Raghav's question at Yahoo Answers (axioms of vector space)

In summary, the axioms of a vector space are a set of properties that a set must satisfy in order to be considered a vector space. These properties include closure under addition and scalar multiplication, associativity and commutativity of addition, existence of an additive identity element, existence of additive inverses, and distributivity of scalar multiplication over addition. The axioms are important because they establish the fundamental properties of a vector space and allow for systematic manipulation of vectors. They differ from the axioms of a group or ring in certain requirements, such as closure under both addition and scalar multiplication, and are used in practical applications in various fields such as physics, engineering, and computer graphics. It is not possible to violate the axioms of a
  • #1
Fernando Revilla
Gold Member
MHB
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Here is the question:

Let V be the set of all positive real numbers: defined by
u $\$$ v = uv ($ is ordinary multiplication) and define #
by e#v = v^e. Prove that V is a vector space.

How do I go about proving this ? I know how to prove if V is a real vector space, but how do I prove if it is a vector space ?

Here is a link to the question:

Proving a set V is a vector Space? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
Last edited:
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  • #2
Hello Raghav,

Clearly, $(V,\$)$ is a commutative group (here, the zero vector is $1$) and $(\mathbb{R},+,\cdot)$ is a field. We need to prove the four properties of the scalar multiplication $\#$. Then, for all $\lambda,\mu$ real scalars and for all $u,v\in V$ vectors:

$(i)\;\lambda\#(u\;\$\;v)=\lambda\#(uv)=(uv)^ {\lambda}=u^{\lambda}v^{\lambda}=u^{\lambda}\;\$\; v^{\lambda}=(\lambda\# u)\;\$\;(\lambda\#v)$

$(ii)\;(\lambda+\mu)\#u=u^{\lambda+\mu}=u^{\lambda}u^{\mu}=(\lambda\#u)\;\$\;(\mu\#u)$

$(iii)\;(\lambda\mu)\# u=u^{\lambda\mu}=(u^{\mu})^{\lambda}=\lambda\#(\mu\#u)$

$(iv)\;1\#u=u^{1}=u$
 

Related to Raghav's question at Yahoo Answers (axioms of vector space)

1. What are the axioms of a vector space?

The axioms of a vector space are a set of properties that must be satisfied in order for a set to be considered a vector space. These properties include closure under addition and scalar multiplication, associativity and commutativity of addition, existence of an additive identity element, existence of additive inverses, and distributivity of scalar multiplication over addition.

2. Why are the axioms of a vector space important?

The axioms of a vector space are important because they establish the fundamental properties that a set must have in order to be considered a vector space. These properties allow for the manipulation and transformation of vectors in a systematic and consistent manner, making vector spaces a powerful tool in many areas of mathematics and science.

3. How do the axioms of a vector space differ from the axioms of a group or ring?

The axioms of a vector space are similar to the axioms of a group or ring, but with a few key differences. For example, in a group, closure under addition is an axiom, while in a vector space, closure under both addition and scalar multiplication is required. Additionally, vector spaces have the added requirement of distributivity of scalar multiplication over addition.

4. Can the axioms of a vector space be violated?

No, the axioms of a vector space cannot be violated. In order for a set to be considered a vector space, it must satisfy all of the axioms. If any of the axioms are not satisfied, then the set cannot be considered a vector space.

5. How are the axioms of a vector space used in practical applications?

The axioms of a vector space are used in a variety of practical applications, such as in physics, engineering, and computer graphics. For example, in physics, vector spaces are used to represent the physical quantities of magnitude and direction. In engineering, vector spaces are used to model and analyze systems with multiple variables. And in computer graphics, vector spaces are used to represent and manipulate geometric objects in 3-dimensional space.

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