Vectorial Subspace: Origin to Vector Mapping

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In summary, the conversation discusses the concept of vectorial subspaces and whether a given set of vectors would qualify as a subspace or not. It is determined that for a subset to be considered a subspace, it must satisfy three conditions: having the 0 vector, being closed under addition, and being closed under scalar multiplication. The set of vectors (b, 2a + 1) is not a subspace of R2 because it does not meet all three conditions. However, the set {(a, 2b + 1) | a, b are real} is a subspace of R2 because it spans all of R^2. The conversation also touches on the difference between (a, 2
  • #1
Fanta
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There's not really a problem statement here.
I just want to know :
If I have a vector starting on the origin (like a position vector), then it will always correspond to a vectorial subspace, right?

For example:

[tex] (b, 2a + b ) : a, b \in R [/tex]

is a vectorial subspace

but is

[tex] (b, 2a + 1 ) : a, b \in R [/tex]

a subspace too?
And if not, why is that?
 
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  • #2
For a subset of a vector space to actually be a subspace of that vector space, the subset has to satisfy three conditions:
The set has to have the 0 vector.
If u and v are in the subset, then u + v is also in the subset.
If u is in the subset, and c is any scalar, then cu is in the subset.

The set of vectors (b, 2a + 1) is not a subspace of R2, because at least one of the three conditions is not met.
 
  • #3
Uh, but (b,2a+1) spans all of R^2.
 
  • #4
Dick said:
Uh, but (b,2a+1) spans all of R^2.
Right, but maybe we aren't talking about the same thing. I'm thinking in terms of the set {(a, 2b + 1) | a, b are real}. This set isn't closed under addition, so isn't a subspace of R2.
 
  • #5
Mark44 said:
Right, but maybe we aren't talking about the same thing. I'm thinking in terms of the set {(a, 2b + 1) | a, b are real}. This set isn't closed under addition, so isn't a subspace of R2.

(a1,2b1+1)+(a2,2b2+1)=(c,2d+1) where c=a1+a2 and d=b1+b2+1/2. The question looks like a different kind of question (i.e. is (a,2b,1) a subspace?). But it's not. 2b+1 is ANY real number, just like b and independent of a.
 
  • #6
Dick, I wasn't confusing it with (a, 2b, 1). I think I got thrown by the lack of dependence of a and b.
 
  • #7
Mark44 said:
Dick, I wasn't confusing it with (a, 2b, 1). I think I got thrown by the lack of dependence of a and b.

That's true. (a,2a+1) would also be a whole different story.
 
  • #8
Dick said:
That's true. (a,2a+1) would also be a whole different story.
That's exactly where I was coming from. My eyes must have glazed over...
 
  • #9
you got me kinda lost here.

so it is indeed a subspace since it spans all R^2?
 
  • #10
Fanta said:
you got me kinda lost here.

so it is indeed a subspace since it spans all R^2?

R^2 is a subspace of R^2, isn't it? Check the conditions to be a subspace Mark44 was referring to.
 

FAQ: Vectorial Subspace: Origin to Vector Mapping

What is a vectorial subspace?

A vectorial subspace is a subset of a vector space that contains all possible linear combinations of its vectors. It is closed under vector addition and scalar multiplication, making it a fundamental concept in linear algebra.

How is a vectorial subspace defined?

A vectorial subspace is defined by three properties: it must contain the zero vector, it must be closed under vector addition, and it must be closed under scalar multiplication. These properties ensure that it is a valid subset of a vector space.

What is the origin to vector mapping?

The origin to vector mapping is a function that maps the origin (zero vector) of a vector space to a specific vector within a vectorial subspace. This mapping is often used to describe the relationship between a vectorial subspace and its parent vector space.

How is the origin to vector mapping used in vectorial subspaces?

The origin to vector mapping is used to identify the basis vectors of a vectorial subspace. By mapping the origin to each basis vector, we can determine the linear combinations of these vectors that form the subspace.

What is the importance of vectorial subspaces in scientific research?

Vectorial subspaces are essential in various fields of science, including physics, engineering, and computer science. They provide a mathematical framework for describing and analyzing physical systems, making them a powerful tool for solving complex problems and making predictions about real-world phenomena.

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