Proving Linear Independence: If v\notin\left\langlev1,...,vk\right\rangle

In summary, the conversation discusses proving that if a set of vectors v1,...,vk,v are linearly independent, then v is not in the span of the set v1,...,vk. The person asking the question is curious about a direct proof using the given statement "If v1,...,vk,v are linear independent". The other person suggests using proof by contradiction. They also clarify that the order of the vectors in the set does not matter, as long as v is included in the set.
  • #1
annoymage
362
0

Homework Statement



If v11,...,vk,v are linear independent, prove that v[tex]\notin[/tex]

[tex]\left\langle[/tex]v1,...,vk[tex]\right\rangle[/tex]


Homework Equations



n/a

The Attempt at a Solution



i can prove it by contrapositive, but I'm curious how to proof it with

"If v11,...,vk,v are linear independent" in the beginning,

any idea? T_T
 
Last edited:
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  • #2
I really cannot read what you have here.

Are you trying to show that "if [itex]\{v_1, v_2, \cdot\cdot\cdot, v_k, v\}[/itex] is linearly independent, then v is not in the span of [itex]\{v_1, v_2, \cdot\cdot\cdot, v_k\}"?

Since proof by contradiction works nicely, why look for a "direct" proof?
 
  • #3
i'm just curious, maybe there is a way that i don't know,

anyway, you wrote

"[itex]
\{v_1, v_2, \cdot\cdot\cdot, v_k, v\}
[/itex] is linearly independent"

is it the same thing as "[itex]
v_1, v_2, \cdot\cdot\cdot, v_k, v
[/itex] are linear independent" ??
 

Related to Proving Linear Independence: If v\notin\left\langlev1,...,vk\right\rangle

1. What is linear independence?

Linear independence is a property of a set of vectors in a vector space, where no vector in the set can be expressed as a linear combination of the other vectors. In other words, no vector in the set is redundant and each vector contributes a unique direction to the vector space.

2. How do you prove linear independence?

To prove linear independence, you can use the definition of linear independence or the linear dependence lemma. This involves writing out the vectors as a linear combination and setting them equal to zero, then solving for the coefficients. If the only solution is the trivial solution (all coefficients equal to zero), then the vectors are linearly independent.

3. What does the notation "v∉⟨v1,...,vk⟩" mean?

This notation means that the vector v is not in the span of the vectors v1,...,vk. In other words, v cannot be expressed as a linear combination of v1,...,vk, and therefore is linearly independent from them.

4. Why is proving linear independence important?

Proving linear independence is important because it helps us determine whether a set of vectors is a basis for a vector space. If a set of vectors is linearly independent, then it spans the entire vector space and can be used to express any vector in that space. Additionally, linearly independent vectors are easier to work with in calculations and can help simplify problems.

5. What are some real-world applications of proving linear independence?

Proving linear independence has various applications in fields such as physics, engineering, and computer science. For example, in physics, linear independence is used to determine the fundamental forces acting on a system. In engineering, it is used to analyze the stability of structures. In computer science, it is used in algorithms for data compression and solving systems of equations.

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