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'''Tricks for checking Linear Independence, Span and Basis'''  
 
'''Tricks for checking Linear Independence, Span and Basis'''  
  
Note: For this article, I am assuming number of vectors is equal to the dimension of the vector space for calculating the determinant. If it is not, you need to do rref.
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Note: For this article, I am assuming number of vectors is equal to the dimension of the vector space for calculating the determinant. If it is not, you need to do rref.  
  
 
<br> <u>'''Linear Independence'''</u>  
 
<br> <u>'''Linear Independence'''</u>  
  
If det(vectors)&nbsp;!= 0 ⇔ '''linearly independent'''<br> If end result of the rref(vectors) gives an identity matrix, it is '''linearly independent'''  
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*If end result of the rref(vectors) gives an identity matrix, it is '''linearly independent'''<br>
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*If end result of the rref(vectors) gives you a parameter in the matrix, the vectors are '''linearly dependent.'''
  
If det(vectors) = 0 ⇔ '''linearly dependent'''<br>If end result of the rref(vectors) gives you a parameter in the equation, the vectors are '''linearly dependent.'''
 
  
Tip: If #No of vectors &gt; Dimension ⇔ it is '''linearly dependent'''<br>  
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Tricks:  
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If det(vectors)&nbsp;!= 0 ⇔ '''linearly independent'''
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If det(vectors) = 0 ⇔ '''linearly dependent'''
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If #No of vectors &gt; Dimension ⇔ it is '''linearly dependent'''
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Example: <math>rref(\left( \begin{smallmatrix} 1&2&3\\ 2&3&4 \end{smallmatrix} \right)) = \left( \begin{smallmatrix} 1&0&-1\\ 0&1&2 \end{smallmatrix} \right)</math> is '''linearly dependent''' in R<sup>2</sup> because the last column [-1 2]<sup>T</sup> i.e z is a parameter as there can be no leading 1 for that column. You can express x = z and y = -2z
  
 
<u>'''Span'''</u>  
 
<u>'''Span'''</u>  
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<br>If Dimension &gt; #No of vectors ⇔ cannot span ⇔ is not a basis  
 
<br>If Dimension &gt; #No of vectors ⇔ cannot span ⇔ is not a basis  
  
If #No of vectors &gt; Dimension -&gt; it is not a basis.
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If #No of vectors &gt; Dimension -&gt; it is not a basis.  
  
If #No of vectors = Dimension -&gt; it has to be linearly independent to span<span class="texhtml"></span>  
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If #No of vectors = Dimension -&gt; it has to be linearly independent to span<span class="texhtml" />  
  
 
[[Category:MA265Spring2011Momin]]
 
[[Category:MA265Spring2011Momin]]
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<br>

Revision as of 09:38, 1 May 2011

Tricks for checking Linear Independence, Span and Basis

Note: For this article, I am assuming number of vectors is equal to the dimension of the vector space for calculating the determinant. If it is not, you need to do rref.


Linear Independence

  • If end result of the rref(vectors) gives an identity matrix, it is linearly independent
  • If end result of the rref(vectors) gives you a parameter in the matrix, the vectors are linearly dependent.


Tricks:

If det(vectors) != 0 ⇔ linearly independent

If det(vectors) = 0 ⇔ linearly dependent

If #No of vectors > Dimension ⇔ it is linearly dependent

Example: $ rref(\left( \begin{smallmatrix} 1&2&3\\ 2&3&4 \end{smallmatrix} \right)) = \left( \begin{smallmatrix} 1&0&-1\\ 0&1&2 \end{smallmatrix} \right) $ is linearly dependent in R2 because the last column [-1 2]T i.e z is a parameter as there can be no leading 1 for that column. You can express x = z and y = -2z

Span

If Dimension > #No of vectors -> it CANNOT span

If det(vectors) != 0 ⇔ it spans
If end result of the rref(vectors) gives you a matrix with all rows having leading 1's, it spans.  For example: $ rref(\left( \begin{smallmatrix} 1&2&3\\ 2&3&4 \end{smallmatrix} \right)) = \left( \begin{smallmatrix} 1&0&-1\\ 0&1&2 \end{smallmatrix} \right) $ spans R2

If det(vectors) = 0 ⇔ does not span
If end result of the rref(vectors) gives you a matrix with not all rows having a leading 1, it does not span.

Basis


If Dimension > #No of vectors ⇔ cannot span ⇔ is not a basis

If #No of vectors > Dimension -> it is not a basis.

If #No of vectors = Dimension -> it has to be linearly independent to span<span class="texhtml" />


Alumni Liaison

Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett