Positive Quadrant Vector Space

In summary, we have discussed how to show that the positive quadrant Q is a vector space by redefining the operations of addition and scalar multiplication. The main difficulty lies in finding the additive identity and inverse, which can be solved by using the properties of the operations. We have also discussed how to find subspaces within a given vector space, using the example of continuous functions. The values of a and b for which the set of all continuous functions would form a vector space depend on the properties of the operations and can be determined by analyzing the axioms.
  • #1
Mindscrape
1,861
1

Homework Statement


Show that the positive quadrant
[tex]Q = ( (x,y) | x,y > 0 ) \in \mathbb{R}^2[/tex]
is a vector space.

Homework Equations


Addition is redefined by
[tex](x_1,y_1) + (x_2,y_2) = (x_1 x_2, y_1 y_2)[/tex]
and scalar multiplication by
[tex] c(x,y) = (x^c , y^c)[/tex]

The Attempt at a Solution


There are two properties I am having trouble with - the additive identity and additive inverse.

Additive identity
[tex](x,y) + (0,0) = (0,0)[/tex]
which violates the definition that v + 0 = v

Additive inverse
[tex](x,y) + (-x,-y) = (-x^2, -y^2) \not \in \mathbb{V}[/tex]
also violates that v + (-v) = 0

For the identity I might just be thinking about the zero element in the wrong way, but I really have no idea how I could have messed up the additive inverse.
 
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  • #2
The zero element could be (1, 1), according to your definition of addition.
 
  • #3
I suspected that the zero element could be (1, 1), but I am not really sure why, other than that it satisfies the additive identity problem.

What about the additive inverse? The inverse of (x,y) would have to be (0,0), which is probably what it is, but again I don't really know the justification other than satisfying the property.

I never payed much attention to vector spaces because they never seemed all that applicable or important, so things like zero elements that aren't actually zero seem to elude me.
 
  • #4
If I'm not mistaken, for every element you can take (0, 0) as the additive inverse, since, by your definition of addition, you have (x, y) + (0, 0) = (x*0, y*0) = (0, 0).
 
  • #5
Mindscrape said:
I suspected that the zero element could be (1, 1), but I am not really sure why, other than that it satisfies the additive identity problem.
What do you think an additive identity is? Any thing that satisfies the additive identity property: v+ e= v.


What about the additive inverse? The inverse of (x,y) would have to be (0,0), which is probably what it is, but again I don't really know the justification other than satisfying the property,

I never payed much attention to vector spaces because they never seemed all that applicable or important, so things like zero elements that aren't actually zero seem to elude me.
WHAT! Vector spaces are everything! Everything "linear" at any rate- and almost all methods of solving "non-linear" problems involve reducing to a linear problem.

radou said:
If I'm not mistaken, for every element you can take (0, 0) as the additive inverse, since, by your definition of addition, you have (x, y) + (0, 0) = (x*0, y*0) = (0, 0).
Absolutely not! Each element of a vector space must have its own additive inverse: when the two are added the result is the additive identity.

Here, are you forgetting that (0,0) is not the additive identity? (1,1) is. Given any (x,y) in this space, its additive inverse is (a,b) such that (x,y)+ (a,b)= (xa, yb)= (1, 1). What is a in terms of x and b in terms of y? Do they always exist?
 
  • #6
HallsofIvy said:
Here, are you forgetting that (0,0) is not the additive identity?

Yes, I am. :yuck: Excuzes moi.
 
  • #7
Mindscrape said:
I suspected that the zero element could be (1, 1), but I am not really sure why, other than that it satisfies the additive identity problem.

What about the additive inverse? The inverse of (x,y) would have to be (0,0), which is probably what it is, but again I don't really know the justification other than satisfying the property.
The point of axiom is that if something satisfies the axioms to be a FOO, then it is a FOO. You have used the wrong axioms in the second part. There is absolutely no reason why (0,0) MUST be the additive identity. I think you're confused because in every other example where you have to verify V is a SUBspace of R^n, (0,...,0) is the additive identity, but that is because of the prefix SUB in there and the additive identity of R^n is (0,0,..,0). Here we just have a set, with operations. We just need to verify these things satisfy the axioms of a vector space.
 
  • #8
Oh, I see now. Since the inverse must go back to the additive identity then it would have to be (1/x, 1/y), which would always exist since (x, y) > 0.

Now if I wanted to find a subspace the subspace must satisfy the altered axioms for closure, scalar multiplication, and addition.
 
  • #9
Yes, that is correct. You said in your original post that you were only having problems with the existence of an additive identity and additive inverses. Can we assume that you have proved that the operations satisfy the require properties: associative, scalar multiplication distributes over addition, etc.? Of course if you have proven them for the space, you don't have to do that again for a subspace- just showing that the subspace is closed under addition and scalar multiplication is sufficient- the other properties follow from the fact that they are true for the entire space.

By the way, it is an easy theorem that the scalar product of 0 with any vector is the additive identity. Do you see how that works here?
 
  • #10
Yes, I was able to prove the other axioms fairly easily. The scalar product of zero with any vector would have to be the additive identity because it is identically zero for all vector functions.

So, here is another question then.
[tex]\mathbb{V} = C^0 \mathbb{(R)}[/tex] with [tex]f:\mathbb{R} \rightarrow \mathbb{R}[/tex]
where the vector space is the set of all continuous functions.

I know that f(1) = 0 would be a subspace because the zero element is identically zero for all zero functions. Also, it is trivial from calculus that continuous functions added, and multiplied by a constant, remain continuous. On the other hand, f(0) = 1 would not be a subspace because the zero element is not identically zero. Considering the more general form of f(a)=b, what values of a and b would make vector spaces? It seems to be that a can be any real number and b must be zero.
 
  • #11
I'm going to be picky.

f(1)=0 is not a subspace. It is an equation.

{f : f(1)=0}

is a subspace.
 
  • #12
Mindscrape said:
Yes, I was able to prove the other axioms fairly easily. The scalar product of zero with any vector would have to be the additive identity because it is identically zero for all vector functions.

So, here is another question then.
[tex]\mathbb{V} = C^0 \mathbb{(R)}[/tex] with [tex]f:\mathbb{R} \rightarrow \mathbb{R}[/tex]
where the vector space is the set of all continuous functions.
You are saying the same thing in 3 different ways here!

I know that f(1) = 0 would be a subspace because the zero element is identically zero for all zero functions. Also, it is trivial from calculus that continuous functions added, and multiplied by a constant, remain continuous. On the other hand, f(0) = 1 would not be a subspace because the zero element is not identically zero. Considering the more general form of f(a)=b, what values of a and b would make vector spaces? It seems to be that a can be any real number and b must be zero.
It's not at all clear what you are saying here! Yes, the set of all continuous functions from R to R forms a vector space over R because we can add continuous functions and multiply continuous functions by real numbers and still get continuous functions. It is a subspace of the vector space of all functions.

But, as matt grime said, "f(1)= 0 would be a supspace" makes no sense. I think you meant it as shorthand for "the set of all continuous functions f, such that f(1)= 0" form a subspace of the space of all continuous functions". Shorthand is not always a good thing! Yes, that statement is true since if f and g are two such functions, and a a real number, then f+ g is continuous with f+g(1)= f(1)+ g(1)= 0+ 0= 0, and af is a continuous function with af(1)= a(0)= 0. That would be true with any number in place of the "1": For any number a, "the set of all continuous functions such that f(a)= 0 forms a subspace of the space of all continuous functions" is true.

On the other hand, "the set of all continuous functions f, such that f(a)= b, forms a subspace" only if b= 0. If f and g are two continuous functions such that f(a)= b and g(a)= b then f+ g(a)= f(a)+ g(a)= 2b which is equal to b only if b= 0. More simply, for any number p, pf(a)= pb which is equal to b only if b= 0.
 
  • #13
Yeah, sorry about saying the same thing three times. I don't know notation all that well and figured one of them had to get the point across. I meant {f: f(1) = 0}, I guess I should be more explicit (I've never really cared about formalities doing applied physics). I think I have a better handle on the more abstract vector spaces (ones that aren't R^n), thanks guys.
 

1. What is a positive quadrant vector space?

A positive quadrant vector space is a mathematical concept used to describe a space where all the coordinates are positive. This type of vector space is commonly used in economics, physics, and other fields to represent quantities that cannot be negative, such as time, mass, or distance.

2. How is a positive quadrant vector space different from a regular vector space?

In a regular vector space, the coordinates can be positive, negative, or zero. In a positive quadrant vector space, the coordinates are limited to being positive. This restriction allows for a more specific representation of certain quantities and can make calculations and interpretations easier.

3. What are some real-life examples of positive quadrant vector spaces?

Some common examples of positive quadrant vector spaces include the first quadrant of the Cartesian coordinate system, where both the x and y coordinates are positive, and the positive side of a number line, where all values are greater than zero. In economics, supply and demand graphs are often represented on a positive quadrant vector space.

4. How are positive quadrant vector spaces used in scientific research?

Positive quadrant vector spaces are used in scientific research to model and analyze data that is restricted to positive values. For example, in biology, population growth models can be represented on a positive quadrant vector space to show the growth of a population over time while taking into account factors such as birth and death rates.

5. Are there any limitations to using a positive quadrant vector space?

One limitation of using a positive quadrant vector space is that it may not accurately represent all data or situations. Some quantities can have negative values, and by restricting them to be positive, important information may be lost. Additionally, in some cases, a positive quadrant vector space may not provide enough information to fully understand a phenomenon and may need to be combined with other mathematical concepts or representations.

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