Prove Linear Independence of {[w1]s, [w2]s,...,[wk]s} in V

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In summary, the conversation discusses the linear independence of sets of vectors in a vector space and the uniqueness of the transition matrix between two ordered bases. It is shown that if {w1, w2, ..., wk} is a linearly independent set in a vector space V, then {[w1]s, [w2]s, ..., [wk]s} is also linearly independent in R^n. For the second part, the approach is to show that if A and B both satisfy A[v]T = [v]S and B[v]T = [v]S for all v in V, then A = B. The direction for this proof is to show that A and B have the same coefficients when written
  • #1
hkus10
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1) Let S be an ordered basis for n-dimensional vector space V. Show that if {w1, w2, ..., wk} is a linearly independent set of vectors in V, then {[w1]s, [w2]s,...,[wk]s} is a linearly independent set of vectors in R^n.

What I got so far is
w1 = a1V1 + a2V2 + ... + anVn
so, [w1]s =
[a1
a2
...
an]

The same thing for w2, [w2]s and wk, [wk]s.
My question how to go from there?

2) Let S and T be two ordered bases of an n-dimensional vector space V. Prove that the transition matrix from T - coordinates to S - coordinates is unique. That is, if A,B belong to Mnn both satisfy A[v]T = [V]S and B[V]T = [v]S for all v belong to V, then A = B.

My approach for this question is that
Let S = {v1, v2, vn}
Let T = {w1, w2, wn}
Av = a1v1+a2v2+...+anvn
v = b1w1+b2w2+...+bnwn
Aa1v1 + Aa2v2+ ... +Aanvn
a1(Av1) + a2(Av2)+...+an(Avn)
= b1w1+b2w2+...+bn(wn)

Am I going the right direction? If no, how should I approach? If yes, how should I move from here?
 
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  • #2
what does [w1]s mean? w1 written in terms of the ordered basis S?
 
  • #3
lanedance said:
what does [w1]s mean? w1 written in terms of the ordered basis S?

[w1]s mean the s-coordinate vector of w1? for the second part,yes.
 

Related to Prove Linear Independence of {[w1]s, [w2]s,...,[wk]s} in V

What is meant by "Linear Independence"?

Linear independence refers to a set of vectors in a vector space that cannot be expressed as a linear combination of each other. In other words, none of the vectors in the set can be written as a combination of the others, making them essential and unique to the set.

Why is proving linear independence important?

Proving linear independence is crucial in linear algebra because it helps determine if a set of vectors can serve as a basis for a vector space. If a set of vectors is linearly independent, it means that they are necessary to span the entire vector space, making them a suitable basis for representing any vector in that space.

How do you prove linear independence?

To prove linear independence, you need to show that the only solution to the linear combination of the vectors in the set is the trivial solution (where all coefficients are equal to zero). This can be done through various methods, such as using Gaussian elimination or the determinant test.

What is the difference between linear independence and linear dependence?

Linear dependence refers to a set of vectors that can be expressed as a linear combination of each other. This means that at least one of the vectors in the set is not necessary to span the vector space. In contrast, linear independence means that all vectors in the set are essential and cannot be written as a linear combination of each other.

Can a set of only two vectors be linearly independent?

Yes, a set of two vectors can be linearly independent if they are not scalar multiples of each other. This means that they cannot be scaled to equal each other, and they are necessary to span the vector space. However, for a set of two vectors to be linearly independent, they must be in a three-dimensional vector space or higher. In a two-dimensional vector space, at least three vectors are needed for linear independence.

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