Dimension and basis for subspace determined by given vectors

In summary, the problem asks to determine the dimension and basis of a linear subspace formed by a set of vectors in an n-dimensional vector space. After trying two approaches, it is found that the dimension is n and the basis is the set of given vectors. However, the key to the problem states that the dimension should actually be n-1 and provides a different basis. After considering the key in a 3D vector space, it is understood that excluding one vector will result in a linearly independent set. Another approach is to show that the matrix with the new vectors as columns is non-singular. Both methods lead to the same conclusion and provide a solution to the problem.
  • #1
Combinatus
42
1

Homework Statement



Assume that [tex]e_1 ,..., e_n[/tex] is a basis for the vector space V. Let W be the linear subspace determined (formed?) by the vectors [tex]e_{1}-e_{2}, e_{2}-e_{3}, ..., e_{n-1}-e_{n}, e_{n}-e_{1}[/tex]. Determine the dimension of W, and a basis for W.

Homework Equations





The Attempt at a Solution



After trying a two separate (and somewhat lengthy) approaches, both yielded that the dimension of W is n, and [tex]e_{1}-e_{2}, e_{2}-e_{3}, ..., e_{n-1}-e_{n}, e_{n}-e_{1}[/tex] forms the basis for W, i.e. no manipulation needed since the aforementioned vectors should already be linearly independent.

The key to the problem states that the subspace states that the dimension should rather be n-1, and the basis [tex]e_{1}-e_{2}, e_{2}-e_{3}, ..., e_{n-1}-e_{n}[/tex].

After considering the key applied to a 3D vector space with the basis [tex]e_1, e_2, e_3[/tex], the key makes sense, since [tex]e_3-e_1[/tex] will be parallel to the plane formed by [tex]e_1-e_2[/tex] and [tex]e_2-e_3[/tex]. I'm not certain how I should apply this knowledge to n-dimensional space.
 
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  • #2
I figured it out. Basically, I showed that the vectors [tex]e_1-e_2, e_2-e_3, ... , e_n-e_1[/tex] will for any dimension be linearly dependent (since the vector sum of all of them will point back to the origin), and excluding one made it possible to show that the new set of vectors will be linearly dependent. It was a reasonably entertaining problem though (which wasn't too hard after all), so if you want to give it a shot, or try a different approach, go for it!
 
  • #3
Another way to do this is to note that the matrix having the new vectors as columns has all "1"s along the main diagonal and "-1" just below the main diagonal. Then it's easy to show that this matrix is non-singular so the vectors are independent. But your method is perfectly good.
 

1. What is the dimension of a subspace determined by given vectors?

The dimension of a subspace determined by given vectors is the number of vectors required to span the subspace. It is also equal to the number of linearly independent vectors in the subspace.

2. How is the basis of a subspace determined by given vectors?

The basis of a subspace determined by given vectors is a set of linearly independent vectors that span the subspace. It can be found by reducing the given vectors to row-echelon form and selecting the pivot columns as the basis vectors.

3. Can the dimension of a subspace be greater than the number of given vectors?

Yes, the dimension of a subspace can be greater than the number of given vectors. This can happen if the given vectors are not linearly independent, or if they do not span the entire subspace.

4. What is the relationship between the dimension and basis of a subspace determined by given vectors?

The dimension and basis of a subspace determined by given vectors are closely related. The dimension is equal to the number of basis vectors, and the basis vectors are linearly independent and span the subspace.

5. How can the dimension and basis of a subspace be used in solving systems of linear equations?

The dimension and basis of a subspace can be used to determine the number of solutions to a system of linear equations. If the dimension of the subspace is equal to the number of variables in the system, then there is a unique solution. If the dimension is less than the number of variables, then there are infinitely many solutions. And if the dimension is greater than the number of variables, then there are no solutions.

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