How Can You Prove Linear Dependence in Vector Sets?

In summary, the given set { (1, 4, -6), (1, 5, 8), (2, 1, 1), (0, 1, 0) } is not a linearly independent subset of R^3. This is because R^3 has a dimension of 3 and the set contains 4 generating vectors. However, the student tried creating a linear system and got stuck. He asked for help and mentioned that the professor alluded to using Gaussian Reduction, which the student is not yet familiar with. After some further studying, the student was able to prove that a(1, 4, -6) + b(1, 5, 8) +
  • #1
vsage
I've already found the answer to this solution but I want to check my methods because the class is very proof-based and the professor likes to take off points for style in proofs on tests:

5. Is {(1, 4, -6), (1, 5, 8), (2, 1, 1), (0, 1, 0)} a linearly independent subset of R^3? Justify your answer.

Obviously the answer is no because R^3 has a dimension of 3 and if you're given 4 generating vectors then one isn't necessary. However, I tried creating the linear system and got stuck trying to prove that for this system:

a + b + 2c = 0
4a + 5b + c + d = 0
-6a + 8b + c = 0

that at least one of a, b, c and d is nonzero and

a(1, 4, -6) + b(1, 5, 8) + c(2, 1, 1) + d(0, 1, 0) = 0

works for at least one nonzero a, b, c, d. Little help? The professor alluded to the fact that you can use Gaussian Reduction but he said we wouldn't learn how to do that for another chapter but he regularly uses it in class. The only way I know of it is studying ahead a year back or so but how do I reduce this?
 
Last edited by a moderator:
Physics news on Phys.org
  • #2
Nevermind I got it.. Proved that a(1, 4, -6) + b(1, 5, 8) + c(2, 1, 1) = (0, -1, 0)
 
  • #3


To prove linear dependence, we need to show that at least one of the vectors in the set is a linear combination of the others. In other words, there exists scalars a, b, c, and d (not all zero) such that:

a(1, 4, -6) + b(1, 5, 8) + c(2, 1, 1) + d(0, 1, 0) = 0

To solve for these scalars, we can use Gaussian reduction. The first step is to set up the augmented matrix:

[1 1 2 0 | 0]
[4 5 1 1 | 0]
[-6 8 1 0 | 0]

Next, we can perform row operations to reduce this matrix to row-echelon form. This involves using elementary row operations, such as multiplying a row by a non-zero scalar, adding a multiple of one row to another, or switching the positions of two rows. The goal is to get the matrix in the form:

[1 0 0 a | 0]
[0 1 0 b | 0]
[0 0 1 c | 0]

If we are able to achieve this form, then we have found the values of a, b, and c that satisfy the equation above. If we are not able to achieve this form, then the set is linearly independent.

In this case, we can perform the following row operations to reduce the matrix:

R2 - 4R1 --> R2 (this eliminates the a term in the second row)
R3 + 6R1 --> R3 (this eliminates the a term in the third row)
R2 - 2R3 --> R2 (this eliminates the c term in the second row)

This gives us the following reduced row-echelon form:

[1 0 0 a | 0]
[0 1 0 b | 0]
[0 0 1 c | 0]

Since we are able to achieve this form, we can conclude that the set is linearly dependent. In fact, we can see that a = -2b and c = 6b, which means that for any non-zero value of b, we can find values of a and c that satisfy the equation above. Therefore, we have proven that at least one of
 

What is the definition of linear dependence?

Linear dependence refers to the relationship between two or more vectors in which one vector can be represented as a linear combination of the other vectors. In other words, one vector can be written as a sum of scalar multiples of the other vectors.

How do you prove linear dependence?

To prove linear dependence, you can use the definition mentioned above and demonstrate that one vector is a linear combination of the other vectors. This can be done by finding a set of scalar values that when multiplied by each vector, will result in the desired vector.

What is an example of linear dependence?

An example of linear dependence can be found in a system of equations. If one equation is a multiple of another equation, then the system is linearly dependent. For example, the equations 2x + 3y = 6 and 4x + 6y = 12 are linearly dependent because the second equation is simply the first equation multiplied by 2.

What is the importance of proving linear dependence?

Proving linear dependence is important in many areas of mathematics and science, such as linear algebra, differential equations, and physics. It allows us to determine if a set of vectors is redundant or if there is a linear relationship between them, which can help simplify complex problems and equations.

What are some techniques for proving linear dependence?

There are several techniques for proving linear dependence, including using the definition of linear dependence, Gaussian elimination, finding a non-trivial solution to a system of equations, and using the concept of rank. Other techniques include finding a linear combination of vectors that equals the zero vector or finding a determinant of zero in a matrix. The appropriate technique will depend on the specific problem and situation.

Similar threads

  • Introductory Physics Homework Help
Replies
1
Views
213
  • Introductory Physics Homework Help
Replies
7
Views
390
  • Calculus and Beyond Homework Help
Replies
2
Views
985
Replies
11
Views
483
  • Introductory Physics Homework Help
Replies
23
Views
345
  • Introductory Physics Homework Help
Replies
12
Views
1K
  • Calculus and Beyond Homework Help
Replies
12
Views
1K
  • Introductory Physics Homework Help
Replies
2
Views
733
  • Introductory Physics Homework Help
Replies
25
Views
274
  • Introductory Physics Homework Help
Replies
2
Views
552
Back
Top