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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.

14. \({\bf{3}}x_{\bf{1}}^{\bf{2}} + {\bf{4}}{x_{\bf{1}}}{x_{\bf{2}}}\)

Short Answer

Expert verified

The new quadratic form is \(Q\left( {\rm{y}} \right) = - y_1^2 + 4y_2^2\).

Step by step solution

01

Step 1: Find the coefficient matrix of the quadratic form

Consider \(3x_1^2 + 4{x_1}{x_2}\).

As the quadratic form is:

\({{\rm{x}}^T}A{\rm{x}} = \left( {\begin{aligned}{{}}{{x_1}}&{{x_2}}\end{aligned}} \right)\left( {\begin{aligned}{{}}3&2\\2&0\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\)

Therefore, thecoefficient matrix of the quadratic form is\(A = \left( {\begin{aligned}{{}}3&2\\2&0\end{aligned}} \right)\).

Now find the characteristic polynomial.

\(\begin{aligned}{}\left| {\begin{aligned}{{}}{3 - \lambda }&2\\2&{ - \lambda }\end{aligned}} \right| &= 0\\\left( {3 - \lambda } \right)\left( { - \lambda } \right) - 4 &= 0\\{\lambda ^2} - 3\lambda - 4 &= 0\\\lambda & = - 1,4\end{aligned}\)

02

Find the basis for eigen value \({\bf{ - 1}}\)

\(\begin{aligned}{}\left( {A - I} \right){\bf{x}} &= 0\\\left( {\begin{aligned}{{}}4&2\\2&1\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) &= \left( {\begin{aligned}{{}}0\\0\end{aligned}} \right)\\4{x_1} + 2{x_2} &= 0\\2{x_1} + {x_2} &= 0\end{aligned}\)

Thus, the eigenvector is \({{\rm{v}}_1} = \left( {\begin{aligned}{{}}{ - 1}\\2\end{aligned}} \right)\).

03

Find the basis for eigen value \({\bf{4}}\)

\(\begin{aligned}{}\left( {A - 0 \cdot I} \right){\bf{x}} &= 0\\\left( {\begin{aligned}{{}}{ - 1}&2\\2&{ - 4}\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) &= \left( {\begin{aligned}{{}}0\\0\end{aligned}} \right)\\ - {x_1} + 2{x_2} &= 0\\2{x_1} - 4{x_2} &= 0\end{aligned}\)

Thus, the eigenvector is \({{\rm{v}}_2} = \left( {\begin{aligned}{{}}2\\1\end{aligned}} \right)\).

04

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

Write the normalization of the vectors.

\(\begin{aligned}{}{{\bf{u}}_1} &= \frac{1}{{\sqrt {1 + 4} }}\left( {\begin{aligned}{{}}{ - 1}\\2\end{aligned}} \right)\\ & = \left( {\begin{aligned}{{}}{ - \frac{1}{{\sqrt 5 }}}\\{\frac{2}{{\sqrt 5 }}}\end{aligned}} \right)\end{aligned}\)

and

\(\begin{aligned}{}{{\bf{u}}_2} & = \frac{1}{{\sqrt {1 + 4} }}\left( {\begin{aligned}{{}}2\\1\end{aligned}} \right)\\ & = \left( \begin{aligned}{l}\frac{2}{{\sqrt 5 }}\\\frac{1}{{\sqrt 5 }}\end{aligned} \right)\end{aligned}\)

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

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

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

\(\begin{aligned}{}Q\left( {\rm{x}} \right) & = {{\rm{x}}^T}A{\rm{x}}\\ & = {\left( {P{\rm{y}}} \right)^T}A\left( {P{\rm{y}}} \right)\\ & = {{\rm{y}}^T}{P^T}AP{\rm{y}}\\ & = {{\rm{y}}^T}D{\rm{y}}\\ & = 10y_1^2\end{aligned}\)

Therefore,

\(\begin{aligned}{}{{\rm{y}}^T}D{\rm{y}} & = \left( {\begin{aligned}{{}}{{y_1}}&{{y_2}}\end{aligned}} \right)\left( {\begin{aligned}{{}}{ - 1}&0\\0&4\end{aligned}} \right)\left( {\begin{aligned}{{}}{{y_1}}\\{{y_2}}\end{aligned}} \right)\\ & = \left( {\begin{aligned}{{}}{ - {y_1}}&{4{y_2}}\end{aligned}} \right)\left( {\begin{aligned}{{}}{{y_1}}\\{{y_2}}\end{aligned}} \right)\\ & = - y_1^2 + 4y_2^2\end{aligned}\)

Thus, the new quadratic form is \(Q\left( {\rm{y}} \right) = - y_1^2 + 4y_2^2\).

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Most popular questions from this chapter

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.

21. \(\left( {\begin{aligned}{{}}4&3&1&1\\3&4&1&1\\1&1&4&3\\1&1&3&4\end{aligned}} \right)\)

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.

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

Let u be a unit vector in \({\mathbb{R}^n}\), and let \(B = {\bf{u}}{{\bf{u}}^T}\).

  1. Given any x in \({\mathbb{R}^n}\), compute Bx and show that Bx is the orthogonal projection of x onto u, as described in Section 6.2.
  2. Show that B is a symmetric matrix and \({B^{\bf{2}}} = B\).
  3. Show that u is an eigenvector of B. What is the corresponding eigenvalue?

Question: 13. Exercises 12–14 concern an \(m \times n\) matrix \(A\) with a reduced singular value decomposition, \(A = {U_r}D{V_r}^T\), and the pseudoinverse \({A^ + } = {U_r}{D^{ - 1}}{V_r}^T\).

Suppose the equation\(A{\rm{x}} = {\rm{b}}\)is consistent, and let\({{\rm{x}}^ + } = {A^ + }{\rm{b}}\). By Exercise 23 in Section 6.3, there is exactly one vector\({\rm{p}}\)in Row\(A\)such that\(A{\rm{p}} = {\rm{b}}\). The following steps prove that\({{\rm{x}}^ + } = {\rm{p}}\)and\({{\rm{x}}^ + }\)is the minimum length solution of\(A{\rm{x}} = {\rm{b}}\).

  1. Show that \({{\rm{x}}^ + }\) is in Row \(A\). (Hint: Write \({\rm{b}}\) as \(A{\rm{x}}\) for some \({\rm{x}}\), and use Exercise 12.)
  2. Show that\({{\rm{x}}^ + }\)is a solution of\(A{\rm{x}} = {\rm{b}}\).
  3. Show that if \({\rm{u}}\) is any solution of \(A{\rm{x}} = {\rm{b}}\), then \(\left\| {{{\rm{x}}^ + }} \right\| \le \left\| {\rm{u}} \right\|\), with equality only if \({\rm{u}} = {{\rm{x}}^ + }\).

Question:Find the principal components of the data for Exercise 1.

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