In Exercises 1 and 2,find the change of variable \({\rm{x}} = P{\rm{y}}\) that transforms the quadratic form \({{\rm{x}}^T}A{\rm{x}}\) into \({{\rm{y}}^T}D{\rm{y}}\) as shown.

1. \(5x_1^2 + 6x_2^2 + 7x_3^2 + 4x_1^{}x_2^{} - 4x_2^{}x_3^{} = 9y_1^2 + 6y_2^2 + 3y_3^2\)

Short Answer

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The requiredchange of variable is:

\(P = \left( {\begin{aligned}{{}}{\frac{1}{3}}&{\frac{2}{3}}&{ - \frac{2}{3}}\\{\frac{2}{3}}&{\frac{1}{3}}&{\frac{2}{3}}\\{ - \frac{2}{3}}&{\frac{2}{3}}&{\frac{1}{3}}\end{aligned}} \right)\).

Step by step solution

01

Symmetric Matrices and Quadratic Forms

When any Symmetric Matrix \(A\) is diagonalized orthogonally as \(PD{P^{ - 1}}\) we have:

\(\begin{aligned}{}{x^T}Ax = {y^T}Dy\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{\rm{as }}x = Py} \right\}\\{\rm{and}}\\\left\| x \right\| = \left\| {Py} \right\| = \left\| y \right\|\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {\forall y \in \mathbb{R}} \right\}\end{aligned}\)

02

Find the Change of Variables

As per the question, we have:

\(5x_1^2 + 6x_2^2 + 7x_3^2 + 4x_1^{}x_2^{} - 4x_2^{}x_3^{} = 9y_1^2 + 6y_2^2 + 3y_3^2\)

The matrix of thequadratic formwill be:

\(A = \left( {\begin{aligned}{{}}5&2&0\\2&6&{ - 2}\\0&{ - 2}&7\end{aligned}} \right)\)

And for each eigenvalue of\(A\)defined in the right-hand side of the equation will be expressed as:

\(\begin{aligned}{}{\lambda _1} = 9 \Rightarrow {P_1} = \left( {\begin{aligned}{{}}{\frac{1}{3}}\\{\frac{2}{3}}\\{ - \frac{2}{3}}\end{aligned}} \right)\\{\lambda _2} = 6 \Rightarrow {P_2} = \left( {\begin{aligned}{{}}{\frac{2}{3}}\\{\frac{1}{3}}\\{\frac{2}{3}}\end{aligned}} \right)\\{\lambda _3} = 3 \Rightarrow {P_3} = \left( {\begin{aligned}{{}}{ - \frac{2}{3}}\\{\frac{2}{3}}\\{\frac{1}{3}}\end{aligned}} \right)\end{aligned}\)

Hence,therequired change of variable is:

\(P = \left( {\begin{aligned}{{}}{\frac{1}{3}}&{\frac{2}{3}}&{ - \frac{2}{3}}\\{\frac{2}{3}}&{\frac{1}{3}}&{\frac{2}{3}}\\{ - \frac{2}{3}}&{\frac{2}{3}}&{\frac{1}{3}}\end{aligned}} \right)\).

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Most popular questions from this chapter

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.

40. \(\left( {\begin{aligned}{{}}{\bf{8}}&{\bf{2}}&{\bf{2}}&{ - {\bf{6}}}&{\bf{9}}\\{\bf{2}}&{\bf{8}}&{\bf{2}}&{ - {\bf{6}}}&{\bf{9}}\\{\bf{2}}&{\bf{2}}&{\bf{8}}&{ - {\bf{6}}}&{\bf{9}}\\{ - {\bf{6}}}&{ - {\bf{6}}}&{ - {\bf{6}}}&{{\bf{24}}}&{\bf{9}}\\{\bf{9}}&{\bf{9}}&{\bf{9}}&{\bf{9}}&{ - {\bf{21}}}\end{aligned}} \right)\)

Question: 4. Let A be an \(n \times n\) symmetric matrix.

a. Show that \({({\rm{Col}}A)^ \bot } = {\rm{Nul}}A\). (Hint: See Section 6.1.)

b. Show that each y in \({\mathbb{R}^n}\) can be written in the form \(y = \hat y + z\), with \(\hat y\) in \({\rm{Col}}A\) and z in \({\rm{Nul}}A\).

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

Let B be an \(n \times n\) symmetric matrix such that \({B^{\bf{2}}} = B\). Any such matrix is called a projection matrix (or an orthogonal projection matrix.) Given any y in \({\mathbb{R}^n}\), let \({\bf{\hat y}} = B{\bf{y}}\)and\({\bf{z}} = {\bf{y}} - {\bf{\hat y}}\).

a) Show that z is orthogonal to \({\bf{\hat y}}\).

b) Let W be the column space of B. Show that y is the sum of a vector in W and a vector in \({W^ \bot }\). Why does this prove that By is the orthogonal projection of y onto the column space of B?

Question: In Exercises 1 and 2, convert the matrix of observations to mean deviation form, and construct the sample covariance matrix.

\(1.\,\,\left( {\begin{array}{*{20}{c}}{19}&{22}&6&3&2&{20}\\{12}&6&9&{15}&{13}&5\end{array}} \right)\)

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