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Compute the quadratic form \({{\bf{x}}^T}A{\bf{x}}\), when \(A = \left( {\begin{aligned}{{}}5&{\frac{1}{3}}\\{\frac{1}{3}}&1\end{aligned}} \right)\) and

a. \({\bf{x}} = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\)

b. \({\bf{x}} = \left( {\begin{aligned}{{}}6\\1\end{aligned}} \right)\)

c. \({\bf{x}} = \left( {\begin{aligned}{{}}1\\3\end{aligned}} \right)\)

Short Answer

Expert verified

a. For \({\bf{x}} = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\): \({{\bf{x}}^T}A{\bf{x}} = 5x_1^2 + \left( {\frac{2}{3}} \right){x_1}{x_2} + x_2^2\)

b.For \({\bf{x}} = \left( {\begin{aligned}{{}}6\\1\end{aligned}} \right)\): \({{\bf{x}}^T}A{\bf{x}} = 185\)

c.For \({\bf{x}} = \left( {\begin{aligned}{{}}1\\3\end{aligned}} \right)\): \({{\bf{x}}^T}A{\bf{x}} = 16\)

Step by step solution

01

Find \({x^T}Ax\)

(a)

The given matrix is\(A = \left( {\begin{aligned}{{}}5&{\frac{1}{3}}\\{\frac{1}{3}}&1\end{aligned}} \right)\), and the given vector is \({\bf{x}} = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\).

Find \({{\bf{x}}^T}A{\bf{x}}\).

\(\begin{aligned}{}{{\bf{x}}^T}A{\bf{x}} &= {\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)^T}\left( {\begin{aligned}{{}}5&{\frac{1}{3}}\\{\frac{1}{3}}&1\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{{}}{{x_1}}&{{x_2}}\end{aligned}} \right)\left( {\begin{aligned}{{}}5&{\frac{1}{3}}\\{\frac{1}{3}}&1\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{{}}{{x_1}}&{{x_2}}\end{aligned}} \right)\left( {\begin{aligned}{{}}{5{x_1} + \frac{1}{3}{x_2}}\\{\frac{1}{3}{x_1} + {x_2}}\end{aligned}} \right)\\ &= {x_1}\left( {5{x_1} + \frac{1}{3}{x_2}} \right) + {x_2}\left( {\frac{1}{3}{x_1} + {x_2}} \right)\\ &= 5x_1^2 + \left( {\frac{2}{3}} \right){x_1}{x_2} + x_2^2{\rm{ }}\left( 1 \right)\end{aligned}\)

Hence, the expression for \({{\bf{x}}^T}A{\bf{x}}\) is \(5x_1^2 + \left( {\frac{2}{3}} \right){x_1}{x_2} + x_2^2\).

02

Find \({x^T}Ax\) for \({\bf{x}} = \left( {\begin{aligned}{{}}6\\1\end{aligned}} \right)\)

(b)

As the expression for \({{\bf{x}}^T}A{\bf{x}}\) is \(5x_1^2 + \left( {\frac{2}{3}} \right){x_1}{x_2} + x_2^2\) when \({\bf{x}} = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\end{aligned}} \right)\).

So, for \({\bf{x}} = \left( {\begin{aligned}{{}}6\\1\end{aligned}} \right)\), simplify \(5x_1^2 + \left( {\frac{2}{3}} \right){x_1}{x_2} + x_2^2\) by substituting \({x_1} = 6,{x_2} = 1\).

\(\begin{aligned}{}{{\bf{x}}^T}A{\bf{x}} &= 5{\left( 6 \right)^2} + \left( {\frac{2}{3}} \right)\left( 6 \right)\left( 1 \right) + {\left( 1 \right)^2}\\ &= 185\end{aligned}\)

So, the value of \({{\bf{x}}^T}A{\bf{x}}\) is 185.

03

Find \({x^T}Ax\) for  \({\bf{x}} = \left( {\begin{aligned}{{}}1\\3\end{aligned}} \right)\)

(c)

For \({\bf{x}} = \left( {\begin{aligned}{{}}1\\3\end{aligned}} \right)\), simplify \(5x_1^2 + \left( {\frac{2}{3}} \right){x_1}{x_2} + x_2^2\) by substituting \({x_1} = 1,{x_2} = 3\).

\(\begin{aligned}{}{{\bf{x}}^T}A{\bf{x}} &= 5{\left( 1 \right)^2} + \left( {\frac{2}{3}} \right)\left( 1 \right)\left( 3 \right) + {\left( 3 \right)^2}\\ &= 16\end{aligned}\)

So, the value of \({{\bf{x}}^T}A{\bf{x}}\) is 16.

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Most popular questions from this chapter

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.

22. Show that if \(A\) is an \(n \times n\) positive definite matrix, then an orthogonal diagonalization \(A = PD{P^T}\) is a singular value decomposition of \(A\).

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: If A is \(m \times n\), then the matrix \(G = {A^T}A\) is called the Gram matrix of A. In this case, the entries of G are the inner products of the columns of A. (See Exercises 9 and 10).

9. Show that the Gram matrix of any matrix A is positive semidefinite, with the same rank as A. (See the Exercises in Section 6.5.)

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

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

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

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