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Expert-verifiedFound in: Page 395

Book edition
5th

Author(s)
David C. Lay, Steven R. Lay and Judi J. McDonald

Pages
483 pages

ISBN
978-03219822384

**Question: Mark Each statement True or False. Justify each answer. In each part, A represents an \(n \times n\) matrix.**

** **

**If***A*is orthogonally diagonizable, then*A*is symmetric.**If***A*is an orthogonal matrix, then*A*is symmetric.**If***A*is an orthogonal matrix, then \(\left\| {A{\bf{x}}} \right\| = \left\| {\bf{x}} \right\|\) for all x in \({\mathbb{R}^n}\).**The principal axes of a quadratic from \({{\bf{x}}^T}A{\bf{x}}\) can be the columns of any matrix***P*that diagonalizes*A*.**If***P*is an \(n \times n\) matrix with orthogonal columns, then \({P^T} = {P^{ - {\bf{1}}}}\).**If every coefficient in a quadratic form is positive, then the quadratic form is positive definite.****If \({{\bf{x}}^T}A{\bf{x}} > {\bf{0}}\) for some x, then the quadratic form \({{\bf{x}}^T}A{\bf{x}}\) is positive definite.****By a suitable change of variable, any quadratic form can be changed into one with no cross-product term.****The largest value of a quadratic form \({{\bf{x}}^T}A{\bf{x}}\), for \(\left\| {\bf{x}} \right\| = {\bf{1}}\) is the largest entery on the diagonal***A*.**The maximum value of a positive definite quadratic form \({{\bf{x}}^T}A{\bf{x}}\) is the greatest eigenvalue of***A*.**A positive definite quadratic form can be changed into a negative definite form by a suitable change of variable \({\bf{x}} = P{\bf{u}}\), for some orthogonal matrix***P*.**An indefinite quadratic form is one whose eigenvalues are not definite.****If***P*is an \(n \times n\) orthogonal matrix, then the change of variable \({\bf{x}} = P{\bf{u}}\) transforms \({{\bf{x}}^T}A{\bf{x}}\) into a quadratic form whose matrix is \({P^{ - {\bf{1}}}}AP\).**If***U*is \(m \times n\) with orthogonal columns, then \(U{U^T}{\bf{x}}\) is the orthogonal projection of x onto Col*U*.**If***B*is \(m \times n\) and x is a unit vector in \({\mathbb{R}^n}\), then \(\left\| {B{\bf{x}}} \right\| \le {\sigma _{\bf{1}}}\), where \({\sigma _{\bf{1}}}\) is the first singular value of*B*.**A singular value decomposition of an \(m \times n\) matrix***B*can be written as \(B = P\Sigma Q\), where*P*is an \(m \times n\) orthogonal matrix and \(\Sigma \) is an \(m \times n\) diagonal matrix.**If***A*is \(n \times n\), then*A*and \({A^T}A\) have the same singular values.

a. The statement is True.

b. The statement is False.

c. The statement is True.

d. The statement is False.

e. The statement is False.

f. The statement is False.

g. The statement is False.

h. The statement is True.

i. The statement is False.

j. The statement is False.

k. The statement is False

l. The statement is False.

m. The statement is True.

n. The statement is False.

o. The statement is True.

p. The statement is True.

q. The statement is False

According to theorem 2, an orthogonally diagonalizable matrix then *A* must be a symmetric matrix.

Thus, the statement is True.

Consider the matrix *A*, \(A = \left[ {\begin{array}{*{20}{c}}0&{ - 1}\\1&0\end{array}} \right]\).

Matrix *A* not symmetric, but it is orthogonal.

Thus, the given statement is False.

According to proof of Theorem 6, if *A* is diagonalized, then;

\[\left\| {A{\bf{x}}} \right\| = \left\| {\bf{x}} \right\|\]

Thus, the statement is True.

The principal axes of \({{\bf{x}}^T}A{\bf{x}}\) are represented by the orthogonal matrix *P*. The matrix *P* diagonalizes *A*.

Thus, the statement is True.

Let a matrix *P* be defined as:

\(P = \left[ {\begin{array}{*{20}{c}}1&{ - 1}\\1&1\end{array}} \right]\)

The columns of *P* are orthogonal but not orthonormal. Therefore, \({P^T} = {P^{ - 1}}\).

Thus, the given statement is False.

According to theorem 5, if all the eigenvalues of the coefficient matrix of a quadratic equation are positive, then the quadratic equation is a positive definite.

Therefore, the statement is false.

Consider the following matrix and vector:

\(A = \left[ {\begin{array}{*{20}{c}}2&0\\0&{ - 3}\end{array}} \right]\) and \(x = \left[ {\begin{array}{*{20}{c}}1\\0\end{array}} \right]\)

Find the product \({x^T}Ax\).

\(\begin{array}{c}{x^T}Ax = \left[ {\begin{array}{*{20}{c}}1&0\end{array}} \right]\left[ {\begin{array}{*{20}{c}}2&0\\0&{ - 3}\end{array}} \right]\left[ {\begin{array}{*{20}{c}}1\\0\end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}}1&0\end{array}} \right]\left[ {\begin{array}{*{20}{c}}2\\0\end{array}} \right]\\ = 2\\ > 0\end{array}\)

But according to theorem 6, \({x^T}Ax\) is an indefinite quadratic form.

Thus, the statement is False.

According to the principal axes theorem, a quadratic form can be represented by the expression \({x^T}Ax\), where *A* is a symmetric matrix.

Therefore, the statement is True.

From example 3 of section 7.3, it can be observed that if \(\left\| x \right\| = 1\), the largest eigenvalue of the quadratic equation \({{\bf{x}}^T}A{\bf{x}}\) is the largest eigenvalue of *A*.

Thus, the statement is False.

The maximum value of the quadratic expression \({{\bf{x}}^T}A{\bf{x}}\) can be made as large as possible, and it depends upon the set of unit vectors.

Thus, the statement is False.

If there is the orthogonal change for \({\bf{x}} = P{\bf{y}}\), then the positive definite quadratic changes into another positive quadratic.

Thus, the given statement is False.

As every square matrix has a characteristic equation, therefore its root, i.e., eigenvalues, always exist.

So, for matrix *A* the eigenvalues exist.

Thus, the statement is False.

If \({\bf{x}} = P{\bf{u}}\), then

\(\begin{array}{c}{{\bf{x}}^T}A{\bf{x}} = {\left( {P{\bf{y}}} \right)^T}A\left( {P{\bf{y}}} \right)\\ = {{\bf{y}}^T}{P^T}AP{\bf{y}}\\ = {{\bf{y}}^T}{P^{ - 1}}AP{\bf{y}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left( {{P^T} = {P^{ - 1}}} \right)\end{array}\)

Thus, the given statement is True.

Let the matrix \(U = \left[ {\begin{array}{*{20}{c}}1&{ - 1}\\1&{ - 1}\end{array}} \right]\).

\(U{U^T}{\bf{x}}\) represents the orthogonal projection of **x** onto Col*U*, if an only if *U* is orthonormal.

Thus, the given statement is False.

As \(\left\| {B{\bf{x}}} \right\|\) shows the length of vectors and \({\sigma _1}\) is the greatest singular value, then the inequality \[\left\| {B{\bf{x}}} \right\| \le {\sigma _1}\] is true.

Therefore, the statement is True.

According to theorem 10, a matrix \(m \times n\) matrix *A* can be expressed as\(U\Sigma {V^T}\), where *U* and *V *are orthogonal.

Hence, \({V^T}\) is also orthogonal.

Thus, the statement is True.

Consider the matrix:

\(A = \left[ {\begin{array}{*{20}{c}}2&0\\0&1\end{array}} \right]\)

The singular values for *A* are 2 and 1, and the singular values of product \({A^T}A\) are 4 and 1.

Thus, the given statement is True.

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