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

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: 2. Let \(\left\{ {{{\bf{u}}_1},{{\bf{u}}_2},....,{{\bf{u}}_n}} \right\}\) be an orthonormal basis for \({\mathbb{R}_n}\) , and let \({\lambda _1},....{\lambda _n}\) be any real scalars. Define

\(A = {\lambda _1}{{\bf{u}}_1}{\bf{u}}_1^T + ..... + {\lambda _n}{\bf{u}}_n^T\)

a. Show that A is symmetric.

b. Show that \({\lambda _1},....{\lambda _n}\) are the eigenvalues of A

Question: 12. 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 \({A^ + } = {U_r}{D^{ - 1}}{V_r}^T\).

Verify the properties of\({A^ + }\):

a. For each\({\rm{y}}\)in\({\mathbb{R}^m}\),\(A{A^ + }{\rm{y}}\)is the orthogonal projection of\({\rm{y}}\)onto\({\rm{Col}}\,A\).

b. For each\({\rm{x}}\)in\({\mathbb{R}^n}\),\({A^ + }A{\rm{x}}\)is the orthogonal projection of\({\rm{x}}\)onto\({\rm{Row}}\,A\).

c. \(A{A^ + }A = A\)and \({A^ + }A{A^ + } = {A^ + }\).

In Exercises 17–24, \(A\) 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 \(m \times n\) “diagonal” matrix with \(r\) positive entries and no negative entries, and \(V\) is an \(n \times n\) orthogonal matrix. Justify each answer.

24. Using the notation of Exercise 23, show that \({A^T}{u_j} = {\sigma _j}{v_j}\) for \({\bf{1}} \le {\bf{j}} \le {\bf{r}} = {\bf{rank}}\;{\bf{A}}\)

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

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