Is M a Vector Space Over Real Numbers?

In summary, the set of vectors M = {(a1,a2,a3) with a1, a2, a3 ∈ ℝ} is not a vector space over the field of real numbers because it does not satisfy the closure property under addition and multiplication. While limitations 2 and 3 satisfy the closure property, limitations 1 and 4 do not, making them not vector spaces.
  • #1
iloveannaw
45
0

Homework Statement



show whether the following set of vectors [tex]M = \left\{\left(a_{1},a_{2},a_{3}\right) with a_{1},a_{2},a_{3} \in \Re\right\}[/tex]

with the following limitations:
1) a1 is rational
2) a1 = 0
3) a1 + a2 = 0
4) a1 + a2 = 1

is a vector space over the field of real numbers.

Homework Equations


various axioms
* x+y = y+x.
* (x+y)+z = x+(y+z).
* 0+x = x+0 = x.
* (-x) + x = x + (-x) = 0.

For every x in X and real numbers c,d, we have

* 0x = 0
* 1x = x
* (cd)x = c(dx)

* c(x+y) = cx + cy.
* (c+d)x = cx +dx.

The Attempt at a Solution



I just don't get it, I really wish I could. I understand the axioms but when I apply them i find that M is a vector space regardless of the limitations.

somebody please help
 
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  • #2
You'll need to check a few things, first of all you'll need closure of the operations.
What do I mean with that?

Take [tex](a_1,a_2,a_3)[/tex] and [tex](b_1,b_2,b_3)[/tex] in M. Does [tex](a_1+b_1,a_2+b_2,a_3+b_3)[/tex] lie in M. In other words, does this triple satisfy the three limitations. This means that the addition is closed in M.

Take [tex](a_1,a_2,a_3)[/tex] in M and [tex]\alpha\in \mathbb{R}[/tex]. Does [tex](\alpha a_1,\alpha a_2, \alpha a_3)[/tex] lie in M. In other words, does this triple satisfy the three limitations. This means that scalar products are closed in M.

I conjecture that neither addition nor multiplication is closed in M (thus we are not dealing with a vector space). Can you give me examples why not?
 
  • #3
how could you tell if
[tex](a_1+b_1,a_2+b_2,a_3+b_3)[/tex] was in M or not, in fact what is in M? I know at least vector [tex](a_1,a_2,a_3)[/tex]

and there are four limitations now - i just edited my op

thanks
 
  • #4
I actually realized that you had 4 problems.

Let me solve problem 1 fo you:
So let M be the collection of triples [tex](a_1,a_2,a_3)[/tex] such that [tex]a_1[/tex] is rational.

First we check closure under addition. Take two triples [tex](a_1,a_2,a_3)[/tex] and [tex](b_1,b_2,b_3)[/tex] in M. This means that [tex]a_1[/tex] and [tex]a_2[/tex] are rational. We need to check that [tex](a_1,a_2,a_3)+(b_1,b_2,b_3)=(a_1+b_1,a_2+b_2,a_3+b_3)[/tex] is in M. For this, we just need to check that [tex]a_1+b_1[/tex] is rational. But since [tex]a_1[/tex] and [tex]a_2[/tex] are rational, so is their sum.
So closure under addition is checked.

Now, closure under multplication. Take a triple [tex](a_1,a_2,a_3)[/tex] in M (this means that [tex]a_1[/tex] is rational) and [tex]\alpha\in \mathbb{R}[/tex]. We need to check that [tex](\alpha a_1,\alpha a_2,\alpha a_3)[/tex] is in M. For this, we just need to check that [tex]\alpha a_1[/tex] is rational. But this is not always the case. For example, [tex](a_1,a_2,a_3)[/tex] could be [tex](1,0,0)[/tex] (this is indeed a vector in M) and [tex]\alpha[/tex] could be [tex]\pi[/tex], then [tex](\alpha a_1,\alpha a_2,\alpha a_3)=(\pi, 0,0)[/tex]. This is not in M, since [tex]\pi[/tex] is not rational.
So we do not have closure under multiplication, so we do not have a vector space.

Can you check the other examples? this is quite analogous.
 
  • #5
thanks, that seems so much clearer. I knew that the result of the operation had to remain in M but I had no idea what that meant.
hmm, if I've understood it 2) a1 = 0 and 3) a1 + a2 = 0 are vector spaces over R and
4) a1 + a2 = 1 isn't because

[tex]a_{1} = 1 - a_{2}[/tex] and [tex]b_{1} = 1 - b_{2}[/tex]

[tex](a_{1} + b_{1}, a_{2} + b_{2}, a_{3} + b_{3}) = (1 - a_{2} + 1 - b_{2}, a_{2} + b_{2}, a_{3} + b_{3}) [/tex]

and

[tex]1 - a_{2} + 1 - b_{2} + a_{2} + b_{2} = 2 [/tex] != 1
 
  • #6
Yes! I've think you've got it.

But note, for 2 and 3, you've just checked that the space is closed under the operations. You'll need to check the other axioms of a vector space to (associativity, neutral element,...). But this shouldn't be to much of a problem...

For 1 and 4, you've seen that they are not closed under the operations, so they cannot be vector spaces...
 

1. What is a vector space?

A vector space is a mathematical structure consisting of a set of objects, called vectors, and a set of rules for combining these vectors to form new vectors. The rules must follow certain properties such as closure, associativity, and distributivity.

2. What are the requirements for a vector space over real numbers?

In order for a set to be considered a vector space over real numbers, it must satisfy the following conditions:

  • The set must contain a zero vector, which is the additive identity element
  • Vector addition must be commutative and associative
  • Scalar multiplication must be distributive over vector addition
  • The set must be closed under vector addition and scalar multiplication

If the set satisfies all of these conditions, it is considered a vector space over real numbers.

3. Can a vector space be over a different set of numbers?

Yes, a vector space can be over a different set of numbers, such as complex numbers or integers. The requirements for a vector space will vary depending on the set of numbers it is defined over.

4. How do I determine if a set is a vector space over real numbers?

To determine if a set is a vector space over real numbers, you must check if it satisfies all of the requirements mentioned in question 2. If the set satisfies all of the conditions, then it is a vector space over real numbers.

5. What is the significance of a vector space over real numbers in science?

Vector spaces over real numbers are used in a variety of scientific fields, such as physics, engineering, and computer science. They provide a mathematical framework for representing and manipulating quantities and their relationships in a more abstract and generalizable way. This allows scientists to make complex calculations and predictions, and formulate theories and models that can be applied to various real-world problems and systems.

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