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Question: In Exercises 21 and 22, A, B, Pand Dare \(n \times n\) matrices. Mark each statement True or False. Justify each answer.(Study Theorem 5 and 6 and the examples in this section carefully before you try these exercises.)

  1. A is diagonalizable if \(A = PD{P^{ - {\bf{1}}}}\) for some matrix D and some invertible matrix P.
  2. If \({\mathbb{R}^n}\) has a basis of eigenvectors of A, then A is diagonziable.
  3. A is diagonlizable if and only if A has n eigenvalues, counting multiplicities.
  4. If A is diagonizable, then A is invertible.

Short Answer

Expert verified

a. The given statement is false.

b. The given statement is true.

c. The given statement is false.

d. The given statement is false.

Step by step solution

01

Find the answer for part (a)

If matrix A is diagonalizable, then \(A = PD{P^{ - 1}}\), where matrix D is diagonal, and matrix Pis invertible.

In the statement, it is not mentioned that Dis diagonalizable.

Therefore, the given statement is false.

02

Find the answer for part (b)

If \({\mathbb{R}^n}\) has a basis of eigenvectors, then matrix A is diagonalizable.

As \({\mathbb{R}^n}\) has a basis of eigenvectors, that there are enough eigenvectors for the basis and the basis \({\mathbb{R}^n}\) is known as eigenvector basis. Thus, A is diagonalizable.

Therefore, the given statement is true.

03

Find the answer for part (c)

For diagonalization, the matrix must be invertible.

Therefore, the given statement is false.

04

Find the answer for part (d)

According to the invertible matrix theorem, zero must not be the eigenvalue of an invertible matrix. But for a diagonalizable matrix, zero may or may not be the eigenvalue.

So, A will not be essentially invertible as it is diagonalizable.

Therefore, the given statement is false.

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Most popular questions from this chapter

Exercises 19–23 concern the polynomial \(p\left( t \right) = {a_{\bf{0}}} + {a_{\bf{1}}}t + ... + {a_{n - {\bf{1}}}}{t^{n - {\bf{1}}}} + {t^n}\) and \(n \times n\) matrix \({C_p}\) called the companion matrix of \(p\): \({C_p} = \left[ {\begin{aligned}{*{20}{c}}{\bf{0}}&{\bf{1}}&{\bf{0}}&{...}&{\bf{0}}\\{\bf{0}}&{\bf{0}}&{\bf{1}}&{}&{\bf{0}}\\:&{}&{}&{}&:\\{\bf{0}}&{\bf{0}}&{\bf{0}}&{}&{\bf{1}}\\{ - {a_{\bf{0}}}}&{ - {a_{\bf{1}}}}&{ - {a_{\bf{2}}}}&{...}&{ - {a_{n - {\bf{1}}}}}\end{aligned}} \right]\).

22. Let \(p\left( t \right) = {a_{\bf{0}}} + {a_{\bf{1}}}t + {a_{\bf{2}}}{t^{\bf{2}}} + {t^{\bf{3}}}\), and let \(\lambda \) be a zero of \(p\).

  1. Write the companion matrix for \(p\).
  2. Explain why \({\lambda ^{\bf{3}}} = - {a_{\bf{0}}} - {a_{\bf{1}}}\lambda - {a_{\bf{2}}}{\lambda ^{\bf{2}}}\), and show that \(\left( {{\bf{1}},\lambda ,{\lambda ^2}} \right)\) is an eigenvector of the companion matrix for \(p\).

Question: Construct a random integer-valued \(4 \times 4\) matrix \(A\).

  1. Reduce \(A\) to echelon form \(U\) with no row scaling, and use \(U\) in formula (1) (before Example 2) to compute \(\det A\). (If \(A\) happens to be singular, start over with a new random matrix.)
  2. Compute the eigenvalues of \(A\) and the product of these eigenvalues (as accurately as possible).
  3. List the matrix \(A\), and, to four decimal places, list the pivots in \(U\) and the eigenvalues of \(A\). Compute \(\det A\) with your matrix program, and compare it with the products you found in (a) and (b).


For the matrix A,find real closed formulas for the trajectory x(t+1)=Ax¯(t)wherex=[01]. Draw a rough sketchA=[-0.51.5-0.61.3]

Question: Find the characteristic polynomial and the eigenvalues of the matrices in Exercises 1-8.

5. \(\left[ {\begin{array}{*{20}{c}}2&1\\-1&4\end{array}} \right]\)

M] In Exercises 19 and 20, find (a) the largest eigenvalue and (b) the eigenvalue closest to zero. In each case, set \[{{\bf{x}}_{\bf{0}}}{\bf{ = }}\left( {{\bf{1,0,0,0}}} \right)\] and carry out approximations until the approximating sequence seems accurate to four decimal places. Include the approximate eigenvector.

19.\[A{\bf{=}}\left[{\begin{array}{*{20}{c}}{{\bf{10}}}&{\bf{7}}&{\bf{8}}&{\bf{7}}\\{\bf{7}}&{\bf{5}}&{\bf{6}}&{\bf{5}}\\{\bf{8}}&{\bf{6}}&{{\bf{10}}}&{\bf{9}}\\{\bf{7}}&{\bf{5}}&{\bf{9}}&{{\bf{10}}}\end{array}} \right]\]

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