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Question: Find an SVD of each matrix in Exercise 5-12. (Hint: In Exercise 11, one choice for U is \(\left( {\begin{array}{*{20}{c}}{ - \frac{{\bf{1}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}\\{\frac{{\bf{2}}}{{\bf{3}}}}&{ - \frac{{\bf{1}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}\\{\frac{{\bf{2}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}&{ - \frac{{\bf{1}}}{{\bf{3}}}}\end{array}} \right)\). In Exercise 12, one column of U can be \(\left( {\begin{array}{*{20}{c}}{\frac{{\bf{1}}}{{\sqrt {\bf{6}} }}}\\{ - \frac{{\bf{2}}}{{\sqrt {\bf{6}} }}}\\{\frac{{\bf{1}}}{{\sqrt {\bf{6}} }}}\end{array}} \right)\).)

10. \(\left( {\begin{array}{*{20}{c}}{\bf{3}}&{ - {\bf{3}}}\\{\bf{0}}&{\bf{0}}\\{\bf{1}}&{\bf{1}}\end{array}} \right)\)

Short Answer

Expert verified

The singular value decomposition of \(A\) is, \(A = \left( {\begin{array}{*{20}{c}}{ - 1}&0&1\\0&0&1\\0&1&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}{3\sqrt 2 }&0\\0&{\sqrt 2 }\\0&0\end{array}} \right)\left( {\begin{array}{*{20}{c}}{ - \frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\end{array}} \right)\).

Step by step solution

01

The Singular Value Decomposition

Consider \(m \times n\) matrix with rank \(r\) as \(A\). There is a \(m \times n\) matrix \(\sum \) as seen in (3) wherein the diagonal entries in \(D\) are the first \(r\) singular valuesof A, \({\sigma _1} \ge \cdots \ge {\sigma _r} > 0,\) and there arises an \(m \times m\) orthogonal matrix \(U\) and an \(n \times n\) orthogonal matrix \(V\) such that \(A = U\sum {V^T}\).

02

Find the eigenvalues of the given matrix

Let \(A = \left( {\begin{array}{*{20}{c}}3&{ - 3}\\0&0\\1&1\end{array}} \right)\). The transpose of A is:

\({A^T} = \left( {\begin{array}{*{20}{c}}3&0&1\\{ - 3}&0&1\end{array}} \right)\)

Find the product \({A^T}A\).

\(\begin{array}{c}{A^T}A = \left( {\begin{array}{*{20}{c}}3&0&1\\{ - 3}&0&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}3&{ - 3}\\0&0\\1&1\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{10}&{ - 8}\\{ - 8}&{10}\end{array}} \right)\end{array}\)

The characteristic equation for \({A^T}A\) is:

\(\begin{array}{c}\det \left| {{A^T}A - \lambda I} \right| = 0\\\left| {\begin{array}{*{20}{c}}{10 - \lambda }&{ - 8}\\{ - 8}&{10 - \lambda }\end{array}} \right| = 0\\{\left( {10 - \lambda } \right)^2} - 64 = 0\\\lambda = 18,\,\,2\end{array}\)

03

Find the eigenvectors of the given matrix

The eigenvectors of the matrix for \(\lambda = 18\) are:

\(\begin{array}{c}\left( {{A^T}A - \lambda I} \right)\left( {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\end{array}} \right) = 0\\\left( {\begin{array}{*{20}{c}}{ - 8}&{ - 8}\\{ - 8}&{ - 8}\end{array}} \right)\left( {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\end{array}} \right) = 0\end{array}\)

The general solution is:

\(\left( {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\end{array}} \right) = \,\left( {\begin{array}{*{20}{c}}{ - 1}\\1\end{array}} \right)\)

The eigenvectors of the matrix for \(\lambda = 10\) are:

\(\begin{array}{c}\left( {{A^T}A - \lambda I} \right)\left( {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\end{array}} \right) = 0\\\left( {\begin{array}{*{20}{c}}8&{ - 8}\\{ - 8}&8\end{array}} \right)\left( {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\end{array}} \right) = 0\end{array}\)

The general solution is:

\(\left( {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\end{array}} \right) = \,\left( {\begin{array}{*{20}{c}}1\\1\end{array}} \right)\)

04

Find the Eigenvector matrix

Normalize the eigenvector \(\left( {\begin{array}{*{20}{c}}{ - 1}\\1\end{array}} \right)\) is:

\(\begin{array}{c}{{\bf{v}}_1} = \frac{1}{{\sqrt {{{\left( { - 1} \right)}^2} + {1^2}} }}\left( {\begin{array}{*{20}{c}}{ - 1}\\1\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{\frac{{ - 1}}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\end{array}} \right)\end{array}\)

Normalize the eigenvector \(\left( {\begin{array}{*{20}{c}}1\\1\end{array}} \right)\) is:

\(\begin{array}{c}{{\bf{v}}_2} = \frac{1}{{\sqrt {{1^2} + {1^2}} }}\left( {\begin{array}{*{20}{c}}1\\1\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\end{array}} \right)\end{array}\)

The eigenvector matrix is:

\(\begin{array}{c}V = \left( {\begin{array}{*{20}{c}}{{{\bf{v}}_1}}&{{{\bf{v}}_2}}\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{ - \frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\end{array}} \right)\end{array}\)

05

Find the value of \(\Sigma \)

The singular value is the square root of eigenvalues of \({A^T}A\) i.e. \(3\sqrt 2 \) and \(\sqrt 2 \). Thus, the matrix \(\Sigma \) is:

\(\Sigma = \left( {\begin{array}{*{20}{c}}{3\sqrt 2 }&0\\0&{\sqrt 2 }\\0&0\end{array}} \right)\)

06

Find the matrix U

Let \(U = \left( {\begin{array}{*{20}{c}}a&b&c\\d&e&f\\g&h&i\end{array}} \right)\). Consider the following equation:

\(\begin{array}{c}U\Sigma = AV\\\left( {\begin{array}{*{20}{c}}a&b&c\\d&e&f\\g&h&i\end{array}} \right)\left( {\begin{array}{*{20}{c}}{3\sqrt 2 }&0\\0&{\sqrt 2 }\\0&0\end{array}} \right) = \left( {\begin{array}{*{20}{c}}3&{ - 3}\\0&0\\1&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}{ - \frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\end{array}} \right)\\\left( {\begin{array}{*{20}{c}}{3\sqrt 2 a}&{\sqrt 2 b}\\{3\sqrt 2 d}&{\sqrt 2 e}\\{3\sqrt 2 g}&{\sqrt 2 h}\end{array}} \right) = \left( {\begin{array}{*{20}{c}}{ - \frac{6}{{\sqrt 2 }}}&0\\0&0\\0&{\frac{2}{{\sqrt 2 }}}\end{array}} \right)\end{array}\)

So, the matrix \(U = \left( {\begin{array}{*{20}{c}}{ - 1}&0&1\\0&0&1\\0&1&1\end{array}} \right)\).

The SVD of A can be written as,

\(\begin{array}{c}A = U\Sigma {V^T}\\ = \left( {\begin{array}{*{20}{c}}{ - 1}&0&1\\0&0&1\\0&1&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}{3\sqrt 2 }&0\\0&{\sqrt 2 }\\0&0\end{array}} \right)\left( {\begin{array}{*{20}{c}}{ - \frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\end{array}} \right)\end{array}\)

Hence, the SVD of the given matrix is, \(A = \left( {\begin{array}{*{20}{c}}{ - 1}&0&1\\0&0&1\\0&1&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}{3\sqrt 2 }&0\\0&{\sqrt 2 }\\0&0\end{array}} \right)\left( {\begin{array}{*{20}{c}}{ - \frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}\end{array}} \right)\).

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Most popular questions from this chapter

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. \(3x_1^2 + 2{x_1}{x_2}\)

In Exercises 17โ€“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 \(m \times n\) โ€œdiagonalโ€ matrix with \(r\) positive entries and no negative entries, and \(V\) is an \(n \times n\) orthogonal matrix. Justify each answer.

20. Show that if\(A\)is an orthogonal\(m \times m\)matrix, then \(PA\) has the same singular values as \(A\).

Question 11: Prove that any \(n \times n\) matrix A admits a polar decomposition of the form \(A = PQ\), where P is a \(n \times n\) positive semidefinite matrix with the same rank as A and where Q is an \(n \times n\) orthogonal matrix. (Hint: Use a singular value decomposition, \(A = U\sum {V^T}\), and observe that \(A = \left( {U\sum {U^T}} \right)\left( {U{V^T}} \right)\).) This decomposition is used, for instance, in mechanical engineering to model the deformation of a material. The matrix P describe the stretching or compression of the material (in the directions of the eigenvectors of P), and Q describes the rotation of the material in space.

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: 3. Let A be an \(n \times n\) symmetric matrix of rank r. Explain why the spectral decomposition of A represents A as the sum of r rank 1 matrices.

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