Line 41: Line 41:
 
Determine whether the vectors  
 
Determine whether the vectors  
  
<math>v1=\left(\begin{array}{cccc}3\\2\\1\end{array}\right)</math>  
+
<math>v1=\left[\begin{array}{cccc}3\\2\\1\end{array}\right]</math>  
  
<math>v2=\left(\begin{array}{cccc}1\\2\\0\end{array}\right)</math>  
+
<math>v2=\left[\begin{array}{cccc}1\\2\\0\end{array}\right]</math>  
  
<math>v3=\left(\begin{array}{cccc}-1\\-2\\-1\end{array}\right)</math>  
+
<math>v3=\left[\begin{array}{cccc}-1\\-2\\-1\end{array}\right]</math>  
  
 
are linearly independent.  
 
are linearly independent.  
Line 55: Line 55:
 
Forming Equation (1)  
 
Forming Equation (1)  
  
<math>a1\left(\begin{array}{cccc}3\\2\\1\end{array}\right)</math><math>+a2\left(\begin{array}{cccc}1\\2\\0\end{array}\right)</math><math>+a3\left(\begin{array}{cccc}-1\\2\\-1\end{array}\right)</math><math>=\left(\begin{array}{cccc}0\\0\\0\end{array}\right)</math>  
+
<math>a1\left[\begin{array}{cccc}3\\2\\1\end{array}\right]</math><math>+a2\left[\begin{array}{cccc}1\\2\\0\end{array}\right]</math><math>+a3\left[\begin{array}{cccc}-1\\2\\-1\end{array}\right]</math><math>=\left[\begin{array}{cccc}0\\0\\0\end{array}\right]</math>  
  
 
we obtain the homogeneous system  
 
we obtain the homogeneous system  
Line 69: Line 69:
 
The corresponding augmented matrix is  
 
The corresponding augmented matrix is  
  
<math>\left(\begin{array}{cccc}3&1&-1&|0\\2&2&2&|0\\1&0&-1&|0\end{array}\right)</math>  
+
<math>\left[\begin{array}{cccc}3&1&-1&|0\\2&2&2&|0\\1&0&-1&|0\end{array}\right]</math>  
  
 
<br>  
 
<br>  
Line 75: Line 75:
 
Whose reduced row echelon form is  
 
Whose reduced row echelon form is  
  
<math>\left(\begin{array}{cccc}1&0&-1&|0\\0&1&2&|0\\0&0&0&|0\end{array}\right)</math>  
+
<math>\left[\begin{array}{cccc}1&0&-1&|0\\0&1&2&|0\\0&0&0&|0\end{array}\right]</math>  
  
 
<br>  
 
<br>  
Line 81: Line 81:
 
This there is a nontrivial solution  
 
This there is a nontrivial solution  
  
<math>\left(\begin{array}{cccc}k\\-2k\\k\end{array}\right)</math>&nbsp; &nbsp; &nbsp; &nbsp;k is not equal to 0  
+
<math>\left[\begin{array}{cccc}k\\-2k\\k\end{array}\right]</math>&nbsp; &nbsp; &nbsp; &nbsp;k is not equal to 0  
  
 
so the vectors are linearly dependent.  
 
so the vectors are linearly dependent.  
Line 93: Line 93:
 
Are the vectors  
 
Are the vectors  
  
<math>v1=\left(\begin{array}{cccc}1&0&1&2\end{array}\right)</math>  
+
<math>v1=\left[\begin{array}{cccc}1&0&1&2\end{array}\right]</math>  
  
<math>v2=\left(\begin{array}{cccc}0&1&1&2\end{array}\right)</math>  
+
<math>v2=\left[\begin{array}{cccc}0&1&1&2\end{array}\right]</math>  
  
 
and  
 
and  
  
<math>v3=\left(\begin{array}{cccc}1&1&1&3\end{array}\right)</math>&nbsp; &nbsp; &nbsp;in&nbsp;<span class="texhtml">''R''</span><sub><span class="texhtml">4</span></sub>  
+
<math>v3=\left[\begin{array}{cccc}1&1&1&3\end{array}\right]</math>&nbsp; &nbsp; &nbsp;in&nbsp;<span class="texhtml">''R''</span><sub><span class="texhtml">4</span></sub>  
  
 
<br>  
 
<br>  
Line 129: Line 129:
 
The corresponding augmented matrix is  
 
The corresponding augmented matrix is  
  
<math>\left(\begin{array}{cccc}1&0&1&|0\\0&1&1&|0\\1&1&1&|0\\2&2&3&|0\end{array}\right)</math>  
+
<math>\left[\begin{array}{cccc}1&0&1&|0\\0&1&1&|0\\1&1&1&|0\\2&2&3&|0\end{array}\right]</math>  
  
 
<br>  
 
<br>  
Line 135: Line 135:
 
and its reduced row echelon form is  
 
and its reduced row echelon form is  
  
<math>\left(\begin{array}{cccc}1&0&0&|0\\0&1&0&|0\\0&0&1&|0\\0&0&0&|0\end{array}\right)</math>  
+
<math>\left[\begin{array}{cccc}1&0&0&|0\\0&1&0&|0\\0&0&1&|0\\0&0&0&|0\end{array}\right]</math>  
  
 
<br>  
 
<br>  
Line 149: Line 149:
 
Let {<span class="texhtml">''S'' = ''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''n''</sub>''}''</span>  
 
Let {<span class="texhtml">''S'' = ''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''n''</sub>''}''</span>  
  
be a set of&nbsp;<span class="texhtml">''n''</span>&nbsp;vectors in&nbsp;<span class="texhtml">''R<sup>'''n'''</sup>''</span>'''''&nbsp;'''(<span class="texhtml">''R''<sub>''n''</sub></span>)''. Let&nbsp;<span class="texhtml">''A''</span>&nbsp;be the matrix whose columns (rows) are the elements of&nbsp;<span class="texhtml">''S''</span>. Then&nbsp;<span class="texhtml">''S''</span>&nbsp;is linearly independent if and only if det(<span class="texhtml">''A''</span>) is not equal to 0.  
+
be a set of&nbsp;<span class="texhtml">''n''</span>&nbsp;vectors in&nbsp;<span class="texhtml">''R<sup>'''n'''</sup>''</span>'''''&nbsp;'''(&lt;span class="texhtml" /&gt;''R''<sub></sub>''n'')''. Let&nbsp;<span class="texhtml">''A''</span>&nbsp;be the matrix whose columns (rows) are the elements of&nbsp;<span class="texhtml">''S''</span>. Then&nbsp;<span class="texhtml">''S''</span>&nbsp;is linearly independent if and only if det(<span class="texhtml">''A''</span>) is not equal to 0.  
  
 
<br>  
 
<br>  
Line 157: Line 157:
 
We shall prove the result for columns only; the proof for rows is analogous.  
 
We shall prove the result for columns only; the proof for rows is analogous.  
  
Suppose that&nbsp;''<span class="texhtml">S</span>''&nbsp;is linearly independent. Then it follows that the reduced row echelon form of&nbsp;''<span class="texhtml">A</span>''&nbsp;is''&nbsp;<span class="texhtml">I<sub>'''n'''</sub></span>''<span class="texhtml" />'''. '''Thus,&nbsp;''A''&nbsp;is row equivalent to ''I''<sub>''n''</sub>, and hence det(''<span class="texhtml">A</span>'') is not equal to 0. Conversely, if det(''<span class="texhtml">A</span>'') is not equal to 0, then''&nbsp;<span class="texhtml">A</span>''&nbsp;is row equivalent to&nbsp;''<span class="texhtml">I</span><sub><span class="texhtml">n</span></sub>''. Now assume that the rows of&nbsp;A&nbsp;are linearly dependent. Then it follows that the reduced row echelon form of&nbsp;''A''&nbsp;has a zero row, which contradicts the earlier conclusion that ''A''&nbsp;is row equivalent to ''I''<sub>''n''</sub>. Hence, rows of&nbsp;<span class="texhtml" />''A''&nbsp;are linearly independent.  
+
Suppose that&nbsp;''<span class="texhtml">S</span>''&nbsp;is linearly independent. Then it follows that the reduced row echelon form of&nbsp;''<span class="texhtml">A</span>''&nbsp;is''&nbsp;<span class="texhtml">I<sub>'''n'''</sub></span>''&lt;span class="texhtml" /&gt;'''. '''Thus,&nbsp;''A''&nbsp;is row equivalent to ''I''<sub>''n''</sub>, and hence det(''<span class="texhtml">A</span>'') is not equal to 0. Conversely, if det(''<span class="texhtml">A</span>'') is not equal to 0, then''&nbsp;<span class="texhtml">A</span>''&nbsp;is row equivalent to&nbsp;''<span class="texhtml">I</span><sub><span class="texhtml">n</span></sub>''. Now assume that the rows of&nbsp;A&nbsp;are linearly dependent. Then it follows that the reduced row echelon form of&nbsp;''A''&nbsp;has a zero row, which contradicts the earlier conclusion that ''A''&nbsp;is row equivalent to ''I''<sub>''n''</sub>. Hence, rows of&nbsp;&lt;span class="texhtml" /&gt;''A''&nbsp;are linearly independent.  
 
+
  
 +
<br>
  
Example 3
+
Example 3  
  
 
Is&nbsp;
 
Is&nbsp;

Revision as of 19:44, 7 December 2011

 Linear Dependence


Definition

The vectors v1,v2,...,vk in a vector space V are said to be linearly dependent if there exist constans a1,a2,...,ak not all zero, such that

(1)


Otherwise, v1,v2,...,vk are called linearly independent. That is v1,v2,...,vk are linearly independent if, whenever a1v1+ a2v2+...+akvk=0


                                     a1= a2=...=ak=0



If S = {v1,v2,...,vk} ,then we also say that the set S is linearly dependent or linearly independent if the vectors have the corresponding property.


         It should be emphasized that for any vectors v1,v2,...,vk, Equation (1) always holds if we choose all the scalars a1,a2,...,ak, equal to zero. The important point in this definition is whether it is possible to satisfy (1) with at least one of the scalars different from zero.


Remark: Definition is started for a finite set of vectors, but it also applies to an infinite set S of a vector space, using corresponding notation for infinite sums.


        To determine whether a set of vectors is linearly independent or linearly dependent, we use Equation (1). Regardless of the form of the vectors, Equation (1) yields a homogeneous linear system of equations. it is always consistent, since a1=a2=...=ak=0 is a solution. However, the main idea from the Definition is whether there is a nontrivial solution.


Example 1

Determine whether the vectors

$ v1=\left[\begin{array}{cccc}3\\2\\1\end{array}\right] $

$ v2=\left[\begin{array}{cccc}1\\2\\0\end{array}\right] $

$ v3=\left[\begin{array}{cccc}-1\\-2\\-1\end{array}\right] $

are linearly independent.


Solution

Forming Equation (1)

$ a1\left[\begin{array}{cccc}3\\2\\1\end{array}\right] $$ +a2\left[\begin{array}{cccc}1\\2\\0\end{array}\right] $$ +a3\left[\begin{array}{cccc}-1\\2\\-1\end{array}\right] $$ =\left[\begin{array}{cccc}0\\0\\0\end{array}\right] $

we obtain the homogeneous system

3a1 + a2a3 = 0

2a1 + 2a2 + 2a3 = 0

a1a3 = 0


The corresponding augmented matrix is

$ \left[\begin{array}{cccc}3&1&-1&|0\\2&2&2&|0\\1&0&-1&|0\end{array}\right] $


Whose reduced row echelon form is

$ \left[\begin{array}{cccc}1&0&-1&|0\\0&1&2&|0\\0&0&0&|0\end{array}\right] $


This there is a nontrivial solution

$ \left[\begin{array}{cccc}k\\-2k\\k\end{array}\right] $       k is not equal to 0

so the vectors are linearly dependent.



Example 2

Are the vectors

$ v1=\left[\begin{array}{cccc}1&0&1&2\end{array}\right] $

$ v2=\left[\begin{array}{cccc}0&1&1&2\end{array}\right] $

and

$ v3=\left[\begin{array}{cccc}1&1&1&3\end{array}\right] $     in R4


linearly dependent or linearly independent?


Solution

We form Equation (1),

a1v1+a2v2+a3v3=0

and solve for a1,a2,a3.

The resulting homogeneous system is

a1 + a3 = 0

a2 + a3 = 0

a1 + a2 + a3 = 0

2a1 + 2a2 + 3a3 = 0


The corresponding augmented matrix is

$ \left[\begin{array}{cccc}1&0&1&|0\\0&1&1&|0\\1&1&1&|0\\2&2&3&|0\end{array}\right] $


and its reduced row echelon form is

$ \left[\begin{array}{cccc}1&0&0&|0\\0&1&0&|0\\0&0&1&|0\\0&0&0&|0\end{array}\right] $


Thus the only solution is the trivial solution

a1 = a2 = a3 = 0

so the vectors are linearly independent.


Let {S = v1,v2,...,vn}

be a set of n vectors in Rn (<span class="texhtml" />Rn). Let A be the matrix whose columns (rows) are the elements of S. Then S is linearly independent if and only if det(A) is not equal to 0.


Proof

We shall prove the result for columns only; the proof for rows is analogous.

Suppose that S is linearly independent. Then it follows that the reduced row echelon form of A is In<span class="texhtml" />. Thus, A is row equivalent to In, and hence det(A) is not equal to 0. Conversely, if det(A) is not equal to 0, then A is row equivalent to In. Now assume that the rows of A are linearly dependent. Then it follows that the reduced row echelon form of A has a zero row, which contradicts the earlier conclusion that A is row equivalent to In. Hence, rows of <span class="texhtml" />A are linearly independent.


Example 3

Is 

Alumni Liaison

To all math majors: "Mathematics is a wonderfully rich subject."

Dr. Paul Garrett