Vector Space Problem: Maximal Linear Independence & Minimal Spanning Set

In summary, we are trying to show that if x1,...xn are a maximal linearly independent subset of a vector space V, then they also form a minimal spanning set of V. This means that they not only span V, but removing any one of them would result in a set that does not span V. We can prove this by showing that any vector in V can be written as a linear combination of x1,...xn, but removing any of them would result in a vector that cannot be written as a combination of the remaining vectors.
  • #1
ak123456
50
0

Homework Statement


Let V be a vector space over a field F and let x1,...xn [tex]\in[/tex]V.Suppose that x1,...xn form a maximal linearly independent subset of V. Show that x1,...xn form a minimal spanning set of V.

Homework Equations





The Attempt at a Solution


I knew that x1,...xn are linear independent and for every x[tex]\in[/tex]V the n+1 vectors x1,...xn , x are linear dependent
then x span x1,...xn
i don't how to continue
any help ?
 
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  • #2
ak123456 said:

Homework Statement


Let V be a vector space over a field F and let x1,...xn [tex]\in[/tex]V.Suppose that x1,...xn form a maximal linearly independent subset of V. Show that x1,...xn form a minimal spanning set of V.

Homework Equations





The Attempt at a Solution


I knew that x1,...xn are linear independent and for every x[tex]\in[/tex]V the n+1 vectors x1,...xn , x are linear dependent
then x span x1,...xn
i don't how to continue
any help ?

hey ak123456

first you have only n vectors

what are your defintions for maximally lineraly independent and minimal span? always a good place to start

to get you started though, the my reasoning would be as follows:
as {x1,...,xn} is maximal lineraly independent show any vector in V can be written as combination of xn's, so {x1,...,xn} spans V.
then try and show if you remove an xi, the remaining vectors no longer span V...
 
  • #3
If {x1, ..., xn} did not span the set, there must exist some x which cannot be written as a combination of the {x1, ..., xn}. What does that tell you about {x1, x2, ..., xn, x}?

If it were not a minimal spanning set, then there must exist a smaller set, {y1, y2,... yn-1} which did span the set. That would mean you could write each of x1, x2, ... xn in terms of those y's. What does that tell you about the independence of x1, x2, ..., xn?
 

Related to Vector Space Problem: Maximal Linear Independence & Minimal Spanning Set

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors that can be added and multiplied by scalars. It is a fundamental concept in linear algebra and is used to describe the properties and relationships of vectors in a mathematical system.

2. What is maximal linear independence?

Maximal linear independence refers to a set of vectors in a vector space that cannot be expressed as a linear combination of other vectors in the set. In other words, the vectors are not redundant and are necessary to span the entire vector space.

3. What is a minimal spanning set?

A minimal spanning set is the smallest set of vectors in a vector space that can generate the entire space through linear combinations. This means that every vector in the space can be written as a linear combination of the vectors in the minimal spanning set.

4. 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 determinant or rank method. The determinant method involves arranging the vectors as columns in a matrix and calculating the determinant. If the determinant is non-zero, then the vectors are linearly independent. The rank method involves forming a matrix with the vectors as rows and reducing it to row-echelon form. If the number of non-zero rows is equal to the number of vectors, then the vectors are linearly independent.

5. How can you find the minimal spanning set for a given vector space?

To find the minimal spanning set for a given vector space, you can use the Gaussian elimination method or the Gram-Schmidt process. The Gaussian elimination method involves reducing a matrix of the vectors to row-echelon form and selecting the non-zero rows as the minimal spanning set. The Gram-Schmidt process involves orthogonalizing the set of vectors and selecting the resulting orthogonal vectors as the minimal spanning set.

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