Line 1: Line 1:
&nbsp;<u>'''Linear Dependence'''</u>
+
----
  
<br>
+
== '''Definition'''&nbsp; &nbsp;  ==
  
'''Definition'''
+
----
 +
 
 +
<br>
  
The vectors <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub>''&nbsp;''</span>in a vector space <span class="texhtml">''V''</span>&nbsp;are said to be linearly dependent if there exist constans&nbsp;<span class="texhtml">''a''<sub>1</sub>,''a''<sub>2</sub><sub></sub>,...,''a''<sub>''k''</sub></span> not all zero, such that<br>  
+
<br> The vectors <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub>''&nbsp;''</span>in a vector space <span class="texhtml">''V''</span>&nbsp;are said to be linearly dependent if there exist constans&nbsp;<span class="texhtml">''a''<sub>1</sub>,''a''<sub>2</sub><sub></sub>,...,''a''<sub>''k''</sub></span> not all zero, such that<br>  
  
 
(1)  
 
(1)  
Line 34: Line 36:
  
 
&nbsp; &nbsp; &nbsp; &nbsp; 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 <span class="texhtml">''a''<sub>1</sub>=''a''<sub>2</sub>=...=''a''<sub>''k''</sub>=0</span>&nbsp;is a solution. However, the main idea from the '''Definition '''is whether there is a nontrivial solution.  
 
&nbsp; &nbsp; &nbsp; &nbsp; 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 <span class="texhtml">''a''<sub>1</sub>=''a''<sub>2</sub>=...=''a''<sub>''k''</sub>=0</span>&nbsp;is a solution. However, the main idea from the '''Definition '''is whether there is a nontrivial solution.  
 +
 +
----
  
 
<br>  
 
<br>  
  
Example 1  
+
<br>
 +
 
 +
----
 +
 
 +
== '''Example 1'''  ==
 +
 
 +
----
  
Determine whether the vectors  
+
<br> 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>  
Line 51: Line 61:
 
<br>  
 
<br>  
  
'''Solution'''  
+
=== '''Solution''' ===
  
 
Forming Equation (1)  
 
Forming Equation (1)  
Line 84: Line 94:
  
 
so the vectors are linearly dependent.  
 
so the vectors are linearly dependent.  
 +
 +
----
  
 
<br>  
 
<br>  
Line 89: Line 101:
 
<br>  
 
<br>  
  
Example 2  
+
<br>
 +
 
 +
----
 +
 
 +
== '''Example 2'''  ==
 +
 
 +
----
  
Are the vectors  
+
<br> 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>  
Line 107: Line 125:
 
<br>  
 
<br>  
  
'''Solution'''  
+
=== '''Solution''' ===
  
 
We form Equation (1),  
 
We form Equation (1),  
Line 144: Line 162:
  
 
so the vectors are linearly independent.  
 
so the vectors are linearly independent.  
 +
 +
----
 +
 +
<br>
  
 
<br>  
 
<br>  
Line 153: Line 175:
 
<br>  
 
<br>  
  
'''Proof'''  
+
=== '''Proof''' ===
  
 
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.  
Line 161: Line 183:
 
<br>  
 
<br>  
  
Example 3  
+
----
 +
 
 +
== Example 3 ==
 +
 
 +
----
  
 
Is&nbsp;
 
Is&nbsp;

Revision as of 19:58, 7 December 2011


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

has a message for current ECE438 students.

Sean Hu, ECE PhD 2009