Linear algebra question: Orthogonal subspaces

In summary, for the given matrices, a basis for the nullspace N(A) can be written as (-4β, 3β)T, where β is a free variable. This is equivalent to the basis vector given in the book, which is (-4,3)T.
  • #1
Mdhiggenz
327
1

Homework Statement



For each of the following matrices, determine a basis for each of the subspaces N(A)

A=[3 4]
[ 6 8]

Homework Equations


The Attempt at a Solution

So reducing it I got [1 4/3]
[0 0]

I know x2 is a free variable

I set x2 = to β

and found my N(A)=(-4/3β,β)T

However the book has simply (-4,3)T

Am I incorrect?
 
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  • #2
What would have happened if you had set ##x_2=3\beta##?
 
  • #3
We get -4B
 
  • #4
To find a basis for the nullspace is to find a minimal set of vector(s) that span your nullspace. If the nullspace is [itex] \binom{-4}{3} [/itex] as the book says, then any member of the nullspace can be written as a multiple of [itex] \binom{-4}{3} [/itex]. This means that the general form of a vector in N(A) is [itex] \binom{-4β}{3β} [/itex], which is equivalent to what you have. So you have written out the general form of a vector in the nullspace (assuming that your β is a free variable), whereas your book just gave the basis vector.
 
Last edited:
  • #5
Awesome thanks!
 

1. What is the definition of orthogonal subspaces?

Orthogonal subspaces are two subspaces in a vector space that are perpendicular to each other, meaning that their basis vectors are all orthogonal (perpendicular) to each other.

2. How can I identify if two subspaces are orthogonal?

To identify if two subspaces are orthogonal, you can check if their basis vectors are all orthogonal to each other. You can also use the dot product to see if all the vectors in one subspace are orthogonal to all the vectors in the other subspace.

3. What is the significance of orthogonal subspaces in linear algebra?

Orthogonal subspaces are important in linear algebra because they have many useful properties. For example, if two subspaces are orthogonal, then their intersection is only the zero vector. This can be used to simplify calculations and proofs in linear algebra.

4. Can orthogonal subspaces be non-trivial?

Yes, orthogonal subspaces can be non-trivial. For example, in a 3-dimensional vector space, two 2-dimensional subspaces can be orthogonal to each other. In this case, the intersection of the two subspaces would be a 1-dimensional subspace.

5. How are orthogonal subspaces related to linear independence?

Orthogonal subspaces are related to linear independence because if two subspaces are orthogonal, then the basis vectors of each subspace must be linearly independent. This means that the subspaces themselves are also linearly independent.

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