Is Linear Independence Preserved Under Subsets?

In summary, the conversation discusses a proof for the linear independence of a set of vectors in a vector space. It is shown that a subset of a linearly independent set is also linearly independent, and that no vector in a linearly independent set can be written as a linear combination of other vectors in the set. This proof may be useful for the final exam in a Linear Algebra course.
  • #1
AlexChandler
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Homework Statement



Let V be a vector space and [tex] \{v_1,...,v_{n+1} \} \subset V [/tex] a set of linearly independent
vectors of V . Show directly: (Don't just quote a theorem!)

(a) The set [tex] \{v_1,...,v_{n} \} [/tex] is linearly independent.

(b) [tex] v_{n+1} \not \in span \{v_1,...,v_{n} \} [/tex]

Homework Equations



[tex] r_1_v_1_ + ... + r_{n+1}v_{n+1} = 0 \Rightarrow r_1=...=r_{n+1} = 0 [/tex]

The Attempt at a Solution



I have a feeling that I am doing something horribly wrong by saying this. But...

(a) We are given that

[tex] r_1_v_1_ + ... + r_{n+1}v_{n+1} = 0 \Rightarrow r_1=...=r_{n+1} = 0 [/tex]

since we know that [tex] r_{n+1} = 0 [/tex]

we must have

[tex] r_1_v_1_ + ...+ r_n v_n + 0 v_{n+1} = 0 \Rightarrow r_1=...=r_n =r_{n+1} = 0 [/tex]

then

[tex] r_1_v_1_ + ...+ r_n v_n = 0 \Rightarrow r_1=...=r_n = 0 [/tex]

(b) suppose [tex] v_{n+1} [/tex] is an element of [tex] span\{v_1,...,v_{n} \} [/tex]

then

[tex] v_{n+1} = r_1_v_1_ + ...+ r_n v_n [/tex]

then we have

[tex] r_1_v_1_ + ...+ r_n v_n - v_{n+1} =0 [/tex]

since we know that [tex] \{v_1,...,v_{n+1} \} [/tex] is linearly independent, this last equation must be impossible. Thus our initial assumption must be incorrect, and we must have:

[tex] v_{n+1} \not \in span \{v_1,...,v_{n} \} [/tex]

I feel a bit more confident on part b, but not completely. We have not really focused much on proofs this semester in Linear Algebra, but I have a feeling they will be emphasized on the final. Any comments would be much appreciated.
 
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  • #2
both of them look good to me
a) definition of linear independence, shows a subset of a linearly independent set mut aslo be linearly independent
b) definition of linear independence shows no vector in a linearly independent set can be re-written as a linear combination of others vectors in the set
 

FAQ: Is Linear Independence Preserved Under Subsets?

1. What is linear independence?

Linear independence refers to a set of vectors in a vector space that cannot be formed by a linear combination of other vectors in the same space. In other words, the vectors are not redundant and are necessary for the space they belong to.

2. How do you prove linear independence?

To prove linear independence, we must show that there is no non-trivial solution to the equation c1v1 + c2v2 + ... + cnvn = 0, where c1, c2, ..., cn are constants and v1, v2, ..., vn are the vectors in question. This can be done through various methods, such as Gaussian elimination or using the determinant of a matrix.

3. What is the importance of linear independence?

Linear independence is important in many areas of mathematics and science, particularly in linear algebra and differential equations. It allows us to determine the dimension of a vector space and to solve systems of equations. It also has applications in physics, engineering, and economics.

4. Can a set of linearly dependent vectors be linearly independent?

No, a set of linearly dependent vectors cannot be linearly independent. If a set of vectors is linearly dependent, it means that at least one vector in the set can be written as a linear combination of the other vectors. This makes the set redundant and not necessary for the vector space.

5. How does linear independence relate to span?

The span of a set of vectors is the set of all possible linear combinations of those vectors. If the vectors are linearly independent, then their span will be the entire vector space they belong to. However, if the vectors are linearly dependent, their span will be a subspace of the vector space. This shows the relationship between linear independence and the size of a vector space.

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