LA - Proof Involving Linear Independence and Spanning

In summary, the conversation discusses a proof for Theorem 2.6 in Axler's Linear Algebra text, where the length of a linearly independent list of vectors is shown to be less than or equal to the length of a spanning list in a finite-dimensional vector space. The person asks for feedback on their proof and welcomes any criticism on their proof writing skills.
  • #1
MushroomPirat
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0

Homework Statement



I was trying to prove a theorem from Axler's Linear Algebra text and my proof is different from the one in the book, and I'm wondering if someone can check whether or not my proof works, since I'm just starting to write proofs.

Theorem 2.6 (pg. 25): In a finite-dimensional vector space, the length of every linearly independent list of vectors is less than or equal to the length of every spanning list of vectors.

Homework Equations


The Attempt at a Solution



Let [itex]V[/itex] be a finite-dimensional vector space. Suppose [itex](u_1,...,u_m)[/itex], where [itex]u_1,...,u_m \in V[/itex] is a list of linearly independent vectors and [itex] (w_1,...,w_n)[/itex] where [itex]w_1,...,w_n \in V[/itex] spans [itex]V[/itex]. We need to prove that [itex]m \leq n[/itex]. Suppose [itex]m>n[/itex]. Because the list [itex] (w_1,...,w_n)[/itex] spans V, there exist scalars [itex]a_{i,j}[/itex] where [itex]i \in [1,m], j \in [1,n][/itex] such that [itex]u_i=a_{i,1}w_1+...+a_{i,n}w_n[/itex]. Thus, the vectors in [itex](u_1,...,u_m)[/itex] can be replaced by these linear combinations. However, since [itex]m>n[/itex], there exists some [itex]k[/itex] where [itex]m<k\leq n[/itex] such that [itex]u_k[/itex] can be written in terms of [itex]u_1,...,u_{k-1}[/itex], and thus [itex](u_1,...,u_m)[/itex] is a linearly dependent list, and we have reached a contradiction. Therefore, [itex]m\leq n[/itex].

If someone could please look this over that would be great. Also, I would really like any criticism on my logic or proof writing skills in general since I have learned proofs on my own and no one has really seen my proofs before.

Thanks for your help :)
 
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  • #2
i think that's looking good and so is the logic ;)
 
  • #3
That's good to hear I'm on the right track. Thanks for taking a look at it :)
 

1. What is linear independence?

Linear independence is the property of a set of vectors where none of the vectors in the set can be written as a linear combination of the others. In other words, each vector in the set contributes a unique direction to the span of the set.

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

To determine if a set of vectors is linearly independent, you can use the following steps:

  1. Set up a system of equations where each vector in the set is a variable.
  2. Solve the system of equations using Gaussian elimination or another method.
  3. If the only solution is when all the variables are equal to zero, then the set of vectors is linearly independent.

3. What is spanning?

Spanning is the property of a set of vectors where the linear combinations of the vectors can reach any point in a vector space. In other words, the span of the set is the set of all possible linear combinations of the vectors in the set.

4. How do you determine if a set of vectors spans a vector space?

To determine if a set of vectors spans a vector space, you can use the following steps:

  1. Select a vector in the vector space that is not in the set.
  2. Set up a system of equations where the coefficients of the vectors in the set are the variables and the selected vector is the solution.
  3. Solve the system of equations using Gaussian elimination or another method.
  4. If a solution is found, then the set of vectors spans the vector space. If no solution is found, then the set of vectors does not span the vector space.

5. What is the relationship between linear independence and spanning?

The relationship between linear independence and spanning is that a set of vectors can only span a vector space if the vectors are linearly independent. If the vectors are not linearly independent, then there will be redundancies in the span of the set, and it will not be able to reach all points in the vector space.

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