Chapter 7: Q7.4-14E (page 395)
Question: In Exercise 7, find a unit vector x at which Ax has maximum length.
Short Answer
The unit vector is, \(\left( {\begin{array}{*{20}{c}}{\frac{2}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\end{array}} \right)\).
Chapter 7: Q7.4-14E (page 395)
Question: In Exercise 7, find a unit vector x at which Ax has maximum length.
The unit vector is, \(\left( {\begin{array}{*{20}{c}}{\frac{2}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\end{array}} \right)\).
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Get started for freeFind the matrix of the quadratic form. Assume x is in \({\mathbb{R}^{\bf{3}}}\).
a. \(3x_1^2 - 2x_2^2 + 5x_3^2 + 4{x_1}{x_2} - 6{x_1}{x_3}\)
b. \(4x_3^2 - 2{x_1}{x_2} + 4{x_2}{x_3}\)
Find the matrix of the quadratic form. Assume x is in \({\mathbb{R}^2}\) .
a. \(3x_1^2 - 4{x_1}{x_2} + 5x_2^2\) b. \(3x_1^2 + 2{x_1}{x_2}\)
(M) 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.
39. \(\left( {\begin{aligned}{{}}{.{\bf{31}}}&{.{\bf{58}}}&{.{\bf{08}}}&{.{\bf{44}}}\\{.{\bf{58}}}&{ - .{\bf{56}}}&{.{\bf{44}}}&{ - .{\bf{58}}}\\{.{\bf{08}}}&{.{\bf{44}}}&{.{\bf{19}}}&{ - .{\bf{08}}}\\{ - .{\bf{44}}}&{ - .{\bf{58}}}&{ - .{\bf{08}}}&{.{\bf{31}}}\end{aligned}} \right)\)
Question: 11. Given multivariate data \({X_1},................,{X_N}\) (in \({\mathbb{R}^p}\)) in mean deviation form, let \(P\) be a \(p \times p\) matrix, and define \({Y_k} = {P^T}{X_k}{\rm{ for }}k = 1,......,N\).
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.
23. Let \(U = \left( {{u_1}...{u_m}} \right)\) and \(V = \left( {{v_1}...{v_n}} \right)\) where the \({{\bf{u}}_i}\) and \({{\bf{v}}_i}\) are in Theorem 10. Show that \(A = {\sigma _1}{u_1}v_1^T + {\sigma _2}{u_2}v_2^T + ... + {\sigma _r}{u_r}v_r^T\).
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