Finite dimensional real vector space

In summary, the conversation is about finding the orthogonal complement of a subspace in a finite dimensional real vector space with an inner product. The task is to prove that the orthogonal complement is also a subspace, the intersection of the subspace and its orthogonal complement is only the zero vector, and the sum of their dimensions is equal to the dimension of the original vector space. The first part is completed, but help is needed for the other parts. The solution for (b) is provided, and for (c), the suggestion is to choose a basis for W and W^0 and show that their union is a basis for V.
  • #1
Benny
584
0
Hi can someone assist me with the following question?

Q. Let V be a finite dimensional real vector space with inner product < , > and let W be a subspace of V. Then the orthogonal complement of W is defined as follows.

[tex]
W^o = \{ v \in V: < v,w > = 0,w \in W\}
[/tex]

Prove the following:

a) [tex]W^o[/tex] is a subspace of V.
b) [tex]W \cap W^o = \left\{ {\mathop 0\limits^ \to } \right\}[/tex]
c) [tex]\dim W + \dim W^o = \dim V[/tex]

My working:

I can do the first part but the others are a problem for me.

b) W and W^o are both subspaces of V and so they both contain the zero vector. Then their intersection also contains the zero vector. Suppose the intersection contains some non-zero vector say f. Then we must have <f,f> = 0 for some non-zero vector f. But this contradicts some inner product property which says <f,f> = 0 iff f = zero vector. So from that I conclude that [tex]W \cap W^o = \left\{ {\mathop 0\limits^ \to } \right\}[/tex].

c) I can't think of a way to do this one. I know that dim(V) >= dim(W), dim(W_0) because any linearly independent set in V has most k elements where k is the number of vectors in a basis for V.

Can someone help me with part c or check my answer for part b? Any help appreciated.
 
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  • #2
Your answer to (b) is completely correct.

To do (c), choose a basis for [tex]W[/tex] and a basis for [tex]W^0[/tex].
Show that their union is a basis for [tex]V[/tex].
 
  • #3
Ok thanks for your help HallsofIvy, I'll give that a go.
 

FAQ: Finite dimensional real vector space

What is a finite dimensional real vector space?

A finite dimensional real vector space is a mathematical structure that consists of a set of vectors, along with operations of addition and scalar multiplication, that satisfy certain properties. These properties include closure under addition and scalar multiplication, as well as associativity, commutativity, and distributivity. The vectors in a finite dimensional real vector space are typically represented as arrows with a specific length and direction.

What are the basic properties of a finite dimensional real vector space?

The basic properties of a finite dimensional real vector space include closure under addition and scalar multiplication, associativity, commutativity, and distributivity. In addition, a finite dimensional real vector space must contain a zero vector, which is the additive identity element, and every vector must have an additive inverse. Furthermore, a finite dimensional real vector space must have a dimension, which is the number of vectors in a basis for the space.

What is the dimension of a finite dimensional real vector space?

The dimension of a finite dimensional real vector space is the number of vectors in a basis for the space. A basis is a set of linearly independent vectors that span the entire space. In other words, any vector in the space can be written as a linear combination of the basis vectors. The dimension of a finite dimensional real vector space is always a positive integer.

What are linear transformations in a finite dimensional real vector space?

A linear transformation in a finite dimensional real vector space is a function that maps one vector space to another while preserving the operations of addition and scalar multiplication. In other words, the image of a linear transformation is a vector space that has the same structure as the original vector space. Linear transformations are important in the study of finite dimensional real vector spaces because they help to define and analyze properties of these spaces.

What are some examples of finite dimensional real vector spaces?

Some examples of finite dimensional real vector spaces include Euclidean n-space, which is the set of n-tuples of real numbers, and the space of polynomials of degree n or less. Other examples include the space of matrices with n rows and m columns, and the space of continuous functions on a closed interval. Additionally, any set of n-dimensional vectors with operations of addition and scalar multiplication that satisfy the properties of a finite dimensional real vector space can be considered an example.

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