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Compute the quadratic form \({x^T}Ax\), when \(A = \left( {\begin{aligned}{{}}3&2&0\\2&2&1\\0&1&0\end{aligned}} \right)\) and

a. \(x = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)\)

b. \(x = \left( {\begin{aligned}{{}}{ - 2}\\{ - 1}\\5\end{aligned}} \right)\)

c. \(x = \left( {\begin{aligned}{{}}{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\end{aligned}} \right)\)

Short Answer

Expert verified

a. For \({\bf{x}} = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)\): \({{\bf{x}}^T}A{\bf{x}} = 3x_1^2 + 2x_2^2 + 4{x_1}{x_2} + 2{x_2}{x_3}\)

b.For \({\bf{x}} = \left( {\begin{aligned}{{}}{ - 2}\\{ - 1}\\5\end{aligned}} \right)\): \({{\bf{x}}^T}A{\bf{x}} = 12\)

c.For \({\bf{x}} = \left( {\begin{aligned}{{}}{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\end{aligned}} \right)\): \({{\bf{x}}^T}A{\bf{x}} = \frac{{11}}{2}\)

Step by step solution

01

Find \({x^T}Ax\)

The given matrix is\(A = \left( {\begin{aligned}{{}}3&2&0\\2&2&1\\0&1&0\end{aligned}} \right)\), and the given vector is \({\bf{x}} = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)\).

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

\(\begin{aligned}{}{{\bf{x}}^T}A{\bf{x}} &= {\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)^T}\left( {\begin{aligned}{{}}3&2&0\\2&2&1\\0&1&0\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{{}}{{x_1}}&{{x_2}}&{{x_3}}\end{aligned}} \right)\left( {\begin{aligned}{{}}3&2&0\\2&2&1\\0&1&0\end{aligned}} \right)\left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)\\ &= \left( {\begin{aligned}{{}}{{x_1}}&{{x_2}}&{{x_3}}\end{aligned}} \right)\left( {\begin{aligned}{{}}{3{x_1} + 2{x_2}}\\{2{x_1} + 2{x_2} + {x_3}}\\{{x_2}}\end{aligned}} \right)\\ &= {x_1}\left( {3{x_1} + 2{x_2}} \right) + {x_2}\left( {2{x_1} + 2{x_2} + {x_3}} \right){x_3}{x_2}\\ &= 3x_1^2 + 2x_2^2 + 4{x_1}{x_2} + 2{x_2}{x_3}\end{aligned}\)

Hence, the expression for \({{\bf{x}}^T}A{\bf{x}}\) is \(3x_1^2 + 2x_2^2 + 4{x_1}{x_2} + 2{x_2}{x_3}\).

02

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

(b).

As expression for \({{\bf{x}}^T}A{\bf{x}}\) is \(3x_1^2 + 2x_2^2 + 4{x_1}{x_2} + 2{x_2}{x_3}\) when \({\bf{x}} = \left( {\begin{aligned}{{}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)\).

So\({\bf{x}} = \left( {\begin{aligned}{{}}{ - 2}\\{ - 1}\\5\end{aligned}} \right)\), simplify \(3x_1^2 + 2x_2^2 + 4{x_1}{x_2} + 2{x_2}{x_3}\) by substituting \({x_1} = - 2,{x_2} = - 1,{x_3} = 5\).

\(\begin{aligned}{}{{\bf{x}}^T}A{\bf{x}} &= 3{\left( { - 2} \right)^2} + 2{\left( { - 1} \right)^2} + 4\left( { - 2} \right)\left( { - 1} \right) + 2\left( { - 1} \right)\left( 5 \right)\\ &= 12\end{aligned}\)

So, the value of \({{\bf{x}}^T}A{\bf{x}}\) is 12.

03

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

(c)

For \({\bf{x}} = \left( {\begin{aligned}{{}}{{1 \mathord{\left/ {\vphantom {1 {\sqrt 2 }}} \right.} {\sqrt 2 }}}\\{{1 \mathord{\left/ {\vphantom {1 {\sqrt 2 }}} \right.} {\sqrt 2 }}}\\{{1 \mathord{\left/ {\vphantom {1 {\sqrt 2 }}} \right.} {\sqrt 2 }}}\end{aligned}} \right)\), simplify \(3x_1^2 + 2x_2^2 + 4{x_1}{x_2} + 2{x_2}{x_3}\) by substituting \({x_1} = \frac{1}{{\sqrt 2 }},{x_2} = \frac{1}{{\sqrt 2 }},{x_3} = \frac{1}{{\sqrt 2 }}\).

\(\begin{aligned}{}{{\bf{x}}^T}A{\bf{x}} &= 3{\left( {\frac{1}{{\sqrt 2 }}} \right)^2} + 2{\left( {\frac{1}{{\sqrt 2 }}} \right)^2} + 4\left( {\frac{1}{{\sqrt 2 }}} \right)\left( {\frac{1}{{\sqrt 2 }}} \right) + 2\left( {\frac{1}{{\sqrt 2 }}} \right)\left( {\frac{1}{{\sqrt 2 }}} \right)\\ &= \frac{{11}}{2}\end{aligned}\)

So, the value of \({{\bf{x}}^T}A{\bf{x}}\) is \(\frac{{11}}{2}\).

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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.

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

Question: 14. 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 \({A^ + } = {U_r}{D^{ - 1}}{V_r}^T\).

Given any \({\rm{b}}\) in \({\mathbb{R}^m}\), adapt Exercise 13 to show that \({A^ + }{\rm{b}}\) is the least-squares solution of minimum length. [Hint: Consider the equation \(A{\rm{x}} = {\rm{b}}\), where \(\mathop {\rm{b}}\limits^\^ \) is the orthogonal projection of \({\rm{b}}\) onto Col \(A\).

Let \(A = \left( {\begin{aligned}{{}}{\,\,\,4}&{ - 1}&{ - 1}\\{ - 1}&{\,\,\,4}&{ - 1}\\{ - 1}&{ - 1}&{\,\,\,4}\end{aligned}} \right)\), and\({\rm{v}} = \left( {\begin{aligned}{{}}1\\1\\1\end{aligned}} \right)\). Verify that 5 is an eigenvalue of \(A\) and \({\rm{v}}\)is an eigenvector. Then orthogonally diagonalize \(A\).

In Exercises 3-6, find (a) the maximum value of \(Q\left( {\rm{x}} \right)\) subject to the constraint \({{\rm{x}}^T}{\rm{x}} = 1\), (b) a unit vector \({\rm{u}}\) where this maximum is attained, and (c) the maximum of \(Q\left( {\rm{x}} \right)\) subject to the constraints \({{\rm{x}}^T}{\rm{x}} = 1{\rm{ and }}{{\rm{x}}^T}{\rm{u}} = 0\).

4. \(Q\left( x \right) = 3x_1^2 + 3x_2^2 + 5x_3^2 + 6x_1^{}x_2^{} + 2x_1^{}x_3^{} + 2x_2^{}x_3^{}\).

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

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