Difference between revisions of "031 Review Problems"

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'''12.''' Consider the matrix &nbsp;<math>A=     
+
'''12.''' Consider the matrix &nbsp;<math style="vertical-align: -31px">A=     
 
     \begin{bmatrix}
 
     \begin{bmatrix}
 
           1 & -4 & 9 & -7 \\
 
           1 & -4 & 9 & -7 \\
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         \end{bmatrix}.</math>       
 
         \end{bmatrix}.</math>       
 
      
 
      
(a) List rank &nbsp;<math>A</math>&nbsp; and &nbsp;<math>\text{dim Nul }A.</math>
+
(a) List rank &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">\text{dim Nul }A.</math>
  
(b) Find bases for &nbsp;<math>\text{Col }A</math>&nbsp; and &nbsp;<math>\text{Nul }A.</math>&nbsp; Find an example of a nonzero vector that belongs to &nbsp;<math>\text{Col }A,</math>&nbsp; as well as an example of a nonzero vector that belongs to &nbsp;<math>\text{Nul }A.</math>
+
(b) Find bases for &nbsp;<math style="vertical-align: 0px">\text{Col }A</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">\text{Nul }A.</math>&nbsp; Find an example of a nonzero vector that belongs to &nbsp;<math style="vertical-align: -5px">\text{Col }A,</math>&nbsp; as well as an example of a nonzero vector that belongs to &nbsp;<math style="vertical-align: 0px">\text{Nul }A.</math>
  
 
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
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  </math>
 
  </math>
 
    
 
    
(a) Is &nbsp;<math>B</math>&nbsp; invertible? Explain.
+
(a) Is &nbsp;<math style="vertical-align: 0px">B</math>&nbsp; invertible? Explain.
  
(b) Define a linear transformation &nbsp;<math>T</math>&nbsp; by the formula &nbsp;<math>T(\vec{x})=B\vec{x}.</math>&nbsp; Is &nbsp;<math>T</math>&nbsp; onto? Explain.
+
(b) Define a linear transformation &nbsp;<math style="vertical-align: 0px">T</math>&nbsp; by the formula &nbsp;<math style="vertical-align: -5px">T(\vec{x})=B\vec{x}.</math>&nbsp; Is &nbsp;<math style="vertical-align: 0px">T</math>&nbsp; onto? Explain.
  
 
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'''15.''' Suppose &nbsp;<math>T</math>&nbsp; is a linear transformation given by the formula  
+
'''15.''' Suppose &nbsp;<math style="vertical-align: 0px">T</math>&nbsp; is a linear transformation given by the formula  
  
 
::<math>T\Bigg(
 
::<math>T\Bigg(
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         \end{bmatrix}</math>
 
         \end{bmatrix}</math>
 
          
 
          
(a) Find the standard matrix for &nbsp;<math>T.</math>
+
(a) Find the standard matrix for &nbsp;<math style="vertical-align: 0px">T.</math>
 
          
 
          
(b) Let &nbsp;<math>\vec{u}=7\vec{e_1}-4\vec{e_2}.</math>&nbsp; Find &nbsp;<math>T(\vec{u}).</math>
+
(b) Let &nbsp;<math style="vertical-align: -5px">\vec{u}=7\vec{e_1}-4\vec{e_2}.</math>&nbsp; Find &nbsp;<math style="vertical-align: -6px">T(\vec{u}).</math>
 
          
 
          
(c) Is &nbsp;<math>\begin{bmatrix}
+
(c) Is &nbsp;<math style="vertical-align: -21px">\begin{bmatrix}
 
           -1 \\
 
           -1 \\
 
           3  
 
           3  
         \end{bmatrix}</math>&nbsp; in the range of &nbsp;<math>T?</math>&nbsp; Explain.
+
         \end{bmatrix}</math>&nbsp; in the range of &nbsp;<math style="vertical-align: 0px">T?</math>&nbsp; Explain.
  
 
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|}
 
|}
  
'''16.''' Let &nbsp;<math>A</math>&nbsp; and &nbsp;<math>B</math>&nbsp; be &nbsp;<math>6\times 6</math>&nbsp; matrices with &nbsp;<math>\text{det }A=-10</math>&nbsp; and &nbsp;<math>\text{det }B=5.</math>&nbsp; Use properties of determinants to compute:
+
'''16.''' Let &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">B</math>&nbsp; be &nbsp;<math style="vertical-align: 0px">6\times 6</math>&nbsp; matrices with &nbsp;<math style="vertical-align: -1px">\text{det }A=-10</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">\text{det }B=5.</math>&nbsp; Use properties of determinants to compute:
  
(a) &nbsp;<math>\text{det }3A</math>
+
(a) &nbsp;<math style="vertical-align: -2px">\text{det }3A</math>
  
(b) &nbsp;<math>\text{det }(A^TB^{-1})</math>
+
(b) &nbsp;<math style="vertical-align: -7px">\text{det }(A^TB^{-1})</math>
  
 
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|}
 
|}
  
'''17.''' Let &nbsp;<math>A=     
+
'''17.''' Let &nbsp;<math style="vertical-align: -20px">A=     
 
     \begin{bmatrix}
 
     \begin{bmatrix}
 
           5 & 1 \\
 
           5 & 1 \\
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         \end{bmatrix}.</math>
 
         \end{bmatrix}.</math>
  
(a) Find a basis for the eigenspace(s) of &nbsp;<math>A.</math>
+
(a) Find a basis for the eigenspace(s) of &nbsp;<math style="vertical-align: 0px">A.</math>
  
(b) Is the matrix &nbsp;<math>A</math>&nbsp; diagonalizable? Explain.
+
(b) Is the matrix &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; diagonalizable? Explain.
  
 
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
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         \end{bmatrix}.</math>
 
         \end{bmatrix}.</math>
 
          
 
          
(a) Find a unit vector in the direction of &nbsp;<math>\vec{v}.</math>
+
(a) Find a unit vector in the direction of &nbsp;<math style="vertical-align: 0px">\vec{v}.</math>
 
          
 
          
(b) Find the distance between &nbsp;<math>\vec{v}</math>&nbsp; and &nbsp;<math>\vec{y}.</math>
+
(b) Find the distance between &nbsp;<math style="vertical-align: 0px">\vec{v}</math>&nbsp; and &nbsp;<math style="vertical-align: -3px">\vec{y}.</math>
 
          
 
          
(c) Let &nbsp;<math>L=\text{Span }\{\vec{v}\}.</math>&nbsp; Compute the orthogonal projection of &nbsp;<math>\vec{y}</math>&nbsp; onto &nbsp;<math>L.</math>
+
(c) Let &nbsp;<math style="vertical-align: -5px">L=\text{Span }\{\vec{v}\}.</math>&nbsp; Compute the orthogonal projection of &nbsp;<math style="vertical-align: -3px">\vec{y}</math>&nbsp; onto &nbsp;<math style="vertical-align: 0px">L.</math>
  
 
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
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           4 \\
 
           4 \\
 
           0
 
           0
         \end{bmatrix}</math>&nbsp; in &nbsp;<math>W^\perp?</math>&nbsp; Explain.
+
         \end{bmatrix}</math>&nbsp; in &nbsp;<math style="vertical-align: 0px">W^\perp?</math>&nbsp; Explain.
  
 
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'''20.'''  
 
'''20.'''  
  
(a) Let &nbsp;<math>T:\mathbb{R}^2\rightarrow \mathbb{R}^2</math>&nbsp; be a transformation given by  
+
(a) Let &nbsp;<math style="vertical-align: -2px">T:\mathbb{R}^2\rightarrow \mathbb{R}^2</math>&nbsp; be a transformation given by  
  
 
::<math>T\bigg(
 
::<math>T\bigg(
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         \end{bmatrix}.</math>
 
         \end{bmatrix}.</math>
  
Determine whether &nbsp;<math>T</math>&nbsp; is a linear transformation. Explain.
+
Determine whether &nbsp;<math style="vertical-align: 0px">T</math>&nbsp; is a linear transformation. Explain.
  
(b) Let &nbsp;<math>A=     
+
(b) Let &nbsp;<math style="vertical-align: -19px">A=     
 
     \begin{bmatrix}
 
     \begin{bmatrix}
 
           1 & -3 & 0 \\
 
           1 & -3 & 0 \\
 
           -4 & 1 &1
 
           -4 & 1 &1
         \end{bmatrix}</math>&nbsp; and &nbsp;<math>B=     
+
         \end{bmatrix}</math>&nbsp; and &nbsp;<math style="vertical-align: -32px">B=     
 
     \begin{bmatrix}
 
     \begin{bmatrix}
 
           2 & 1\\
 
           2 & 1\\
 
           1 & 0 \\
 
           1 & 0 \\
 
           -1 & 1
 
           -1 & 1
         \end{bmatrix}.</math>&nbsp; Find &nbsp;<math>AB,</math>&nbsp; &nbsp;<math>BA^T</math>&nbsp; and &nbsp;<math>A-B^T.</math>
+
         \end{bmatrix}.</math>&nbsp; Find &nbsp;<math style="vertical-align: -4px">AB,~BA^T</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">A-B^T.</math>
  
 
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|}
 
|}
  
'''21.''' Let &nbsp;<math>A=     
+
'''21.''' Let &nbsp;<math style="vertical-align: -31px">A=     
 
     \begin{bmatrix}
 
     \begin{bmatrix}
 
           1 & 3 & 8 \\
 
           1 & 3 & 8 \\
 
           2 & 4 &11\\
 
           2 & 4 &11\\
 
           1 & 2 & 5
 
           1 & 2 & 5
         \end{bmatrix}.</math>&nbsp; Find &nbsp;<math>A^{-1}</math>&nbsp; if possible.
+
         \end{bmatrix}.</math>&nbsp; Find &nbsp;<math style="vertical-align: 0px">A^{-1}</math>&nbsp; if possible.
  
 
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'''23.'''  
 
'''23.'''  
  
(a) Show that if &nbsp;<math>\vec{x}</math>&nbsp; is an eigenvector of the matrix &nbsp;<math>A</math>&nbsp; corresponding to the eigenvalue 2, then &nbsp;<math>\vec{x}</math>&nbsp; is an eigenvector of &nbsp;<math>A^3-A^2+I.</math>&nbsp; What is the corresponding eigenvalue?
+
(a) Show that if &nbsp;<math style="vertical-align: 0px">\vec{x}</math>&nbsp; is an eigenvector of the matrix &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; corresponding to the eigenvalue 2, then &nbsp;<math style="vertical-align: 0px">\vec{x}</math>&nbsp; is an eigenvector of &nbsp;<math style="vertical-align: -2px">A^3-A^2+I.</math>&nbsp; What is the corresponding eigenvalue?
  
(b) Show that if &nbsp;<math>\vec{y}</math>&nbsp; is an eigenvector of the matrix &nbsp;<math>A</math>&nbsp; corresponding to the eigenvalue 3 and &nbsp;<math>A</math>&nbsp; is invertible, then &nbsp;<math>\vec{y}</math>&nbsp; is an eigenvector of &nbsp;<math>A^{-1}.</math>&nbsp; What is the corresponding eigenvalue?
+
(b) Show that if &nbsp;<math style="vertical-align: -3px">\vec{y}</math>&nbsp; is an eigenvector of the matrix &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; corresponding to the eigenvalue 3 and &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; is invertible, then &nbsp;<math style="vertical-align: -3px">\vec{y}</math>&nbsp; is an eigenvector of &nbsp;<math style="vertical-align: 0px">A^{-1}.</math>&nbsp; What is the corresponding eigenvalue?
  
 
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         \end{bmatrix}.</math>  
 
         \end{bmatrix}.</math>  
  
Use the Diagonalization Theorem to find the eigenvalues of &nbsp;<math>A</math>&nbsp; and a basis for each eigenspace.
+
Use the Diagonalization Theorem to find the eigenvalues of &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; and a basis for each eigenspace.
  
 
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|}
 
|}
  
'''25.''' Give an example of a &nbsp;<math>3\times 3</math>&nbsp; matrix &nbsp;<math>A</math>&nbsp; with eigenvalues 5,-1 and 3.
+
'''25.''' Give an example of a &nbsp;<math style="vertical-align: 0px">3\times 3</math>&nbsp; matrix &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; with eigenvalues 5,-1 and 3.
  
 
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|}
 
|}
  
'''26.''' Assume &nbsp;<math>A^2=I.</math>&nbsp; Find &nbsp;<math>\text{Nul }A.</math>
+
'''26.''' Assume &nbsp;<math style="vertical-align: 0px">A^2=I.</math>&nbsp; Find &nbsp;<math style="vertical-align: -1px">\text{Nul }A.</math>
  
 
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|}
 
|}
  
'''27.''' If &nbsp;<math>A</math>&nbsp; is an &nbsp;<math>n\times n</math>&nbsp; matrix such that &nbsp;<math>AA^T=I,</math>&nbsp; what are the possible values of &nbsp;<math>\text{det }A?</math>
+
'''27.''' If &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; is an &nbsp;<math style="vertical-align: 0px">n\times n</math>&nbsp; matrix such that &nbsp;<math style="vertical-align: -4px">AA^T=I,</math>&nbsp; what are the possible values of &nbsp;<math style="vertical-align: 0px">\text{det }A?</math>
  
 
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|}
 
|}
  
'''28.''' Show that if &nbsp;<math>\vec{x}</math>&nbsp; is an eigenvector of the matrix product &nbsp;<math>AB</math>&nbsp; and &nbsp;<math>B\vec{x}\ne \vec{0},</math>&nbsp; then &nbsp;<math>B\vec{x}</math>&nbsp; is an eigenvector of &nbsp;<math>BA.</math>
+
'''28.''' Show that if &nbsp;<math style="vertical-align: 0px">\vec{x}</math>&nbsp; is an eigenvector of the matrix product &nbsp;<math style="vertical-align: 0px">AB</math>&nbsp; and &nbsp;<math style="vertical-align: -5px">B\vec{x}\ne \vec{0},</math>&nbsp; then &nbsp;<math style="vertical-align: 0px">B\vec{x}</math>&nbsp; is an eigenvector of &nbsp;<math style="vertical-align: 0px">BA.</math>
  
 
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
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'''29.'''  
 
'''29.'''  
  
(a) Suppose a &nbsp;<math>6\times 8</math>&nbsp; matrix &nbsp;<math>A</math>&nbsp; has 4 pivot columns. What is &nbsp;<math>\text{dim Nul }A?</math>&nbsp; Is &nbsp;<math>\text{Col }A=\mathbb{R}^4?</math>&nbsp; Why or why not?
+
(a) Suppose a &nbsp;<math style="vertical-align: 0px">6\times 8</math>&nbsp; matrix &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; has 4 pivot columns. What is &nbsp;<math style="vertical-align: -1px">\text{dim Nul }A?</math>&nbsp; Is &nbsp;<math style="vertical-align: -1px">\text{Col }A=\mathbb{R}^4?</math>&nbsp; Why or why not?
  
(b) If &nbsp;<math>A</math>&nbsp; is a &nbsp;<math>7\times 5</math>&nbsp; matrix, what is the smallest possible dimension of &nbsp;<math>\text{Nul }A?</math>
+
(b) If &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; is a &nbsp;<math style="vertical-align: 0px">7\times 5</math>&nbsp; matrix, what is the smallest possible dimension of &nbsp;<math style="vertical-align: -1px">\text{Nul }A?</math>
  
 
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
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::<math>3x_1+5x_2=2k</math>
 
::<math>3x_1+5x_2=2k</math>
  
Find all real values of &nbsp;<math>k</math>&nbsp; such that the system has only one solution.
+
Find all real values of &nbsp;<math style="vertical-align: 0px">k</math>&nbsp; such that the system has only one solution.
  
 
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
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|}
 
|}
  
'''31.''' Suppose &nbsp;<math>\{\vec{u},\vec{v}\}</math>&nbsp; is a basis of the eigenspace corresponding to the eigenvalue 0 of a &nbsp;<math>5\times 5</math>&nbsp; matrix &nbsp;<math>A.</math>
+
'''31.''' Suppose &nbsp;<math style="vertical-align: -5px">\{\vec{u},\vec{v}\}</math>&nbsp; is a basis of the eigenspace corresponding to the eigenvalue 0 of a &nbsp;<math style="vertical-align: 0px">5\times 5</math>&nbsp; matrix &nbsp;<math style="vertical-align: 0px">A.</math>
  
(a) Is &nbsp;<math>\vec{w}=\vec{u}-2\vec{v}</math>&nbsp; an eigenvector of &nbsp;<math>A?</math>&nbsp; If so, find the corresponding eigenvalue.  
+
(a) Is &nbsp;<math style="vertical-align: 0px">\vec{w}=\vec{u}-2\vec{v}</math>&nbsp; an eigenvector of &nbsp;<math style="vertical-align: 0px">A?</math>&nbsp; If so, find the corresponding eigenvalue.  
  
 
If not, explain why.
 
If not, explain why.
  
(b) Find the dimension of &nbsp;<math>\text{Col }A.</math>
+
(b) Find the dimension of &nbsp;<math style="vertical-align: -1px">\text{Col }A.</math>
  
 
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"

Latest revision as of 13:11, 25 August 2017

This is a list of sample problems and is meant to represent the material usually covered in Math 31. An actual test may or may not be similar.


1. True or false: If all the entries of a    matrix    are    then    must be  

Solution:  
Final Answer:  

2. True or false: If a matrix    is diagonalizable, then the matrix    must be diagonalizable as well.

Solution:  
Final Answer:  

3. True or false: If    is a    matrix with characteristic equation    then    is diagonalizable.

Solution:  
Final Answer:  

4. True or false: If    is invertible, then    is diagonalizable.

Solution:  
Final Answer:  

5. True or false: If    and    are invertible    matrices, then so is  

Solution:  
Final Answer:  

6. True or false: If    is a    matrix and    then    is consistent for all    in  

Solution:  
Final Answer:  

7. True or false: Let    for    matrices    and    If    is invertible, then    is invertible.

Solution:  
Final Answer:  

8. True or false: Let    be a subspace of    and    be a vector in    If    and    then  

Solution:  
Final Answer:  

9. True or false: If    is an invertible    matrix, and    and    are    matrices such that    then  

Solution:  
Final Answer:  

10.

(a) Is the matrix    diagonalizable? If so, explain why and diagonalize it. If not, explain why not.

(b) Is the matrix    diagonalizable? If so, explain why and diagonalize it. If not, explain why not.

Solution:  
Final Answer:  

11. Find the eigenvalues and eigenvectors of the matrix  

Solution:  
Final Answer:  

12. Consider the matrix    and assume that it is row equivalent to the matrix

(a) List rank    and  

(b) Find bases for    and    Find an example of a nonzero vector that belongs to    as well as an example of a nonzero vector that belongs to  

Solution:  
Final Answer:  

13. Find the dimension of the subspace spanned by the given vectors. Are these vectors linearly independent?

Solution:  
Final Answer:  

14. Let  

(a) Is    invertible? Explain.

(b) Define a linear transformation    by the formula    Is    onto? Explain.

Solution:  
Final Answer:  

15. Suppose    is a linear transformation given by the formula

(a) Find the standard matrix for  

(b) Let    Find  

(c) Is    in the range of    Explain.

Solution:  
Final Answer:  

16. Let    and    be    matrices with    and    Use properties of determinants to compute:

(a)  

(b)  

Solution:  
Final Answer:  

17. Let  

(a) Find a basis for the eigenspace(s) of  

(b) Is the matrix    diagonalizable? Explain.

Solution:  
Final Answer:  

18. Let    and  

(a) Find a unit vector in the direction of  

(b) Find the distance between    and  

(c) Let    Compute the orthogonal projection of    onto  

Solution:  
Final Answer:  

19. Let    Is    in    Explain.

Solution:  
Final Answer:  

20.

(a) Let    be a transformation given by

Determine whether    is a linear transformation. Explain.

(b) Let    and    Find    and  

Solution:  
Final Answer:  

21. Let    Find    if possible.

Solution:  
Final Answer:  

22. Find a formula for    by diagonalizing the matrix.

Solution:  
Final Answer:  

23.

(a) Show that if    is an eigenvector of the matrix    corresponding to the eigenvalue 2, then    is an eigenvector of    What is the corresponding eigenvalue?

(b) Show that if    is an eigenvector of the matrix    corresponding to the eigenvalue 3 and    is invertible, then    is an eigenvector of    What is the corresponding eigenvalue?

Solution:  
Final Answer:  

24. Let  

Use the Diagonalization Theorem to find the eigenvalues of    and a basis for each eigenspace.

Solution:  
Final Answer:  

25. Give an example of a    matrix    with eigenvalues 5,-1 and 3.

Solution:  
Final Answer:  

26. Assume    Find  

Solution:  
Final Answer:  

27. If    is an    matrix such that    what are the possible values of  

Solution:  
Final Answer:  

28. Show that if    is an eigenvector of the matrix product    and    then    is an eigenvector of  

Solution:  
Final Answer:  

29.

(a) Suppose a    matrix    has 4 pivot columns. What is    Is    Why or why not?

(b) If    is a    matrix, what is the smallest possible dimension of  

Solution:  
Final Answer:  

30. Consider the following system of equations.

Find all real values of    such that the system has only one solution.

Solution:  
Final Answer:  

31. Suppose    is a basis of the eigenspace corresponding to the eigenvalue 0 of a    matrix  

(a) Is    an eigenvector of    If so, find the corresponding eigenvalue.

If not, explain why.

(b) Find the dimension of  

Solution:  
Final Answer: