In a book I'm reading, it says:If beta is orthogonal to the set A,

In summary, the conversation discusses the concept of orthogonality in relation to a set A. It is stated that if beta is orthogonal to A, then it is also orthogonal to the closure of the linear span of A. The reason for mentioning closure is because in most applications, it is necessary for x to be orthogonal to the closure of span(A). The speaker also mentions that in general, if x is orthogonal to a set A, then it is also orthogonal to the closure of A. The question of whether the inner product with x is a continuous function is also brought up, to which the answer is yes.
  • #1
yifli
70
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In a book I'm reading, it says:

If beta is orthogonal to the set A, then beta is orthogonal to the closure of the linear span of A

It's easy to see beta is orthogonal to the linear span of A, but I don't understand why it has to mention closure here?
 
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  • #2


Hi yifli! :smile:

What is your question exactly?

Do you wonder why they wrote this here? Well, because it's true. Of course it's also true that x is orthogonal to span(A), so they could have written this too. However, in most applications, you need x orthogonal to the closure of span(A). This is why they've written this here.

Do you ask why it is true? Well, in general, if x is orthogonal to a set A, then x is orthogonal to the closure of A. Indeed, pick a in the closure of A, then there exists a sequence an that converges to a. Since an is in A, we know that <x,an>=0. And thus [itex]<x,a>=\lim_{n\rightarrow +\infty}{<x,a_n>}=0[/itex]. Thus x is orthogonal to a.
 
  • #3


micromass said:
Hi yifli! :smile:

What is your question exactly?

Do you wonder why they wrote this here? Well, because it's true. Of course it's also true that x is orthogonal to span(A), so they could have written this too. However, in most applications, you need x orthogonal to the closure of span(A). This is why they've written this here.

Do you ask why it is true? Well, in general, if x is orthogonal to a set A, then x is orthogonal to the closure of A. Indeed, pick a in the closure of A, then there exists a sequence an that converges to a. Since an is in A, we know that <x,an>=0. And thus [itex]<x,a>=\lim_{n\rightarrow +\infty}{<x,a_n>}=0[/itex]. Thus x is orthogonal to a.

micromass, thank you so much for your answer!
 
  • #4


micromass said:
Hi yifli! :smile:

What is your question exactly?

Do you wonder why they wrote this here? Well, because it's true. Of course it's also true that x is orthogonal to span(A), so they could have written this too. However, in most applications, you need x orthogonal to the closure of span(A). This is why they've written this here.

Do you ask why it is true? Well, in general, if x is orthogonal to a set A, then x is orthogonal to the closure of A. Indeed, pick a in the closure of A, then there exists a sequence an that converges to a. Since an is in A, we know that <x,an>=0. And thus [itex]<x,a>=\lim_{n\rightarrow +\infty}{<x,a_n>}=0[/itex]. Thus x is orthogonal to a.

Do you need to show that the inner product with x is a continuous function?
 
  • #5


lavinia said:
Do you need to show that the inner product with x is a continuous function?

Of course! :smile: But that's not that hard, I believe...
 

Related to In a book I'm reading, it says:If beta is orthogonal to the set A,

1. What does it mean for beta to be orthogonal to a set?

Orthogonality in mathematics refers to the concept of two vectors being perpendicular to each other, meaning they form a 90 degree angle. In this context, beta being orthogonal to a set A means that it is perpendicular to all vectors in the set A.

2. Why is orthogonality important in mathematics?

Orthogonality is a fundamental concept in mathematics, particularly in linear algebra. It allows us to easily solve systems of equations and perform geometric transformations. It also has many applications in fields such as physics, engineering, and computer science.

3. How is orthogonality related to linear independence?

Orthogonality and linear independence are closely related concepts. In order for a set of vectors to be linearly independent, they must be orthogonal to each other. This means that none of the vectors can be expressed as a linear combination of the others.

4. Can you give an example of two orthogonal vectors?

Yes, two vectors in a 2-dimensional space that are orthogonal to each other are (1,0) and (0,1). This means that they form a 90 degree angle when plotted on a graph. In higher dimensions, there are an infinite number of possible orthogonal vectors.

5. How is orthogonality represented mathematically?

In linear algebra, the dot product (also known as the inner product) is used to determine orthogonality. If the dot product of two vectors is equal to 0, then they are orthogonal. This can be represented as a·b = 0, where a and b are the two vectors.

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