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Make a change of variable, \({\bf{x}} = P{\bf{y}}\), that transforms the quadratic form \(x_{\bf{1}}^{\bf{2}} + {\bf{10}}{x_{\bf{1}}}{x_{\bf{2}}} + x_{\bf{2}}^{\bf{2}}\) into a quadratic form with no cross-product term. Give P and the new quadratic form.

Short Answer

Expert verified

The matrix Pis \(P = \left( {\begin{aligned}{{}{}}{\frac{1}{{\sqrt 2 }}}&{ - \frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\end{aligned}} \right)\).

The new quadratic form is \(6y_1^2 - 4y_2^2\).

Step by step solution

01

Find the eigenvalues of the coefficient matrix of the quadratic equation

The coefficient matrix for the equation \(x_1^2 + 10{x_1}{x_2} + x_2^2\) is shown below:

\(A = \left( {\begin{aligned}{{}{}}1&5\\5&1\end{aligned}} \right)\)

The characteristic equation of A can be written as:

\(\begin{aligned}{}\det \left( {A - \lambda I} \right) &= 0\\\left| {\begin{aligned}{{}{}}{1 - \lambda }&5\\5&{1 - \lambda }\end{aligned}} \right| &= 0\\{\left( {1 - \lambda } \right)^2} - 25 &= 0\\\lambda &= 6, - 4\end{aligned}\)

02

Find the eigen vector of matrix A

Find the eigenvector for \(\lambda = 6\):

\(\begin{aligned}{}\left( {A - 6I} \right)X &= 0\\\left( {\begin{aligned}{{}{}}{ - 5}&5\\5&{ - 5}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) &= \left( {\begin{aligned}{{}{}}0\\0\end{aligned}} \right)\\ - 5{x_1} + 5{x_2} &= 0\\{x_1} - {x_2} &= 0\end{aligned}\)

Thus, the general solution of the equation is\(\left( {\begin{aligned}{{}{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) = \left( {\begin{aligned}{{}{}}1\\1\end{aligned}} \right)\).

Find the eigenvector for \(\lambda = - 4\):

\(\begin{aligned}{}\left( {A + 4I} \right)X &= 0\\\left( {\begin{aligned}{{}{}}5&5\\5&5\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) &= \left( {\begin{aligned}{{}{}}0\\0\end{aligned}} \right)\\5{x_1} + 5{x_2} &= 0\\{x_1} + {x_2} &= 0\end{aligned}\)

Thus, the general solution of the equation is\(\left( {\begin{aligned}{{}{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) = \left( {\begin{aligned}{{}{}}{ - 1}\\1\end{aligned}} \right)\).

03

Find normalized eigenvectors of A

The normalized eigenvectors are:

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

And,

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

04

Write the matrix P and D

Write matrix Pusing the normalized eigenvectors:

\(\begin{aligned}{}P &= \left( {\begin{aligned}{{}{}}{{{\bf{u}}_1}}&{{{\bf{u}}_2}}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{{}{}}{\frac{1}{{\sqrt 2 }}}&{ - \frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\end{aligned}} \right)\end{aligned}\)

Write matrix D using the eigenvalues of A as:

\(D = \left( {\begin{aligned}{{}{}}6&0\\0&{ - 4}\end{aligned}} \right)\)

05

Find the new quadratic form

Consider the expression \({{\bf{x}}^T}A{\bf{x}}\).

\(\begin{aligned}{}{{\bf{x}}^T}A{\bf{x}} &= {\left( {P{\bf{y}}} \right)^T}A\left( {P{\bf{y}}} \right)\\ &= {{\bf{y}}^T}{P^T}AP{\bf{y}}\\ &= {{\bf{y}}^r}D{\bf{y}}\\ &= \left( {\begin{aligned}{{}{}}{{{\bf{y}}_1}}&{{{\bf{y}}_2}}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}6&0\\0&{ - 4}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{{{\bf{y}}_1}}\\{{{\bf{y}}_2}}\end{aligned}} \right)\\ &= 6y_1^2 - 4y_2^2\end{aligned}\)

Thus, the new quadratic form is \(6y_1^2 - 4y_2^2\).

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Most popular questions from this chapter

Orthogonally diagonalize the matrices in Exercises 13โ€“22, giving an orthogonal matrix\(P\)and a diagonal matrix\(D\). To save you time, the eigenvalues in Exercises 17โ€“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. \(\left( {\begin{aligned}{{}}{\,1}&{ - 5}\\{ - 5}&{\,\,1}\end{aligned}} \right)\)

Question 8: Use Exercise 7 to show that if A is positive definite, then A has a LU factorization, \(A = LU\), where U has positive pivots on its diagonal. (The converse is true, too).

Suppose\(A = PR{P^{ - {\bf{1}}}}\), where P is orthogonal and R is upper triangular. Show that if A is symmetric, then R is symmetric and hence is actually a diagonal matrix.

Question: In Exercises 17-22, determine which sets of vectors are orthonormal. If a set is only orthogonal, normalize the vectors to produce an orthonormal set.

18. \(\left( {\begin{array}{*{20}{c}}0\\1\\0\end{array}} \right),{\rm{ }}\left( {\begin{array}{*{20}{c}}0\\{ - 1}\\0\end{array}} \right)\)

Question: Let \({\bf{X}}\) denote a vector that varies over the columns of a \(p \times N\) matrix of observations, and let \(P\) be a \(p \times p\) orthogonal matrix. Show that the change of variable \({\bf{X}} = P{\bf{Y}}\) does not change the total variance of the data. (Hint: By Exercise 11, it suffices to show that \(tr\left( {{P^T}SP} \right) = tr\left( S \right)\). Use a property of the trace mentioned in Exercise 25 in Section 5.4.)

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