Linear dependence of a set under linear transformation?

In summary, the conversation discusses a problem in linear algebra regarding the dependence of a linear transformation T(S) when S is a linearly dependent subset of V. The usual proof for this problem involves showing that T(S) is dependent by using distinct vectors v_1, ..., v_n in S and scalars a_1, ..., a_n. However, a question is raised about the validity of this proof if some v_i's take on the same value under T. Further examples and discussions are provided, ultimately concluding that the statement being discussed is not true.
  • #1
poochie_d
18
0
Hi all,

Here is the problem:
If T: V -> W is a linear transformation and S is a linearly dependent subset of V, then prove that T(S) is linearly dependent.

Now, I know that the usual proof goes as follows:
Since S is linearly dependent, there are distinct vectors [itex]v_1, ..., v_n[/itex] in S and scalars [itex]a_1, ..., a_n[/itex] (not all zero) such that [itex] \sum_{i=1}^n a_i v_i = 0. [/itex]

=> [itex] \sum_{i=1}^n a_i T(v_i) = T(\sum_{i=1}^n a_i v_i) = T(0) = 0 [/itex]

=> Since there are vectors [itex]T(v_1), ..., T(v_n)[/itex] in T(S) and scalars [itex]a_1, ..., a_n[/itex] (not all zero) such that they form a nontrivial representation of 0, it follows that T(S) is dependent.

What I am wondering is whether the above proof is still valid if some of the [itex]v_i[/itex]'s take on the same value under T. In this case, wouldn't the proof be wrong, since you have to have distinct vectors to show that the set is dependent?

e.g. What if you have a situation where [itex]S = \{v_1,v_2,v_3\}[/itex] and [itex]v_1 + v_2 - 2v_3 = 0,[/itex] but [itex]T(v_1) = T(v_2) = T(v_3) = w[/itex] (say), so that
[itex]0 = T(v_1) + T(v_2) - T(v_3) = w + w - 2w = 0w[/itex]? This doesn't prove that T(S) is dependent! (Or does it?)

Any help would be much appreciated. Thanks!


PS: I am posting this here since it is related to linear algebra, but maybe this is a homework-type question; please feel free to move it to a different forum if it doesn't belong here.
 
Physics news on Phys.org
  • #2
I moved this to the Homework & Coursework section, which is where it should go. Adding this note will bump the question.
 
  • #3
Oh, never mind; I figured it out. It turns out the statement I was trying to prove is not true...
e.g. If you have [itex]T:\mathbb{R}^2 \to \mathbb{R}, \: T(x,y) = x+y,[/itex] and [itex]S = \{(2,0),(0,2),(1,1)\},[/itex] then [itex]S[/itex] is linearly dependent but [itex]T(S) = \{2\}[/itex] is not.
 

FAQ: Linear dependence of a set under linear transformation?

What is linear dependence?

Linear dependence refers to the property of a set of vectors where one or more vectors in the set can be expressed as a linear combination of the other vectors in the set. In other words, one or more vectors in the set are redundant and do not add any new information or direction to the set.

What is a linear transformation?

A linear transformation is a function that maps one vector space to another, while preserving the operations of vector addition and scalar multiplication. In other words, it transforms a vector into a new vector in a linear manner.

How does linear dependence affect a set under linear transformation?

If a set is linearly dependent, then its image under a linear transformation will also be linearly dependent. This means that the linear transformation will not change the linear dependence of the set, as the redundant vectors will still exist in the transformed set.

What is the difference between linear independence and linear dependence?

Linear independence refers to a set of vectors that cannot be expressed as a linear combination of each other. This means that each vector in the set adds new information or direction to the set. On the other hand, linear dependence refers to a set of vectors where one or more vectors can be expressed as a linear combination of the other vectors in the set, making them redundant.

How can linear dependence be determined?

Linear dependence can be determined by using the concept of linear independence. If a set of vectors is not linearly independent, then it is linearly dependent. This can also be determined by checking if the determinant of the matrix formed by the vectors is equal to zero, as a zero determinant indicates linear dependence.

Back
Top