Dimension of subspace of V^n with orthogonal vectors

In summary, in a space V^n, the set of all vectors that are orthogonal to any non-zero vector v form a subspace of dimension (n-1). This can be proven by extending v to a basis of V^n and showing that the subspace spanned by the remaining vectors in the basis is equal to the space of vectors orthogonal to v.
  • #1
raphael3d
45
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in a space V^n, prove that the set of all vectors {v1,v2,..}, orthogonal to any v≠0, form a subspace V^(n-1).

i know that a subspace of V^n must be at least one dimension less and the set of vector v1,v2,... build a orthogonal basis, but how can one show with this preconditions that the subspace has to be of dimension (n-1)?
 
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  • #2
For a fixed non-zero vector v extend (v) to a basis [itex](v,b_2,b_3,\ldots,b_n)[/itex] of V^n. Now what is the dimension of [itex]\text{Span}(b_2,\ldots,b_n)[/itex] and how this space relate to all vectors orthogonal to v?
 
  • #3
so would v1+ span(b2,...,bn) be a subspace of v^n ?
but why should (v,b2,b3,...,bn) be a basis, when v isn't normalized to a unit vector?

thanks rasmhop for your reply
 
  • #4
raphael3d said:
so would v1+ span(b2,...,bn) be a subspace of v^n ?
but why should (v,b2,b3,...,bn) be a basis, when v isn't normalized to a unit vector?

thanks rasmhop for your reply

I'm not talking about an orthonormal basis or anything like that, just a basis. In general in a vector space V of dimension n, for m linearly independent vectors [itex]b_1,\ldots,b_m[/itex] we can find vectors [itex]b_{m+1},\ldots,b_n[/itex] such that [itex]b_1,\ldots,b_n[/itex] is a basis for V.

The idea of my first reply was that we let [itex]W = \text{Span}(b_2,\ldots,b_n)[/itex] and let W' be the vector space of vectors orthogonal to v. Now it's possible to show W=W' by showing that if a vector is in W', then it must be in W; and if a vector is in W, then it is in W'.
 

What is the definition of a subspace in linear algebra?

A subspace in linear algebra is a subset of a vector space that satisfies three conditions: it contains the zero vector, it is closed under vector addition, and it is closed under scalar multiplication.

How do you determine the dimension of a subspace?

The dimension of a subspace is equal to the number of linearly independent vectors that span the subspace. This can be found by using row reduction on a matrix of the vectors, and then counting the number of non-zero rows in the reduced matrix.

What are orthogonal vectors?

Orthogonal vectors are vectors that are perpendicular to each other, meaning their dot product is equal to 0. In other words, they form a 90-degree angle with each other.

What is the significance of orthogonal vectors in determining the dimension of a subspace?

Orthogonal vectors are important in determining the dimension of a subspace because they are linearly independent. This means that they can span a subspace without any redundancy, making it easier to determine the dimension.

Can a subspace have more than one orthogonal basis?

Yes, a subspace can have multiple orthogonal bases. This is because any multiple of an orthogonal basis will also be orthogonal, and thus can be used as a basis for the same subspace.

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