Ortogonal subspace proof - Leon 5.2

In summary: The Attempt at a SolutionAny hints?In summary, the homework statement asks for a subspace S of R3 that is spanned by the vectors x and y. The subspace S is the orthogonal complement of S and has dimension 1. The null set N(A) is the set of all solutions to Ax = 0. There exists z = (z1, z2, z3) orthogonal to x and y.
  • #1
IntroAnalysis
64
0

Homework Statement


Let S be a subspace of R3 spanned by the vectors x = (x1, x2, x3)T and y = (y1, y2, y3)T

Let A = (x1 x2 x3 )
( y1 y2 y3)

Show that S[tex]\bot[/tex] = N(A).

Homework Equations





The Attempt at a Solution


Any hints?
 
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  • #2
IntroAnalysis said:

Homework Statement


Let S be a subspace of R3 spanned by the vectors x = (x1, x2, x3)T and y = (y1, y2, y3)T

Let A = (x1 x2 x3 )
( y1 y2 y3)

Show that S[tex]\bot[/tex] = N(A).

Homework Equations





The Attempt at a Solution


Any hints?
Start with some definitions of the terms in this problem. Do you know what S[tex]\bot[/tex] and N(A) mean?

How much do you understand about this problem? For example, what does it mean that S is spanned by those two vectors?
 
  • #3
The subspace S spanned by the two vectors means that any vector in S can be written as a linear combination of x and y. This means x and y are linearly independent.

S[tex]\bot[/tex] is the orthogonal complement of S. It means the set w an element of R3 such that wTs= 0 for every s that's an element of S.

The null set N(A) is the set of all solutions to Ax = 0.

There exists z = (z1, z2, z3) orthogonal to x and y.

(z1, z2, z3)^T* (x1, x2, x3) = 0 ; and (z1, z2, z3)^T* (y1, y2, y3) = 0

and the nullspace of N(A) = (x1 x2 x3) * (z1, z2, z3)^T = x1z1 + x2z2 +x3z3 = 0
(y1, y2, y3) * (z1, z2, z3)^T = y1z1 + y2z2 + y3z3 = 0

Therefore, the orthogonal complement of S = N(A)

Does that work? Thanks for your assistance!
 
  • #4
IntroAnalysis said:
The subspace S spanned by the two vectors means that any vector in S can be written as a linear combination of x and y. This means x and y are linearly independent.
Not necessarily. For example, the vectors <1, 1> and <2, 2>} span a subspace of R2 but they aren't linearly independent. This subspace has dimension 1.
IntroAnalysis said:
S[tex]\bot[/tex] is the orthogonal complement of S. It means the set w an element of R3 such that wTs= 0 for every s that's an element of S.

The null set N(A) is the set of all solutions to Ax = 0.

There exists z = (z1, z2, z3) orthogonal to x and y.

(z1, z2, z3)^T* (x1, x2, x3) = 0 ; and (z1, z2, z3)^T* (y1, y2, y3) = 0

and the nullspace of N(A) = (x1 x2 x3) * (z1, z2, z3)^T = x1z1 + x2z2 +x3z3 = 0
(y1, y2, y3) * (z1, z2, z3)^T = y1z1 + y2z2 + y3z3 = 0

Therefore, the orthogonal complement of S = N(A)

Does that work? Thanks for your assistance!

You have the guts of the proof, but your presentation is on the clunky side. To show that two sets are equal, show that if u is in the first set, it must be in the second set. The show that if u is in the second set, it must also be in the first set.

So for your problem, assume that u is in N(A). You should be able to show that u is also in S[itex]\bot[/itex].
Now assume that u is in S[itex]\bot[/itex]. Then show that u must also be in N(A).
 
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1. What is an orthogonal subspace proof?

An orthogonal subspace proof is a mathematical method used to prove that two subspaces of a vector space are orthogonal to each other. This means that the dot product of any two vectors from the two subspaces is equal to zero.

2. What is the importance of orthogonal subspaces?

Orthogonal subspaces play a crucial role in linear algebra, especially in applications such as regression analysis, signal processing, and quantum mechanics. They allow for easier computation and interpretation of data, and also help to reduce the dimensionality of a problem.

3. How do you prove that two subspaces are orthogonal using the orthogonal subspace proof?

The first step is to choose a basis for each subspace. Then, take any two vectors, one from each subspace, and calculate their dot product. If the dot product is equal to zero, then the two subspaces are orthogonal. If not, repeat the process with different vectors until you either find a non-zero dot product or exhaust all possible combinations.

4. Can the orthogonal subspace proof be used for non-orthogonal subspaces?

No, the orthogonal subspace proof only works for orthogonal subspaces. If the dot product of two vectors from different subspaces is not equal to zero, then the subspaces are not orthogonal.

5. Are there any limitations to the orthogonal subspace proof?

Yes, the orthogonal subspace proof can only be used for finite-dimensional vector spaces. It also assumes that the dot product is the only inner product defined for the vector space, and that the subspaces in question are closed under addition and scalar multiplication.

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