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Suppose \({\rm{x}}\)is a unit eigenvector of a matrix \(A\) corresponding to an eigenvalue 3. What is the value of \({{\rm{x}}^T}A{\rm{x}}\)?

Short Answer

Expert verified

The required value is: \({{\rm{x}}^T}A{\rm{x}} = 3\).

Step by step solution

01

Symmetric Matrices and Quadratic Forms 

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

\(\begin{array}{l}{{\rm{x}}^T}A{\rm{x}} = {{\rm{y}}^T}D{\rm{y}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {{\rm{as x}} = Py} \right\}\\\\\left\| {\rm{x}} \right\| = \left\| {P{\rm{y}}} \right\| = \left\| {\rm{y}} \right\|\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left\{ {\forall {\rm{y}} \in \mathbb{R}} \right\}\end{array}\)

02

Find the maximum value

As per the question, we have:

The matrix\(A\)has eigenvalue 3 with eigenvector\({\rm{x}}\)which is a unit vector, then:

\(\begin{array}{c}A{\rm{x}} = 3{\rm{x}}\\{{\rm{x}}^T}A{\rm{x}} = {{\rm{x}}^T}3{\rm{x}}\\ = 3\left( {{{\rm{x}}^T}{\rm{x}}} \right)\\ = 3{\left\| {\rm{x}} \right\|^2}\\ = 3\end{array}\)

Hence, the required value is \({{\rm{x}}^T}A{\rm{x}} = 3\).

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Most popular questions from this chapter

Orthogonally diagonalize the matrices in Exercises 13鈥22, giving an orthogonal matrix \(P\) and a diagonal matrix \(D\). To save you time, the eigenvalues in Exercises 17鈥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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Orthogonally diagonalize the matrices in Exercises 13鈥22, giving an orthogonal matrix\(P\)and a diagonal matrix\(D\). To save you time, the eigenvalues in Exercises 17鈥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.

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

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39. \(\left( {\begin{aligned}{{}}{.{\bf{31}}}&{.{\bf{58}}}&{.{\bf{08}}}&{.{\bf{44}}}\\{.{\bf{58}}}&{ - .{\bf{56}}}&{.{\bf{44}}}&{ - .{\bf{58}}}\\{.{\bf{08}}}&{.{\bf{44}}}&{.{\bf{19}}}&{ - .{\bf{08}}}\\{ - .{\bf{44}}}&{ - .{\bf{58}}}&{ - .{\bf{08}}}&{.{\bf{31}}}\end{aligned}} \right)\)

In Exercises 17鈥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 \(m \times n\) 鈥渄iagonal鈥 matrix with \(r\) positive entries and no negative entries, and \(V\) is an \(n \times n\) orthogonal matrix. Justify each answer.

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