\8&{11}&6&0\\0&6&{11}&8\\{ - 6}&0&8&{11}\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\\{{x_4}}\end{aligned}} \right)\)

Therefore, the coefficient matrix of the quadratic form is \(A = \left( {\begin{aligned}{{}}{11}&8&0&{ - 6}\\8&{11}&6&0\\0&6&{11}&8\\{ - 6}&0&8&{11}\end{aligned}} \right)\).

02

Find the eigen values

Using the MATLAB command\({\rm{eigs}}\left( A \right)\), the eigenvalues are as:

\(\begin{aligned}{}{\lambda _1} = 21,\\{\lambda _2} = 21,\\{\lambda _3} = 1,\\{\lambda _4} = 1\end{aligned}\)

The eigenvectors are:

\({{\rm{v}}_1} = \left( {\begin{aligned}{{}}4\\5\\3\\0\end{aligned}} \right)\), \({{\rm{v}}_2} = \left( {\begin{aligned}{{}}{ - 5}\\{ - 4}\\0\\3\end{aligned}} \right)\), \({{\rm{v}}_3} = \left( {\begin{aligned}{{}}4\\{ - 5}\\3\\0\end{aligned}} \right)\), \({{\rm{v}}_4} = \left( {\begin{aligned}{{}}5\\{ - 4}\\0\\3\end{aligned}} \right)\)

03

Find the matrix \(P\) and \(D\)

Write the normalization of the vectors.

\({{\bf{u}}_1} = \left( {\begin{aligned}{{}}{\frac{{2\sqrt 2 }}{5}}\\{\frac{{\sqrt 2 }}{2}}\\{\frac{{3\sqrt 2 }}{{10}}}\\0\end{aligned}} \right)\),\({{\bf{u}}_2} = \left( {\begin{aligned}{{}}{ - \frac{{3\sqrt 2 }}{{10}}}\\0\\{\frac{{2\sqrt 2 }}{5}}\\{\frac{{\sqrt 2 }}{2}}\end{aligned}} \right)\),\({{\bf{u}}_3} = \left( {\begin{aligned}{{}}{\frac{{2\sqrt 2 }}{5}}\\{\frac{{ - \sqrt 2 }}{2}}\\{\frac{{3\sqrt 2 }}{{10}}}\\0\end{aligned}} \right)\),\({{\bf{u}}_4} = \left( {\begin{aligned}{{}}{\frac{{3\sqrt 2 }}{{10}}}\\0\\{ - \frac{{2\sqrt 2 }}{5}}\\{\frac{{\sqrt 2 }}{2}}\end{aligned}} \right)\)

Then the matrix\(P\)and\(D\)is shown below:

\(P = \left( {\begin{aligned}{{}}{\frac{{2\sqrt 2 }}{5}}&{ - \frac{{3\sqrt 2 }}{{10}}}&{\frac{{2\sqrt 2 }}{5}}&{\frac{{3\sqrt 2 }}{{10}}}\\{\frac{{\sqrt 2 }}{2}}&0&{ - \frac{{\sqrt 2 }}{2}}&0\\{\frac{{3\sqrt 2 }}{{10}}}&{\frac{{2\sqrt 2 }}{5}}&{\frac{{3\sqrt 2 }}{{10}}}&{ - \frac{{2\sqrt 2 }}{5}}\\0&{\frac{{\sqrt 2 }}{2}}&0&{\frac{{\sqrt 2 }}{2}}\end{aligned}} \right)\)and\(D = \left( {\begin{aligned}{{}}{21}&0&0&0\\0&{21}&0&0\\0&0&1&0\\0&0&0&1\end{aligned}} \right)\)

Now write the new quadratic form by using the transformation\({\rm{x}} = P{\rm{y}}\).

\(\begin{aligned}{}Q\left( {\rm{y}} \right) &= {{\rm{y}}^T}D{\rm{y}}\\ & = \left( {\begin{aligned}{{}}{{y_1}}&{{y_2}}&{{y_3}}&{{y_4}}\end{aligned}} \right)\left( {\begin{aligned}{{}}{21}&0&0&0\\0&{21}&0&0\\0&0&1&0\\0&0&0&1\end{aligned}} \right)\left( {\begin{aligned}{{}}{{y_1}}\\{{y_2}}\\{{y_3}}\\{{y_4}}\end{aligned}} \right)\\ & = 21y_1^2 + 21y_2^2 + y_3^2 + y_4^2\end{aligned}\)

Thus, the new quadratic form is \(Q\left( {\rm{y}} \right) = 21y_1^2 + 21y_2^2 + y_3^2 + y_4^2\).

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Most popular questions from this chapter

Classify the quadratic forms in Exercises 9–18. Then make a change of variable, \({\bf{x}} = P{\bf{y}}\), that transforms the quadratic form into one with no cross-product term. Write the new quadratic form. Construct \(P\) using the methods of Section 7.1.

12.\({\bf{ - }}x_{\bf{1}}^{\bf{2}}{\bf{ - 2}}{x_{\bf{1}}}{x_{\bf{2}}} - x_{\bf{2}}^{\bf{2}}\)

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

8. \(\left( {\begin{aligned}{{}}1&{\,\,\,1}\\1&{ - 1}\end{aligned}} \right)\)

Orthogonally diagonalize the matrices in Exercises 13–22, giving an orthogonal matrix\(P\)and a diagonal matrix\(D\). To save you time, the eigenvalues in Exercises 17–22 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.

14. \(\left( {\begin{aligned}{{}}{\,1}&{ - 5}\\{ - 5}&{\,\,1}\end{aligned}} \right)\)

Determine which of the matrices in Exercises 1–6 are symmetric.

5. \(\left( {\begin{aligned}{{}{}}{ - 6}&2&0\\2&{ - 6}&2\\0&2&{ - 6}\end{aligned}} \right)\)

Question: [M] A Landsat image with three spectral components was made of Homestead Air Force Base in Florida (after the base was hit by Hurricane Andrew in 1992). The covariance matrix of the data is shown below. Find the first principal component of the data, and compute the percentage of the total variance that is contained in this component.

\[S = \left[ {\begin{array}{*{20}{c}}{164.12}&{32.73}&{81.04}\\{32.73}&{539.44}&{249.13}\\{81.04}&{246.13}&{189.11}\end{array}} \right]\]

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