Linear Independence of Subsets: Necessary and Sufficient Conditions

In summary, the necessary and sufficient condition for the set S=\left\{b, v_{2}, \cdots, v_{n}\right\} to be linearly independent is that \alpha_{1} \neq 0, while the rest of the scalars \alpha_{2}, \cdots, \alpha_{n} can be any elements of the field F. This condition ensures that none of the vectors in the set S can be expressed as a linear combination of the other vectors in the set.
  • #1
radou
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Let V be a vector space over a field F, [tex]v_{1}, \cdots, v_{n} \in V[/tex] and [tex]\alpha_{1}, \cdots, \alpha_{n} \in F[/tex]. Further on, let the set [tex]\left\{v_{1}, \cdots, v_{n}\right\}[/tex] be linearly independent, and b be a vector defined with [tex]b=\sum_{i=1}^n \alpha_{i}v_{i}[/tex]. One has to find necessary and sufficient conditions on the scalars [tex]\alpha_{1}, \cdots, \alpha_{n}[/tex] such that the set S=[tex]\left\{b, v_{2}, \cdots, v_{n}\right\}[/tex] is linearly independent, too.

Well, I just used a simple proposition which states that a set is dependent if there exists at least one vector from that set which can be shown as a linear combination of the rest of the vectors from the same set. So, obviously, for [tex]\alpha_{1} = 0[/tex], the set S is dependent, which makes [tex]\alpha_{1} \neq 0[/tex] a necessary condition for S to be independent. Further on, a sufficient condition would be [tex]\alpha_{1} = \cdots = \alpha_{n} = 0[/tex], which leaves us with the set S\{b}. This set must be linearly independent, since it is a subset of {v1, ..., vn}, which we know is linearly independent.

I may be boring, but I'm just checking if my reasoning is allright.. :smile:

Edit. I just realized, for [tex]\alpha_{1} = \cdots = \alpha_{n} = 0[/tex] we have b = 0, which makes the set dependent! So [tex]\alpha_{1} \neq 0[/tex] is a necessary condition, but what's the sufficient condition? Is it that at least one of the scalars [tex]\alpha_{2}, \cdots, \alpha_{n}[/tex]must not be equal zero?
 
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  • #2
The sufficient condition certainly includes this [tex]\alpha_{1} \neq 0[/tex]

Basically, if a condition is necessary and sufficient, it means that the statement is true iff the condition is true. So search for a set of conditions that are true iff the set b, v 2-n is linearly independent.

Note that if all the alphas except [tex] \alpha_1[/tex] are zero, then b is just a multiple of v1, and the new set is certainly linearly independent
 
  • #3
Yes, I realized that.. So, it seems [tex]\alpha_{1} \neq 0[/tex] is both necessary and sufficient condition, since the rest of the scalars can be any elements of F, all zero, all non zero, or combined.
 
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FAQ: Linear Independence of Subsets: Necessary and Sufficient Conditions

What is linear independence?

Linear independence is a concept in linear algebra that describes a set of vectors in a vector space that cannot be expressed as a linear combination of other vectors in that space.

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

A set of vectors is linearly independent if and only if the only solution to the equation c1v1 + c2v2 + ... + cnvn = 0, where c1, c2, ..., cn are coefficients and v1, v2, ..., vn are the vectors in the set, is c1 = c2 = ... = cn = 0.

What is the importance of linear independence in linear algebra?

Linear independence is important in linear algebra because it allows us to determine if a set of vectors is a basis for a vector space. It also helps us to simplify calculations and solve systems of linear equations.

Can a set of vectors be linearly dependent in one vector space and linearly independent in another?

Yes, it is possible for a set of vectors to be linearly dependent in one vector space and linearly independent in another. This depends on the dimension and span of the vector spaces.

How is linear independence related to the concept of span?

The span of a set of vectors is the set of all possible linear combinations of those vectors. Linear independence is related to span because a set of linearly independent vectors can span a vector space, while a set of linearly dependent vectors cannot span a vector space.

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