Difference between revisions of "031 Review Part 2, Problem 10"

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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">(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?
    \begin{bmatrix}
 
          1 & -4 & 9 & -7 \\
 
          -1 & 2  & -4 & 1 \\
 
          5 & -6 & 10 & 7
 
        \end{bmatrix}</math>&nbsp; and assume that it is row equivalent to the matrix  
 
 
 
::<math>B=   
 
    \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">(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>
  
 
{| 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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|-
 +
|'''1.''' The dimension of &nbsp;<math style="vertical-align: -2px">\text{Col }A</math>&nbsp; is equal to the number of pivots in &nbsp;<math style="vertical-align: 0px">A.</math>
 +
|-
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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
 
|-
 
|-
 
|
 
|
 +
::<math>\text{dim Nul }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;  
 +
|-
 +
|Since &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; has 4 pivot columns,
 
|-
 
|-
 
|
 
|
 +
::<math>\text{dim Col }A=4.</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;
 +
|-
 +
|Since &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; is a &nbsp;<math style="vertical-align: 0px">6\times 8</math>&nbsp; matrix, &nbsp;<math style="vertical-align: 0px">\text{Col }A</math>&nbsp; contains vectors in &nbsp;<math style="vertical-align: 0px">\mathbb{R}^6.</math>
 +
|-
 +
|Since a vector in &nbsp;<math style="vertical-align: 0px">\mathbb{R}^6</math>&nbsp; is not a vector in &nbsp;<math style="vertical-align: -4px">\mathbb{R}^4,</math>&nbsp; we have
 
|-
 
|-
 
|
 
|
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::<math>\text{Col }A\ne \mathbb{R}^4.</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;  
 +
|-
 +
|By the Rank Theorem, we have
 
|-
 
|-
 
|
 
|
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::<math>\text{dim Nul }A+\text{dim Col }A=5.</math>
 +
|-
 +
|Thus,
 +
|-
 +
|
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::<math>\text{dim Nul }A=5-\text{dim Col}A.</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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|If we want to minimize &nbsp;<math style="vertical-align: -4px">\text{dim Nul }A,</math>&nbsp; we need to maximize &nbsp;<math style="vertical-align: 0px">\text{dim Col }A.</math>
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|-
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|To do this, we need to find out the maximum number of pivots in &nbsp;<math style="vertical-align: 0px">A.</math>
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|-
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|Since &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; is a &nbsp;<math style="vertical-align: -1px">7\times 5</math>&nbsp; matrix, the maximum number of pivots in &nbsp;<math style="vertical-align: 0px">A</math>&nbsp; is 5.
 +
|-
 +
|Hence, the smallest possible value for &nbsp;<math style="vertical-align: -1px">\text{dim Nul }A</math>&nbsp; is
 
|-
 
|-
 
|
 
|
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&nbsp; &nbsp; &nbsp; &nbsp;<math>\begin{array}{rcl}
 +
\displaystyle{\text{dim Nul }A} & = & \displaystyle{5-\text{dim Col }A}\\
 +
&&\\
 +
& \ge & \displaystyle{5-5}\\
 +
&&\\
 +
& \ge & \displaystyle{0.}
 +
\end{array}</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; <math style="vertical-align: -1px">\text{dim Col }A=4</math>&nbsp; and &nbsp;<math style="vertical-align: -6px">\text{Col }A\ne \mathbb{R}^4</math>
 
|-
 
|-
|&nbsp;&nbsp; '''(b)''' &nbsp; &nbsp;  
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|&nbsp;&nbsp; '''(b)''' &nbsp; &nbsp; <math style="vertical-align: -1px">0</math>
 
|}
 
|}
[[031_Review_Part_2|'''<u>Return to Sample Exam</u>''']]
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[[031_Review_Part_2|'''<u>Return to Review Problems</u>''']]

Latest revision as of 13:42, 15 October 2017

(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  

Foundations:  
1. The dimension of    is equal to the number of pivots in  
2. By the Rank Theorem, if    is a    matrix, then


Solution:

(a)

Step 1:  
Since    has 4 pivot columns,
Step 2:  
Since    is a    matrix,    contains vectors in  
Since a vector in    is not a vector in    we have

(b)

Step 1:  
By the Rank Theorem, we have
Thus,
Step 2:  
If we want to minimize    we need to maximize  
To do this, we need to find out the maximum number of pivots in  
Since    is a    matrix, the maximum number of pivots in    is 5.
Hence, the smallest possible value for    is

       


Final Answer:  
   (a)       and  
   (b)    

Return to Review Problems