Vectors Linear Independent - Are These Vectors Linearly Independent?

In summary, the given vectors v1=(1,2,0,2), v2=(2,3,1,4), and v3=(0,1,-1,0) are linearly dependent. The vector v=(3,5,1,6) can be expressed as a linear combination of the three vectors with p=1, q=1, and r=0.
  • #1
CSNabeel
12
0

Homework Statement


Considering the following vectors R[tex]^{4}[/tex]:

v1 = (1,2,0,2) v2 = (2,3,1,4) v3 = (0,1,-1,0)

Determine if these vectors are linearly independent. Let S be the linear span of the three vectors. Define a basis and the dimensions of S. Express the vector v=(3,5,1,6) as a linear combination of the three vectors. Can this be achieved in a unique way? Justify your answer?

Homework Equations


I tried to put it into matrix form and reduce via row echolon but I'm not if this is the correct or proper way


The Attempt at a Solution



[ 1 2 0 2
2 3 1 4
0 1 -1 0
3 5 1 6]

[ 1 2 0 2
0 -1 1 0
0 1 -1 0
0 0 0 0 ]

x +2y = 2
y - z = 0
-y + 2 = 0
therefore
y=z making it linearly independent
 
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  • #2
You need to prove that p=q=r=0, for v1,v2,v3 to be linear independent:

[tex]pv_1 + qv_2 +rv_3=0[/tex]

[tex]p(1,2,0,2)+q(2,3,1,4)+r(0,1,-1,0)=0[/tex]

You should express the vector v in same manner as linear combination of v1,v2,v3: i.e pv1+qv2+rv3=v

p,q,r are random scalars.

Regards.
 
  • #3
so with that being said which of the two do I follow from below to work out the answer?

a)

1p + 2q = 0
2p +3q +r = 0
q - r = 0
2p + 4q = 0

b)

1p + 2q = 3
2p +3q +r = 5
q - r = 1
2p + 4q = 6

and if I follow b I'm I right to think that p = 1 q =2 and r = 0
 
Last edited:
  • #4
CSNabeel said:
so with that being said which of the two do I follow from below to work out the answer?

a)

1p + 2q = 0
2p +3q +r = 0
q - r = 0
2p + 4q = 0

b)

1p + 2q = 3
2p +3q +r = 5
q - r = 1
2p + 4q = 6

and if I follow b I'm I right to think that p = 1 q =2 and r = 0

Ok, your task have two parts,

a) to check the linear independence of the vectors v1,v2 and v3

b)to find out if the vector v can be represented as linear combination of the vectors v1,v2 and v3.

So you need to solve both a) and b).

Regards.
 
  • #5
a)

1p + 2q = 0 (1)
2p +3q +r = 0 (2)
q - r = 0 (3)
2p + 4q = 0 (4)

(3) q = r
(1) p = -2q
put (3)and(1) into (2) 2(-2q) + 3(q) +q = -4q +3q + q = 0

p=-2
q = 1
r = 1

vectors are dependentb)

1p + 2q = 3 (1)
2p +3q +r = 5 (2)
q - r = 1 (3)
2p + 4q = 6 (4)

(3) q - 1 = r
(3) into (1) 2p + 3q + (q-1) = 5 ; 2p +4q = 6 (same as 4)
(4) can be divide by 2 to equal (1) answer therefore is

p = 1
q = 1
r = 0

so it that then correct?

Thank you by the way your really helpful
 
  • #6
I am glad that I helped you.

Just a little correction:
a)
r=q
p=-2q
q any number in R, you chose q=1

The vectors are linear dependent

b)
r=q-1
p=3-2q
q any number in R, you chose it q=1

Regards.
 

1. What do you mean by "vectors linear independent"?

Vectors are considered linearly independent if none of them can be written as a linear combination of the others. This means that each vector in the set contributes to the overall direction and magnitude of the set.

2. How can you tell if a set of vectors is linearly independent?

A set of vectors is linearly independent if the only solution to the equation c1v1 + c2v2 + ... + cnvn = 0 is when all of the coefficients (c1, c2, ..., cn) are equal to 0.

3. Can a set of two vectors be linearly dependent?

Yes, a set of two vectors can be linearly dependent if one vector is a multiple of the other. For example, if one vector is (2, 4) and the other is (4, 8), they are linearly dependent because one is twice the other.

4. What is the difference between linearly independent and linearly dependent vectors?

Linearly independent vectors cannot be written as a linear combination of each other, while linearly dependent vectors can be written as a multiple of each other. In other words, linearly independent vectors are unique and contribute to the overall direction and magnitude of a set, while linearly dependent vectors do not add any new information.

5. How is linear independence related to the rank of a matrix?

The rank of a matrix is equal to the number of linearly independent columns or rows in the matrix. This means that a matrix with linearly independent columns has a higher rank, while a matrix with linearly dependent columns has a lower rank.

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