Linear Dependence of Equation Vectors

In summary, the given set of vectors is linearly dependent and the linear relation among them is a = 3, b = -6, and c = 1. This can be seen through a row reduction of the matrix of the given vectors, which results in a system of equations that has infinitely many solutions, indicating linear dependence. This concept of linear dependence/independence is typically discussed before eigenvalues and Wronskians in textbooks.
  • #1
pillanoid
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0

Homework Statement



Determine whether the members of the given set of vectors are linearly independent for -[tex]\infty[/tex] < t < [tex]\infty[/tex]. If they are linearly dependent, find the linear relation among them.

x(1)(t) = (e-t, 2e-t), x(2)(t) = (e-t, e-t), x(3)(t) = (3e-t, 0)

(the vectors are written as row vectors)

Homework Equations



The section in my book about linear dependence of equation vectors is immediately followed by a discussion of eigenvalues. Wronskians are not covered until the next chapter.

The Attempt at a Solution



I set up a matrix of equations, each vector in the problem statement a column of the matrix, augmented it with the 0 vector, and row-reduced, resulting in

[e-t, 0, -3e-t | 0; 0, e-t, 6e-t | 0]

The book doesn't give any examples, and I'm having a hard time with where to go from here, or if this is the right approach in the first place.
 
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  • #2
What does your reduced matrix tell you about the linear independence/dependence of your three vectors?

Your answer should address the question in your problem.

Has your book discussed linear independence and dependence for ordinary vectors? The idea here is about the same.
 
  • #3
The e-t is just a red herring. You can factor it out and your problem amounts to asking whether (1,2), (1,1), and (3,0) are independent. You probably already know that you can't have 3 independent vectors in R2. So they are going to be dependent. If you try the condition directly for independence you check:

a(1,2) + b(1,1) + c(3,0) = 0 or

a + b + 3c = 0
2a + b + 0c = 0

The row reduction you have done is the same as writing these as

a + 0b -3c = 0
0a +b + 6c = 0

Clearly you can take the c on the other side and let it be most anything except 0. Say c = 1, giving a = 3 and b = -6.

So your row reduction really does the work, you just need to interpret it right.
 

Related to Linear Dependence of Equation Vectors

1. What is the concept of "linear dependence" in equation vectors?

Linear dependence refers to the relationship between two or more vectors where one vector can be expressed as a linear combination of the other vectors. In other words, if one vector can be written as a sum of multiples of other vectors, then the vectors are considered linearly dependent.

2. How do you determine if a set of equation vectors is linearly dependent?

To determine if a set of equation vectors is linearly dependent, you can use the Gaussian elimination method or the determinant method. In Gaussian elimination, you perform row operations on a matrix formed by the vectors and check if the resulting matrix has a row of zeros. If it does, then the vectors are linearly dependent. In the determinant method, you calculate the determinant of the matrix formed by the vectors. If the determinant is equal to 0, then the vectors are linearly dependent.

3. Can a set of equation vectors be linearly dependent in some cases and linearly independent in others?

Yes, a set of equation vectors can be linearly dependent in one case and linearly independent in another case. This depends on the coefficients of the linear combination used to express one vector in terms of the others. If the coefficients are all 0, then the vectors are linearly independent. However, if at least one coefficient is not 0, then the vectors are linearly dependent.

4. How does linear dependence affect the solutions of a system of equations?

If a system of equations has linearly dependent equation vectors, then there are infinitely many solutions or no solutions at all. This is because the equations are essentially expressing the same information, leading to redundant or contradictory equations.

5. What are some real-life applications of linear dependence of equation vectors?

Linear dependence of equation vectors is used in various fields such as physics, engineering, and economics. For example, in physics, it is used to determine the forces acting on an object by analyzing the linear dependence of the equations of motion. In economics, it is used to analyze the relationships between different variables in a system. Additionally, linear dependence is also important in linear algebra and computer graphics.

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