Question about Linear Dependency

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In summary, the vectors u1, v1, and w1 are linearly dependent for every value of a. This can be seen by expressing the equation d1u1 + d2v1 + d3w1 = 0 in terms of a linear combination of u, v, and w, and using the fact that u, v, and w are linearly dependent. It is not possible for u1, v1, and w1 to be linearly independent, as this would create a subspace of greater dimension than the space it is contained in.
  • #1
Yankel
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which one of the next statements is the correct one ?

Let v,u,w be linearly dependent vectors in a vector space over R.

u1 = 2u
v1 = -3u+4v
w1 = u+2v-aw (a scalar from R)

(1) the vectors u1, v1 and w1 are linearly dependent for every value of a
(2) the vectors u1, v1 and w1 are linearly independent for every value of a
(3) the vectors u1, v1 and w1 are linearly independent for every value of a apart from 0
(4) the vectors u1, v1 and w1 are linearly independent for every positive value of a
(5) there exists a value of a for which the vectors u1, v1 and w1 are linearly independent

Thanks a lot !
 
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  • #2
Yankel said:
which one of the next statements is the correct one ?

Let v,u,w be linearly dependent vectors in a vector space over R.

u1 = 2u
v1 = -3u+4v
w1 = u+2v-aw (a scalar from R)

(1) the vectors u1, v1 and w1 are linearly dependent for every value of a
(2) the vectors u1, v1 and w1 are linearly independent for every value of a
(3) the vectors u1, v1 and w1 are linearly independent for every value of a apart from 0
(4) the vectors u1, v1 and w1 are linearly independent for every positive value of a
(5) there exists a value of a for which the vectors u1, v1 and w1 are linearly independent

Thanks a lot !

This is somewhat similar to the last one. Again, we know that $u,v,w$ are dependent, implying that there are constants $c_1,c_2,c_3$ not all zero such that $c_1u+c_2v+c_3w=0$. Now, we want to analyze when the following is true:

\[d_1u_1+d_2v_1+d_3w_1=0\]

where $d_1,d_2,d_3\in\mathbb{R}$ are arbitrary constants. The idea now is to express the above equation in terms of a linear combination of just $u,v,w$, then use the fact that $u,v,w$ are linearly dependent to come up with the appropriate conclusion.

I hope this helps!
 
  • #3
right, so if I am not mistaken I get:

(2d1-3d2+d3)u + (-4d2+2d3)v + (-ad3)w = 0

what does it tells me ? I know that at least one of the coefficients is not zero, because u,v and w are dependent...what can I say about u1,v1,w1 and what about a ?
 
  • #4
i think focusing on the coefficients overmuch is a mistake.

it is clear that:

$\{u_1,v_1,w_1\} \subset \text{Span}(\{u,v,w\})$.

since {u,v,w} is linearly dependent, this has dimension ≤ 2.

therefore $u_1,v_1,w_1$ cannot be linearly independent, else we have a subspace of greater dimension than a space which contains it.

("a" is a red herring).
 
  • #5


The correct statement is (3) the vectors u1, v1 and w1 are linearly independent for every value of a apart from 0. This is because for a to be equal to 0, w1 would be equal to u+2v, which is a linear combination of u and v and therefore would make the three vectors linearly dependent. For any other value of a, the vectors would be linearly independent.
 

FAQ: Question about Linear Dependency

What is linear dependency?

Linear dependency is a mathematical concept that refers to the relationship between two or more variables in a system. It means that one variable can be expressed as a combination of other variables in the system.

How can you determine if variables are linearly dependent?

To determine if variables are linearly dependent, you can use the method of elimination by setting up a system of equations and solving for the variables. If one variable can be expressed in terms of the other variables, then the variables are linearly dependent.

What is the difference between linear dependence and linear independence?

Linear dependence means that one variable can be expressed in terms of other variables, while linear independence means that no variable can be expressed in terms of other variables. In other words, in a linearly independent system, each variable contributes unique information.

Why is linear dependency important in science?

Linear dependency is important in science because it helps us understand the relationships between different variables in a system. By identifying linearly dependent variables, we can simplify complex systems and make predictions about how changes in one variable may affect others.

How can linear dependency be avoided in scientific experiments?

To avoid linear dependency in scientific experiments, researchers often use statistical methods such as regression analysis to identify and account for any relationships between variables. It is also important to carefully design experiments and control for confounding variables to reduce the risk of linear dependency.

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