Line 1: | Line 1: | ||
− | = <u>'''Linear Dependence'''</u> = | + | = ''<u>'''Linear Dependence'''</u>''<u></u> = |
<br> | <br> | ||
Line 5: | Line 5: | ||
---- | ---- | ||
− | == '''Definition''' == | + | == '''''Definition''''' == |
---- | ---- | ||
Line 11: | Line 11: | ||
<br> | <br> | ||
− | <br> The vectors <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub>'' ''</span>in a vector space <span class="texhtml">''V''</span> are said to be linearly dependent if there exist constans <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>'' ''</span>in a vector space <span class="texhtml">''V''</span> are said to be linearly dependent if there exist constans <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 17: | Line 17: | ||
<br> | <br> | ||
− | Otherwise, <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub></span> are called linearly independent. That is <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub></span> are linearly independent if, whenever <span class="texhtml">''a''<sub>1</sub>''v''<sub>1</sub>+ ''a''<sub>2</sub>''v''<sub>2</sub>+...+''a''<sub>''k''</sub>''v''<sub>''k''</sub>=0</span> | + | == Otherwise, <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub></span> are called linearly independent. That is <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub></span> are linearly independent if, whenever <span class="texhtml">''a''<sub>1</sub>''v''<sub>1</sub>+ ''a''<sub>2</sub>''v''<sub>2</sub>+...+''a''<sub>''k''</sub>''v''<sub>''k''</sub>=0</span> == |
<br> | <br> | ||
− | <span class="texhtml">''a''<sub>1</sub>= ''a''<sub>2</sub>=...=''a''<sub>''k''</sub>=0</span> | + | == <span class="texhtml">''a''<sub>1</sub>= ''a''<sub>2</sub>=...=''a''<sub>''k''</sub>=0</span> == |
<br> | <br> | ||
Line 27: | Line 27: | ||
<br> | <br> | ||
− | If <span class="texhtml">''S'' = {''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub>''}''</span> ,then we also say that the set <span class="texhtml">''S''</span> is linearly dependent or linearly independent if the vectors have the corresponding property. | + | == If <span class="texhtml">''S'' = {''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub>''}''</span> ,then we also say that the set <span class="texhtml">''S''</span> is linearly dependent or linearly independent if the vectors have the corresponding property. == |
<br> | <br> | ||
− | It should be emphasized that for any vectors <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub></span>, Equation (1) always holds if we choose all the scalars <span class="texhtml">''a''<sub>1</sub>,''a''<sub>2</sub>,...,''a''<sub>''k''</sub></span>, 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. | + | == It should be emphasized that for any vectors <span class="texhtml">''v''<sub>1</sub>,''v''<sub>2</sub>,...,''v''<sub>''k''</sub></span>, Equation (1) always holds if we choose all the scalars <span class="texhtml">''a''<sub>1</sub>,''a''<sub>2</sub>,...,''a''<sub>''k''</sub></span>, 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. == |
<br> | <br> | ||
− | Remark: '''Definition''' is started for a finite set of vectors, but it also applies to an infinite set <span class="texhtml">''S''</span> of a vector space, using corresponding notation for infinite sums. | + | == Remark: '''Definition''' is started for a finite set of vectors, but it also applies to an infinite set <span class="texhtml">''S''</span> of a vector space, using corresponding notation for infinite sums. == |
<br> | <br> | ||
− | 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> is a solution. However, the main idea from the '''Definition '''is whether there is a nontrivial solution. | + | == 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> is a solution. However, the main idea from the '''Definition '''is whether there is a nontrivial solution. == |
---- | ---- | ||
Line 49: | Line 49: | ||
---- | ---- | ||
− | == '''Example 1''' == | + | == '''''Example 1''''' == |
---- | ---- | ||
− | <br> 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 61: | Line 61: | ||
<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. == |
<br> | <br> | ||
− | === '''Solution''' === | + | === <u>'''Solution'''</u> === |
− | 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 == |
− | <span class="texhtml">3''a''<sub>1</sub> + ''a''<sub>2</sub> − ''a''<sub>3</sub> = 0</span> | + | == <span class="texhtml">3''a''<sub>1</sub> + ''a''<sub>2</sub> − ''a''<sub>3</sub> = 0</span> == |
− | <span class="texhtml">2''a''<sub>1</sub> + 2''a''<sub>2</sub> + 2''a''<sub>3</sub> = 0</span> | + | == <span class="texhtml">2''a''<sub>1</sub> + 2''a''<sub>2</sub> + 2''a''<sub>3</sub> = 0</span> == |
− | <span class="texhtml">''a''<sub>1</sub> − ''a''<sub>3</sub> = 0</span> | + | == <span class="texhtml">''a''<sub>1</sub> − ''a''<sub>3</sub> = 0</span> == |
<br> | <br> | ||
− | 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> | ||
Line 87: | Line 87: | ||
<br> | <br> | ||
− | 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> | ||
Line 93: | Line 93: | ||
<br> | <br> | ||
− | This there is a nontrivial solution | + | == This there is a nontrivial solution == |
− | <math>\left[\begin{array}{cccc}k\\-2k\\k\end{array}\right]</math> k is not equal to 0 | + | === <math>\left[\begin{array}{cccc}k\\-2k\\k\end{array}\right]</math> k is not equal to 0 === |
− | so the vectors are linearly dependent. | + | == so the vectors are linearly dependent. == |
---- | ---- | ||
Line 109: | Line 109: | ||
---- | ---- | ||
− | == '''Example 2''' == | + | == '''''Example 2''''' == |
---- | ---- | ||
− | <br> 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> | ||
− | <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><br> |
− | + | == <math>v3=\left[\begin{array}{cccc}1&1&1&3\end{array}\right]</math> in <span class="texhtml">''R''</span><sub><span class="texhtml">4</span></sub> == | |
− | + | == linearly dependent or linearly independent? == | |
<br> | <br> | ||
− | + | === <u>'''Solution'''</u> === | |
− | + | == We form Equation (1), == | |
− | === ''' | + | == <span class="texhtml">''a''<sub>1</sub>''v''<sub>1</sub>+''a''<sub>2</sub>''v''<sub>2</sub>+''a''<sub>3</sub>''v''<sub>3</sub>=0</span> == |
− | + | == and solve for <span class="texhtml">''a''<sub>1</sub>,''a''<sub>2</sub>,''a''</span><sub><span class="texhtml">3</span></sub><span class="texhtml">.</span><br> == | |
− | + | == The resulting homogeneous system is == | |
− | + | == <span class="texhtml">''a''<sub>1</sub> + ''a''<sub>3</sub> = 0</span> == | |
− | + | == <span class="texhtml">''a''<sub>2</sub> + ''a''<sub>3</sub> = 0</span> == | |
− | <span class="texhtml">''a''<sub>1</sub> + ''a''<sub>3</sub> = 0</span> | + | == <span class="texhtml">''a''<sub>1</sub> + ''a''<sub>2 </sub>+ ''a''<sub>3</sub> = 0</span> == |
− | <span class="texhtml">''a''<sub>2</sub> + ''a''<sub>3</sub> = 0</span> | + | == <span class="texhtml">2''a''<sub>1</sub> + 2''a''<sub>2 </sub>+ 3''a''<sub>3</sub> = 0</span> == |
− | + | == <br> == | |
− | + | == 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> | ||
Line 155: | Line 151: | ||
<br> | <br> | ||
− | 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> | ||
Line 161: | Line 157: | ||
<br> | <br> | ||
− | Thus the only solution is the trivial solution | + | == Thus the only solution is the trivial solution == |
− | <span class="texhtml">''a''<sub>1</sub> = ''a''<sub>2</sub> = ''a''<sub>3</sub> = 0</span> | + | == <span class="texhtml">''a''<sub>1</sub> = ''a''<sub>2</sub> = ''a''<sub>3</sub> = 0</span> == |
− | so the vectors are linearly independent. | + | == so the vectors are linearly independent. == |
---- | ---- | ||
Line 173: | Line 169: | ||
<br> | <br> | ||
− | 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 <span class="texhtml">''n''</span> vectors in <span class="texhtml">''R<sup>'''n'''</sup>''</span>''''' '''(<span class="texhtml" />''R''<sub></sub> | + | == be a set of <span class="texhtml">''n''</span> vectors in <span class="texhtml">''R<sup>'''n'''</sup>''</span>''''' '''(<span class="texhtml" />''R''<sub></sub>''. Let <span class="texhtml">''A''</span> be the matrix whose columns (rows) are the elements of <span class="texhtml">''S''</span>. Then <span class="texhtml">''S''</span> is linearly independent if and only if det(<span class="texhtml">''A''</span>) is not equal to 0. == |
<br> | <br> | ||
− | === '''Proof''' === | + | === <u>'''Proof'''</u> === |
− | 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 ''<span class="texhtml">S</span>'' is linearly independent. Then it follows that the reduced row echelon form of ''<span class="texhtml">A</span>'' is'' <span class="texhtml">I<sub>'''n'''</sub></span>'' | + | == Suppose that ''<span class="texhtml">S</span>'' is linearly independent. Then it follows that the reduced row echelon form of ''<span class="texhtml">A</span>'' is'' <span class="texhtml">I<sub>'''n'''</sub></span>'''''. '''Thus, ''A'' 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'' <span class="texhtml">A</span>'' is row equivalent to ''<span class="texhtml">I</span><sub><span class="texhtml">n</span></sub>''. 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 ''I''<sub>''n''</sub>. Hence, rows of ''A'' are linearly independent. == |
− | <br> | + | == <br> == |
---- | ---- | ||
− | == Example 3 == | + | == ''Example 3'' == |
---- | ---- | ||
Line 195: | Line 191: | ||
== Is <math>S=\left[\begin{array}{cccc}1&2&3\end{array}\right]</math> <math>,\left[\begin{array}{cccc}0&1&2\end{array}\right]</math> <math>,\left[\begin{array}{cccc}3&0&-1\end{array}\right]</math> == | == Is <math>S=\left[\begin{array}{cccc}1&2&3\end{array}\right]</math> <math>,\left[\begin{array}{cccc}0&1&2\end{array}\right]</math> <math>,\left[\begin{array}{cccc}3&0&-1\end{array}\right]</math> == | ||
− | a linearly independent set of vectors in <span class="texhtml">''R''</span><sup><span class="texhtml">3</span></sup><span class="texhtml">?</span> | + | == a linearly independent set of vectors in <span class="texhtml">''R''</span><sup><span class="texhtml">3</span></sup><span class="texhtml">?</span> == |
− | + | <br> | |
− | == Solution == | + | == <u>Solution</u> == |
− | We form the matrix <span class="texhtml">''A''</span> whose rows are the vectors in <span class="texhtml">''S''</span>: | + | == We form the matrix <span class="texhtml">''A''</span> whose rows are the vectors in <span class="texhtml">''S''</span>: == |
<br> | <br> | ||
Line 209: | Line 205: | ||
<br> | <br> | ||
− | Since det(<span class="texhtml">''A''</span>) = 2, we conclude that <span class="texhtml">''S''</span> is linearly independent. | + | == Since det(<span class="texhtml">''A''</span>) = 2, we conclude that <span class="texhtml">''S''</span> is linearly independent. == |
− | + | ||
+ | <br> | ||
− | 223 | + | 223 |
---- | ---- |
Revision as of 20:09, 7 December 2011
Contents
- 1 Linear Dependence
- 1.1 Definition
- 1.2 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
- 1.3 Otherwise, v1,v2,...,vk are called linearly independent. That is v1,v2,...,vk are linearly independent if, whenever a1v1+ a2v2+...+akvk=0
- 1.4 a1= a2=...=ak=0
- 1.5 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.
- 1.6 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.
- 1.7 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.
- 1.8 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.
- 1.9 Example 1
- 1.10 Determine whether the vectors
- 1.11 are linearly independent.
- 1.12 Forming Equation (1)
- 1.13 we obtain the homogeneous system
- 1.14 3a1 + a2 − a3 = 0
- 1.15 2a1 + 2a2 + 2a3 = 0
- 1.16 a1 − a3 = 0
- 1.17 The corresponding augmented matrix is
- 1.18 Whose reduced row echelon form is
- 1.19 This there is a nontrivial solution
- 1.20 so the vectors are linearly dependent.
- 1.21 Example 2
- 1.22 Are the vectors
- 1.23 $ v3=\left[\begin{array}{cccc}1&1&1&3\end{array}\right] $ in R4
- 1.24 linearly dependent or linearly independent?
- 1.25 We form Equation (1),
- 1.26 a1v1+a2v2+a3v3=0
- 1.27 and solve for a1,a2,a3.
- 1.28 The resulting homogeneous system is
- 1.29 a1 + a3 = 0
- 1.30 a2 + a3 = 0
- 1.31 a1 + a2 + a3 = 0
- 1.32 2a1 + 2a2 + 3a3 = 0
- 1.33
- 1.34 The corresponding augmented matrix is
- 1.35 and its reduced row echelon form is
- 1.36 Thus the only solution is the trivial solution
- 1.37 a1 = a2 = a3 = 0
- 1.38 so the vectors are linearly independent.
- 1.39 Let {S = v1,v2,...,vn}
- 1.40 be a set of n vectors in Rn (<span class="texhtml" />R. 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.
- 1.41 We shall prove the result for columns only; the proof for rows is analogous.
- 1.42 Suppose that S is linearly independent. Then it follows that the reduced row echelon form of A is In. 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 A are linearly independent.
- 1.43
- 1.44 Example 3
- 1.45 Is $ S=\left[\begin{array}{cccc}1&2&3\end{array}\right] $ $ ,\left[\begin{array}{cccc}0&1&2\end{array}\right] $ $ ,\left[\begin{array}{cccc}3&0&-1\end{array}\right] $
- 1.46 a linearly independent set of vectors in R3?
- 1.47 Solution
- 1.48 We form the matrix A whose rows are the vectors in S:
- 1.49 Since det(A) = 2, we conclude that S is linearly independent.
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 + a2 − a3 = 0
2a1 + 2a2 + 2a3 = 0
a1 − a3 = 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] $
$ 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" />R. 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. 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 A are linearly independent.
Example 3
Is $ S=\left[\begin{array}{cccc}1&2&3\end{array}\right] $ $ ,\left[\begin{array}{cccc}0&1&2\end{array}\right] $ $ ,\left[\begin{array}{cccc}3&0&-1\end{array}\right] $
a linearly independent set of vectors in R3?
Solution
We form the matrix A whose rows are the vectors in S:
$ A=\left[\begin{array}{cccc}1&2&3\\0&1&2\\3&0&-1\end{array}\right] $
Since det(A) = 2, we conclude that S is linearly independent.
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