Are $V_1$ and $V_2$ Vector Spaces According to Defined Properties?

In summary, the vector space $V_1$ is not a vector space because it does not satisfy the five properties of a vector space. Additionally, vector addition and scalar multiplication are not well-defined in $V_1$.
  • #1
mathmari
Gold Member
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Hey! :eek:

I want to check if the following are true.
  1. $V_1=\{a\in \mathbb{R}\mid a>0\}$ with the common multiplication as the vector addition and the scalar multiplication $\lambda \odot v=v^{\lambda}$ is a $\mathbb{R}$-vector space.
  2. $V_2=\{(x,y)\in \mathbb{Q}^2 \mid x^2=-y^2\}$ with the addition and scalar multiplication of $\mathbb{Q}^2$ is a $\mathbb{Q}$-vector space.
We have the following:
Let $K$ be a field. A vector space over $K$ (or $K$-vector space) is a set $V$ with an addition $V \times V \rightarrow V : (x, y) \mapsto x + y$ and a scalar multiplication $K \times V \rightarrow V : (\lambda , x) \mapsto \lambda \cdot x$, so that the following holds:
  • (V1) : $(V,+)$ is an abelian group, with the neutral element $0$.
  • (V2) : $\forall a, b \in K, \forall x \in V : (a + b) \cdot x = a \cdot x + b \cdot x$
  • (V3) : $\forall a \in K, \forall x, y \in V : a \cdot (x + y) = a \cdot x + a \cdot y$
  • (V4) : $\forall a, b \in K, \forall x \in V : (ab) \cdot x = a \cdot (b \cdot x)$
  • (V5) : $\forall x \in V : 1 \cdot x = x$ ( $1 = 1_K$ is the identity in $K$).

So, we have to check at both cases these $5$ properties, right?
  1. We have that $x+y=x\cdot y$ and $x\cdot y=x\odot y$, or not?
    For (V2) we have that for $a,b\in \mathbb{R}$ and $x\in V_1$
    $(a\cdot b)\odot x=x^{a\cdot b}$ but $a\odot x \cdot b\odot x=x^a\cdot x^b=x^{a+b}$. So the property (V2) is not satisfied. Therefore, $V_1$ is not a vector space.
    Is this correct? (Wondering)

    We could also prove it as follows, or not? (Wondering)
    So that $V_1$ is a $\mathbb{R}$-vector space it must be closed under scalar multiplication. We have that $a\in V_1$, so $a>0$. Then $-1\in \mathbb{R}$ bur $(-1)\cdot a=-a<0$ and so $(-1)\cdot a\notin V_1$.
  2. The properties (V2)-(V5) are satisfied, or not? How can we check the property (V1) ? (Wondering)
 
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  • #2
Hey mathmari! (Smile)

mathmari said:
So, we have to check at both cases these $5$ properties, right?

Additionally, we have to check if vector addition and scalar multiplication are well-defined.
That is, whether the vector space is closed for vector addition, and also closed for scalar multiplication. (Thinking)
1.
We have that $x+y=x\cdot y$ and $x\cdot y=x\odot y$, or not?

Let's phrase it: $x+y=xy$ and $\lambda\cdot x=\lambda\odot x$.
That is, we have a good chance to mix up additions and multiplications.
So we need to be mindful that we consistently apply:
\begin{array}{|c|c|c|}
\hline
Notation & Operation & Result
\\
\hline
x + y & \text{vector addition} & \text{vector} \\
xy & \text{multiplication of reals} & \text{vector} \\
\lambda \cdot x & \text{multiplication of scalar with vector} & \text{vector} \\
\lambda\odot x & \text{multiplication of scalar with vector} & \text{vector} \\
x^\lambda & \text{power operation of reals} & \text{vector}\\
\hline
a+b & \text{addition of reals} & \text{scalar} \\
a\cdot b & \text{multiplication of reals} & \text{scalar} \\
\hline
\end{array}

In case of doubt, we could also make it $\overrightarrow x + \overrightarrow y = \overrightarrow {xy}$. (Thinking)

For (V2) we have that for $a,b\in \mathbb{R}$ and $x\in V_1$
$(a\cdot b)\odot x=x^{a\cdot b}$ but $a\odot x \cdot b\odot x=x^a\cdot x^b=x^{a+b}$. So the property (V2) is not satisfied. Therefore, $V_1$ is not a vector space.
Is this correct? (Wondering)

Shouldn't (V2) be: $(a + b)\odot x \overset ? = a\odot x + b\odot x$? (Wondering)
Then we have:
$$(a + b)\odot x = x^{a+b} = (x^a)(x^b) = (a\odot x)(b\odot x) = a\odot x + b\odot x$$
(Thinking)
 
  • #3
I like Serena said:
Shouldn't (V2) be: $(a + b)\odot x \overset ? = a\odot x + b\odot x$? (Wondering)
Then we have:
$$(a + b)\odot x = x^{a+b} = (x^a)(x^b) = (a\odot x)(b\odot x) = a\odot x + b\odot x$$
(Thinking)

I got confused with "common multiplication as the vector addition". What does this mean? That the addition of that space is the common multiplication? (Wondering)
 
  • #4
mathmari said:
I got confused with "common multiplication as the vector addition". What does this mean? That the addition of that space is the common multiplication? (Wondering)

Each vector is represented by a single real number.
Adding two such vectors is executed by multiplying the real numbers they represent.
We can distinguish by writing:
$$\overrightarrow x + \overrightarrow y = \overrightarrow {xy}$$
As an example:
$$\overrightarrow 2 + \overrightarrow 3 = \overrightarrow {2\cdot 3} = \overrightarrow {6}$$
(Thinking)
 
  • #5
I like Serena said:
Each vector is represented by a single real number.
Adding two such vectors is executed by multiplying the real numbers they represent.
We can distinguish by writing:
$$\overrightarrow x + \overrightarrow y = \overrightarrow {xy}$$
(Thinking)

So, how can identity V2 be written? (Wondering)
 
  • #6
mathmari said:
So, how can identity V2 be written? (Wondering)
(V2) We want to verify if for all $a,b\in \mathbb R, \overrightarrow x \in V$:
$$(a+b)\cdot \overrightarrow x \overset ?= a\cdot \overrightarrow x + b \cdot \overrightarrow x$$
Working it out we get:
$$(a+b)\cdot \overrightarrow x = \overrightarrow{ (a+b)\odot x} = \overrightarrow{ x^{a+b}} \\
a\cdot \overrightarrow x + b \cdot \overrightarrow x = \overrightarrow{a\odot x} + \overrightarrow{b\odot x}
= \overrightarrow{x^a} + \overrightarrow{x^b}
= \overrightarrow{(x^a)(x^b)} = \overrightarrow{x^{a+b}}
$$
So (V2) checks out. (Thinking)
 
  • #7
I like Serena said:
(V2) We want to verify if for all $a,b\in \mathbb R, \overrightarrow x \in V$:
$$(a+b)\cdot \overrightarrow x \overset ?= a\cdot \overrightarrow x + b \cdot \overrightarrow x$$
Working it out we get:
$$(a+b)\cdot \overrightarrow x = \overrightarrow{ (a+b)\odot x} = \overrightarrow{ x^{a+b}} \\
a\cdot \overrightarrow x + b \cdot \overrightarrow x = \overrightarrow{a\odot x} + \overrightarrow{b\odot x}
= \overrightarrow{x^a} + \overrightarrow{x^b}
= \overrightarrow{(x^a)(x^b)} = \overrightarrow{x^{a+b}}
$$
So (V2) checks out. (Thinking)
Ah ok... (Thinking)

When we symbolize the addition of vectors as $\oplus$ we have the following:

(V2) : Let $a,b\in K$ and $x\in V_1$
$(a+ b)\odot x=x^{a+ b}$
$a\odot x\oplus b\odot x=x^a\cdot x^b=x^{a+b}$

(V3) : Let $a\in K$ and $x,y\in V_1$
$a\odot (x\oplus y)=a\odot (x\cdot y)=(x\cdot y)^a=x^a\cdot y^a$
$a\odot x\oplus a\odot y=x^a\cdot y^a$

(V4) : Let $a,b\in K $ and $x\in V_1$
$(a\cdot b)\odot x=x^{a\cdot b}$
$a\odot (b\odot x)=a\odot x^b=(x^b)^a=x^{a\cdot b}$

(V5) : Let $x\in V_1$
$1\odot x=x^1=x$Is everything correct so far? (Wondering)

It is left to check the first property, right? How could we do that? (Wondering)
 
  • #8
mathmari said:
Ah ok... (Thinking)

When we symbolize the addition of vectors as $\oplus$ we have the following:

(V2) : Let $a,b\in K$ and $x\in V_1$
$(a+ b)\odot x=x^{a+ b}$
$a\odot x\oplus b\odot x=x^a\cdot x^b=x^{a+b}$

(V3) : Let $a\in K$ and $x,y\in V_1$
$a\odot (x\oplus y)=a\odot (x\cdot y)=(x\cdot y)^a=x^a\cdot y^a$
$a\odot x\oplus a\odot y=x^a\cdot y^a$

(V4) : Let $a,b\in K $ and $x\in V_1$
$(a\cdot b)\odot x=x^{a\cdot b}$
$a\odot (b\odot x)=a\odot x^b=(x^b)^a=x^{a\cdot b}$

(V5) : Let $x\in V_1$
$1\odot x=x^1=x$Is everything correct so far? (Wondering)

Those power identities are not generally true.
Consider for instance that $-1 = (-1)^{1} = (-1)^{(1/2 \cdot 2)} \ne ((-1)^{1/2})^2 = \text{undefined}$, since we cannot take the square root of a negative number within the reals.
And $-1 = (-1)^{1} = (-1)^{(2 \cdot 1/2)} \ne ((-1)^2)^{1/2} = 1^{1/2} = 1$. (Worried)

It is left to check the first property, right? How could we do that? (Wondering)

Check the axioms of an abelian group?
That is, closure of addition, associativity, existence of neutral element, existence of additive inverse, commutativity. (Thinking)
 
  • #9
I like Serena said:
Those power identities are not generally true.
Consider for instance that $-1 = (-1)^{1} = (-1)^{(1/2 \cdot 2)} \ne ((-1)^{1/2})^2 = \text{undefined}$, since we cannot take the square root of a negative number within the reals.
And $-1 = (-1)^{1} = (-1)^{(2 \cdot 1/2)} \ne ((-1)^2)^{1/2} = 1^{1/2} = 1$. (Worried)

$x$ must be always positive, or not? (Wondering)
I like Serena said:
Check the axioms of an abelian group?
That is, closure of addition, associativity, existence of neutral element, existence of additive inverse, commutativity. (Thinking)

Let $x,y\in V_1$ then $x\oplus y=xy\in V_1$, since $x>0$ and $y>0$ and so $xy>0$.

The neutral element doesn'tbelong to $V_1$, does it? (Wondering)
 
  • #10
mathmari said:
$x$ must be always positive, or not? (Wondering)

Yes.
We require that $x$ is positive and that the scalars are real for those power identities to hold.
They are, but perhaps we should mention that as part of the verification? (Wondering)
Let $x,y\in V_1$ then $x\oplus y=xy\in V_1$, since $x>0$ and $y>0$ and so $xy>0$.

The neutral element doesn'tbelong to $V_1$, does it? (Wondering)

Let's see... we're looking for an element $e$, such that $e\oplus x=x\oplus e = x$.
What does that mean for $e$? (Wondering)
 
  • #11
I like Serena said:
Yes.
We require that $x$ is positive and that the scalars are real for those power identities to hold.
They are, but perhaps we should mention that as part of the verification? (Wondering)

Let's see... we're looking for an element $e$, such that $e\oplus x=x\oplus e = x$.
What does that mean for $e$? (Wondering)

$e$ must be $1$, right? (Wondering)

Doesn't $0$ must also an element of the set? (Wondering)
 
  • #12
mathmari said:
$e$ must be $1$, right? (Wondering)
Yep.

Doesn't $-0$ must also an element of the set? (Wondering)
Here's another mixup in additions and multiplications..
The $0$ vector must indeed be an element of the set. That is, the vector that is neutral for addition.
However, that happens to be $\overrightarrow 1$, which is indeed part of the set! (Tauri)

In particular $0$ itself is not part of the set, so neither is its additive inverse.
And the additive inverse of $\overrightarrow 1$ is itself.
 
  • #13
I like Serena said:
Check the axioms of an abelian group?
That is, closure of addition, associativity, existence of neutral element, existence of additive inverse, commutativity. (Thinking)

Closure of addition: $x\oplus y=x\cdot y\in V_1$

Associativity: $(x\oplus y)\oplus z=(x\cdot y)\cdot z=x\cdot (y\cdot z)=x\cdot (y\oplus z)=x\oplus (y\oplus z)$

Existence of neutral element: $e\oplus x=x=x\oplus e \Rightarrow e\cdot x=x=x\cdot e \Rightarrow e=1$

Existence of additive inverse: $x\oplus y=e=y\oplus x \Rightarrow x\cdot y=e=y\cdot x$, so $y$ is $x^{-1}$ ? (Wondering)

Commutativity: $x\oplus y=x\cdot y=y\cdot x=y\oplus x$ Is everything correct? (Wondering)
 
  • #14
Yep. All correct.
Note that all $x^-1 \in V_1$ since we're talking about all positive reals.
 
  • #15
I like Serena said:
Yep. All correct.
Note that all $x^-1 \in V_1$ since we're talking about all positive reals.

Ok! Thank you very much! (Happy)
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and two operations: vector addition and scalar multiplication. These operations follow specific rules and properties, such as closure, associativity, and distributivity, which allow for algebraic manipulations of the vectors.

2. How can I determine if something is a vector space?

To determine if something is a vector space, you can check if it satisfies the 10 vector space axioms. These axioms include properties such as closure, associativity, and distributivity, as well as the existence of a zero vector, additive inverse, and scalar multiplication identity.

3. What are some common examples of vector spaces?

Some common examples of vector spaces include Euclidean spaces (such as 2D and 3D spaces), function spaces, and matrix spaces. Other examples include the set of all polynomials of a certain degree, the set of all real or complex numbers, and the set of all continuous functions on a given interval.

4. Can a set of matrices be a vector space?

Yes, a set of matrices can be a vector space if it satisfies the 10 vector space axioms. For example, the set of all 2x2 matrices with real entries is a vector space, since it satisfies all the axioms.

5. What are some applications of vector spaces?

Vector spaces have many applications in mathematics and various fields of science, such as physics, computer science, and engineering. They are used to model physical quantities, such as forces and velocities, and to solve systems of linear equations. Vector spaces are also essential in data analysis and machine learning, where they are used to represent and manipulate data points and features.

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