Lenear algebra independance theoretical question

Is it at least four?In summary, the conversation discusses a vector space V=Q^4 over Q and a series of vectors (v1, v2, v3, v4, v5) that spans V. The question is whether the subset (v1, v2, v3, v4) is a basis for V, and if not, to provide a counter example. It is suggested that v5 may be a linear combination of the other vectors. The properties of a basis are also mentioned, and it is noted that if any two are true, the third must be as well. The question of whether V has a dimension of at least four is posed.
  • #1
transgalactic
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there is a space V=Q^4 over Q .
there is a series of vectors (v1 ,v2 ,v3,v4,v5)
that spans V
does the series
(v1 ,v2 ,v3,v4) is a basis of V?
if it is explain
if not give a counter example??

we could have that v5 is a linear combination of others.

??
 
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  • #2
transgalactic said:
there is a space V=Q^4 over Q .
there is a series of vectors (v1 ,v2 ,v3,v4,v5)
that spans V
does the series
(v1 ,v2 ,v3,v4) is a basis of V?
if it is explain
if not give a counter example??

we could have that v5 is a linear combination of others.

??
A basis for a vector space has three properties.
1) It spans the space.
2) The vectors in it are linearly independent.
3) The number of vectors in it is the same as the dimension of the vector space.

Further, if any two of those are true, the third must be. You are told that {v1, v2, v3, v4} spans V and you can certainly see that there are four vectors in that set. What is the dimension of V?
 

FAQ: Lenear algebra independance theoretical question

What is linear algebra independence?

Linear algebra independence refers to the relationship between vectors in a vector space. A set of vectors is considered to be linearly independent if none of the vectors in the set can be written as a linear combination of the other vectors in the set. In other words, no vector in the set is redundant or can be expressed as a combination of the others.

Why is linear algebra independence important?

Linear algebra independence is important because it allows us to determine the dimension of a vector space and to solve systems of linear equations. It is also a fundamental concept in the study of linear transformations and their properties.

What is the difference between linear independence and linear dependence?

Linear independence and linear dependence are opposite concepts. As mentioned, a set of vectors is linearly independent if none of the vectors can be expressed as a combination of the others. Conversely, a set of vectors is linearly dependent if at least one vector in the set can be expressed as a combination of the others.

How can I determine if a set of vectors is linearly independent?

To determine if a set of vectors is linearly independent, you can use the definition of linear independence or perform a linear independence test. One way to do this is to create a matrix using the vectors as columns, and then use row operations to reduce the matrix to its echelon form. If there are any zero rows in the echelon form, the vectors are linearly dependent. If there are no zero rows, the vectors are linearly independent.

What is the role of linear independence in solving systems of linear equations?

Linear independence plays a crucial role in solving systems of linear equations. In a system of linear equations, the number of equations must be equal to or greater than the number of variables for a unique solution to exist. The concept of linear independence allows us to determine the number of independent equations in a system, which helps us determine if a unique solution exists or not.

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