Tyler's question at Yahoo Answers (linear independence)

In summary, given a basis of a real vector space $V$, if the set of vectors $\{w_1, ... w_m\}$ is linearly independent in $V$, then the set of their corresponding coordinate vectors $\{[w_1]_B, ... [w_m]_B\}$ is also linearly independent in $\mathbb{R}^n$.
  • #1
Fernando Revilla
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Here is the question:

Show that if {w1, ... wm} is linearly independent in V, then {[w1]B , ... [wm]B} is linearly independent in Rn.

Here is a link to the question:

Linear algebra help please please? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
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  • #2
Hello Tyler,

Fixing a basis $B=\{w_1,\ldots,w_m\}$ of a real vector space $V$ we know that

$(i)\;[x+y]_B=[x]_B+[y]_B$ or all $x,y\in V$.
$(ii)\;[\lambda x]_B=\lambda [x]_B$ for all $\lambda \in \mathbb{R}$.
$(iii)\;[x]_B=(0,\ldots ,0)\Leftrightarrow x=0$.

Suppose $\lambda_1 [w_1]_B +\ldots +\lambda_m [w_m]_B=(0,\ldots ,0)$. Using $(i)$ and $(ii)$:
$$[\lambda_1 w_1 +\ldots +\lambda_m w_m]_B=(0,\ldots, 0)$$
Using $(iii)$: $\lambda_1 w_1 +\ldots +\lambda_m w_m=0$.

By hypothesis $ \{w_1,\ldots ,w_m\}$ are linearly independent, so $\lambda_1=\ldots =\lambda_m=0$. This proves that $\{[w_1]_B,\ldots,[w_m]_B\}$ are linearly independent.
 

Related to Tyler's question at Yahoo Answers (linear independence)

1. What is linear independence?

Linear independence is a concept in linear algebra that refers to a set of vectors being able to uniquely represent any other vector in a given vector space. In other words, no vector in the set can be expressed as a linear combination of the other vectors in 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 perform row reduction on the matrix formed by the vectors. If the reduced row echelon form of the matrix has a pivot in every column, then the vectors are linearly independent. Alternatively, you can also check if the determinant of the matrix formed by the vectors is non-zero.

3. What is the importance of linear independence in mathematics?

Linear independence is important in mathematics because it allows us to understand and manipulate vector spaces. It also has applications in various fields such as engineering, physics, and computer science.

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

No, a set of linearly dependent vectors cannot be linearly independent. This is because if one vector in the set can be expressed as a linear combination of the other vectors, then the set is not unique in representing all vectors in the given vector space.

5. How does linear independence relate to linear transformations?

Linear independence is closely related to linear transformations because it determines the number of dimensions in the image or range of a linear transformation. If a set of vectors is linearly independent, then the linear transformation will have the same number of dimensions as the vector space it maps from. If the vectors are linearly dependent, then the linear transformation will have a lower dimension in its image or range.

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