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In Exercises 25 and 26, mark each statement True or False. Justify each answer.

26.

  1. There are symmetric matrices that are not orthogonally diagonizable.
  2. b. If \(B = PD{P^T}\), where \({P^T} = {P^{ - {\bf{1}}}}\) and D is a diagonal matrix, then B is a symmetric matrix.
  3. c. An orthogonal matrix is orthogonally diagonizable.
  4. d. The dimension of an eigenspace of a symmetric matrix is sometimes less than the multiplicity of the corresponding eigenvalue.

Short Answer

Expert verified

(a) The statement is False.

(b) The statement is True.

(c) The statement is False.

(d) The statement isFalse.

Step by step solution

01

Find an answer for part (a)

According to theorem 2, a matrix of order \(n \times n\) is orthogonally diagonalizable when the matrix is symmetric.

So, the statement is False.

02

Find an answer for part (b)

As \({P^T} = {P^{ - 1}}\), then P is an orthogonal matrix, so by the equation,\(B = PD{P^T}\) it shows thatB is orthogonally diagonalizable, and thus,B is a symmetric matrix.

So, the statement is True.

03

Find an answer for part (c)

An orthogonal matrix can be symmetric, but not every orthogonal matrix, but not all orthogonal matrix is symmetric.

Thus, the statement is False.

04

Find an answer for part (d)

According to theorem 3(b), the dimension of eigenspace is less than or equal to the multiplicity corresponding to the eigenvalue. But it can be less than the value of multiplicity of the corresponding eigenvalue for a symmetric matrix.

Thus, the statement is False.

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Most popular questions from this chapter

Compute the quadratic form \({{\bf{x}}^T}A{\bf{x}}\), when \(A = \left( {\begin{aligned}{{}}5&{\frac{1}{3}}\\{\frac{1}{3}}&1\end{aligned}} \right)\) and

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b. \({\bf{x}} = \left( {\begin{aligned}{{}}6\\1\end{aligned}} \right)\)

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Let B be an \(n \times n\) symmetric matrix such that \({B^{\bf{2}}} = B\). Any such matrix is called a projection matrix (or an orthogonal projection matrix.) Given any y in \({\mathbb{R}^n}\), let \({\bf{\hat y}} = B{\bf{y}}\)and\({\bf{z}} = {\bf{y}} - {\bf{\hat y}}\).

a) Show that z is orthogonal to \({\bf{\hat y}}\).

b) Let W be the column space of B. Show that y is the sum of a vector in W and a vector in \({W^ \bot }\). Why does this prove that By is the orthogonal projection of y onto the column space of B?

Suppose A is a symmetric \(n \times n\) matrix and B is any \(n \times m\) matrix. Show that \({B^T}AB\), \({B^T}B\), and \(B{B^T}\) are symmetric matrices.

Question: 13. The sample covariance matrix is a generalization of a formula for the variance of a sample of \(N\) scalar measurements, say \({t_1},................,{t_N}\). If \(m\) is the average of \({t_1},................,{t_N}\), then the sample variance is given by

\(\frac{1}{{N - 1}}\sum\limits_{k = 1}^n {{{\left( {{t_k} - m} \right)}^2}} \)

Show how the sample covariance matrix, \(S\), defined prior to Example 3, may be written in a form similar to (1). (Hint: Use partitioned matrix multiplication to write \(S\) as \(\frac{1}{{N - 1}}\) times the sum of \(N\) matrices of size \(p \times p\). For \(1 \le k \le N\), write \({X_k} - M\) in place of \({\hat X_k}\).)

Construct a spectral decomposition of A from Example 3.

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