Basis and Subspace Help: Exploring the Relationship between Vectors e and d

In summary, if you have a 2-D vector space E and a 2-D vector space D, and you know that e1'd1 = 0, e2'd2 = 0, and e3'd3 = 0, then there are no conclusions that can be made about E and D.
  • #1
Sue_2010
8
0
Suppose I have 3 vectors e1, e2, e3 that spans the subspace E, another 3 vectors d1, d2, d3 that spans the subspace D. If I also know that e1’d1 = 0, e2’d2 = 0, e3’d3 = 0, are there any conclusions I can make in terms of E and D? like row(E) = null(D)?
 
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  • #2
what is row(E) and null(D)...E and D are subspaces, not matrices. If you mean E=[e1 e2 e3] and D=[d1 d2 d3], you can't really make any judgements, to conclude that row(E)=null(D) you'd have to know that ei'dj=0 for all i,j=1,2,3.
 
  • #3
Actually, I was wrong. Even then all you would get is that [tex]D \subset \operatorname{null} E^\top[/tex] and similarly [tex]E \subset \operatorname{null} D^T[/tex]. The questions of whether or not we may even have row(E)=null(D) depends on the ambient space, because in general the dimensions of these two subspaces need not agree even if E and D are both 3-dimensional. Apologies for the mix up.
 
Last edited:
  • #4
Thank you for your reply!

You are right, E and D are matrices. Let me state my question more clearly.

I want to find a projection that maximally preserves the Euclidean distance between two points (vectors in general). Consider a 2-D example as show in the figure. The direction that maximally preserves the distance between c1 and c2 will be (c2-c1), or I can say it's the row space for matrix E = [(c2-c1)].
http://www.personal.psu.edu/sxy162/temp/1.jpg

If c2 and c1 have the same norm, then (c2+ c1) is perpendicular to (c2 - c1), or (c2-c1)*(c2+c1)' = 0. Therefore, instead of projecting to row of matrix E = [(c2-c1)], I can project to the null space of matrix D = [(c2 + c1)], right? It only works if all the vectors are normalized.

Now my question is about more general problem. Say
E = [
(c1,1 - c1,2)
(c2,1 - c2, 2)
...
(cn,1 - cn, 2)
]

and

D =
[
(c1,1 + c1,2)
(c2,1 + c2, 2)
...
(cn,1 + cn, 2)
]

Now, matrices E and D each has n rows, instead of 1 row in the first example.

The question is, can I still say that, projecting to null(D) is equivalent to projecting to row(E)?
 
Last edited by a moderator:
  • #5
Hi rochfor1,

Now I understand that it's not right to compare null(D) and row(E), since they are of different dimensions.

Can we compare the projections to null(D) and row(E) in terms of their effects to the separability of the pairs of vectors, (c11 , c12) ... (cn1 , cn2) I mentioned in the above thread?
 
  • #6
And how do you get D in null(E') and E in null(D') ?
 
  • #7
Hello everyone,

I really want to get some feedback. Thank you!
 

1. What is a basis in linear algebra?

A basis in linear algebra is a set of linearly independent vectors that span a vector space. This means that any vector in the vector space can be written as a linear combination of the basis vectors. A basis is an essential concept in linear algebra as it allows us to represent and manipulate vectors in a more efficient way.

2. How do you find a basis for a subspace?

To find a basis for a subspace, we first need to determine the dimension of the subspace. Then, we can choose any linearly independent set of vectors in the subspace and check if they span the entire subspace. If they do, then this set of vectors is a basis for the subspace. If not, we can add more vectors to the set until we have a set of linearly independent vectors that span the subspace.

3. What is the difference between a basis and a subspace?

A basis is a set of linearly independent vectors that span a vector space, while a subspace is a subset of the vector space that satisfies certain properties (such as being closed under addition and scalar multiplication). In other words, a basis is a set of vectors that represent the entire vector space, while a subspace is a smaller subset of the vector space.

4. Why is it important to understand basis and subspace in linear algebra?

Basis and subspace are fundamental concepts in linear algebra, and understanding them is crucial in order to solve more complex problems in the subject. By understanding basis and subspace, we can easily represent and manipulate vectors in a more efficient way, and also solve problems related to linear transformations and systems of linear equations.

5. What are some common applications of basis and subspace in real life?

Basis and subspace have a wide range of applications in various fields such as computer graphics, data compression, and signal processing. In computer graphics, basis and subspace are used to represent and manipulate 3D objects and animations. In data compression, basis and subspace techniques are used to reduce the size of data without losing important information. In signal processing, basis and subspace are used to analyze and manipulate signals for various applications such as noise reduction and image enhancement.

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