Difference between revisions of "031 Review Part 3, Problem 11"

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(Created page with "<span class="exam">Consider the matrix  <math style="vertical-align: -31px">A= \begin{bmatrix} 1 & -4 & 9 & -7 \\ -1 & 2 & -4 & 1 \\...")
 
 
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<span class="exam">Consider the matrix &nbsp;<math style="vertical-align: -31px">A=   
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<span class="exam">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>
    \begin{bmatrix}
 
          1 & -4 & 9 & -7 \\
 
          -1 & & -4 & 1 \\
 
          5 & -6 & 10 & 7
 
        \end{bmatrix}</math>&nbsp; and assume that it is row equivalent to the matrix  
 
  
::<math>B=   
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<span class="exam">(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.
    \begin{bmatrix}
 
          1 & 0 & -1 & 5 \\
 
          0 & -2  & 5 & -6 \\
 
          0 & 0 & 0 & 0
 
        \end{bmatrix}.</math>     
 
   
 
<span class="exam">(a) List rank &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">\text{dim Nul }A.</math>
 
  
<span class="exam">(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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<span class="exam">If not, explain why.
  
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<span class="exam">(b) Find the dimension of &nbsp;<math style="vertical-align: -1px">\text{Col }A.</math>
  
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
!Foundations: &nbsp;  
 
!Foundations: &nbsp;  
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|-
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|'''1.''' An eigenvector &nbsp;<math style="vertical-align: 0px">\vec{x}</math>&nbsp; of a matrix &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; corresponding to the eigenvalue &nbsp;<math style="vertical-align: 0px">\lambda</math>&nbsp; is a nonzero vector such that
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|-
 +
|
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::<math>A\vec{x}=\lambda\vec{x}.</math>
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|-
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|'''2.''' By the Rank Theorem, if &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; is a &nbsp;<math style="vertical-align: 0px">m\times n</math>&nbsp; matrix, then
 
|-
 
|-
 
|
 
|
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::<math>\text{rank }A+\text{dim Col }A=n.</math>
 
|}
 
|}
  
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
!Step 1: &nbsp;  
 
!Step 1: &nbsp;  
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|-
 +
|First, notice
 +
|-
 +
|
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::<math>\vec{w}\ne \vec{0}</math>
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|-
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|since &nbsp;<math style="vertical-align: -5px">\{\vec{u},\vec{v}\}</math>&nbsp; is a basis of the eigenspace corresponding to the eigenvalue 0 of &nbsp;<math style="vertical-align: 0px">A.</math>
 +
|-
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|Also, we have
 
|-
 
|-
 
|
 
|
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::<math style="vertical-align: -1px">A\vec{u}=\vec{0}</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">A\vec{v}=\vec{0}</math>
 +
|-
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|since &nbsp;<math style="vertical-align: 0px">\vec{u}</math>&nbsp; and &nbsp;<math style="vertical-align: 0px">\vec{v}</math>&nbsp; are eigenvectors of &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; corresponding to the eigenvalue 0.
 
|}
 
|}
  
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
!Step 2: &nbsp;
 
!Step 2: &nbsp;
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|-
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|Now, we have
 
|-
 
|-
 
|
 
|
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&nbsp; &nbsp; &nbsp; &nbsp;<math>\begin{array}{rcl}
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\displaystyle{A\vec{w}} & = & \displaystyle{A(\vec{u}-2\vec{v})}\\
 +
&&\\
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& = & \displaystyle{A\vec{u}-2A\vec{v}}\\
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&&\\
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& = & \displaystyle{\vec{0}-2\cdot \vec{0}}\\
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&&\\
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& = & \displaystyle{\vec{0}.}
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\end{array}</math>
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|-
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|Hence, &nbsp;<math style="vertical-align: 0px">\vec{w}</math>&nbsp; is an eigenvector of &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; corresponding to the eigenvalue &nbsp;<math style="vertical-align: 0px">0.</math>
 
|}
 
|}
  
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{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
!Step 1: &nbsp;  
 
!Step 1: &nbsp;  
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|-
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|Since &nbsp;<math style="vertical-align: -5px">\{\vec{u},\vec{v}\}</math>&nbsp; is a basis for the eigenspace of &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; corresponding to the eigenvalue 0, we know that
 
|-
 
|-
 
|
 
|
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::<math>\text{dim Nul }A=2.</math>
 
|}
 
|}
  
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
{| class="mw-collapsible mw-collapsed" style = "text-align:left;"
 
!Step 2: &nbsp;
 
!Step 2: &nbsp;
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|-
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|Then, by the Rank Theorem, we have
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|-
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|&nbsp; &nbsp; &nbsp; &nbsp;<math>\begin{array}{rcl}
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\displaystyle{5} & = & \displaystyle{\text{dim Col }A+\text{dim Nul }A}\\
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&&\\
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& = & \displaystyle{\text{dim Col }A+2.}
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\end{array}</math>
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|-
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|Hence, we have
 
|-
 
|-
 
|
 
|
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::<math>\text{dim Col }A=3.</math>
 
|}
 
|}
  
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!Final Answer: &nbsp;  
 
!Final Answer: &nbsp;  
 
|-
 
|-
|&nbsp;&nbsp; '''(a)''' &nbsp; &nbsp;  
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|&nbsp;&nbsp; '''(a)''' &nbsp; &nbsp; See solution above.
 
|-
 
|-
|&nbsp;&nbsp; '''(b)''' &nbsp; &nbsp;  
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|&nbsp;&nbsp; '''(b)''' &nbsp; &nbsp; <math style="vertical-align: -3px">\text{dim Col }A=3</math>
 
|}
 
|}
[[031_Review_Part_3|'''<u>Return to Sample Exam</u>''']]
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[[031_Review_Part_3|'''<u>Return to Review Problems</u>''']]

Latest revision as of 14:14, 15 October 2017

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  

Foundations:  
1. An eigenvector    of a matrix    corresponding to the eigenvalue    is a nonzero vector such that
2. By the Rank Theorem, if    is a    matrix, then


Solution:

(a)

Step 1:  
First, notice
since    is a basis of the eigenspace corresponding to the eigenvalue 0 of  
Also, we have
  and  
since    and    are eigenvectors of    corresponding to the eigenvalue 0.
Step 2:  
Now, we have

       

Hence,    is an eigenvector of    corresponding to the eigenvalue  

(b)

Step 1:  
Since    is a basis for the eigenspace of    corresponding to the eigenvalue 0, we know that
Step 2:  
Then, by the Rank Theorem, we have
       
Hence, we have


Final Answer:  
   (a)     See solution above.
   (b)    

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