Some help understanding vector space

In summary: You'll soon see that proving things about vector spaces is a important part of mathematics. In summary, you don't understand what vector space is, or why you need to verify axioms, and you are asking for help to understand.
  • #1
MarcL
170
2
Hey, so my class just got into vector space and I don't have a clue what is going on. My teacher makes us go through the axiom for every problem but I can't visualize what is happening in our problems ( and our workbook makes me even more confused)

It seems so... useless. The axioms. Why would I need to verify x+y=y+x. Isn't that always true for additions?
Or that c(x,y)=cx,cy. Or telling me that there is a zero vector in V so u+0=u+0=u.

Sorry if this sounds really dumb it just... doesn't make sense to me. It all sounds so redundant because those are basics vector operations and such no? Samething as a vector space, wouldn't it just be a plane?

I'm just all confused as to what this is... just need help clarifying things.
 
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  • #2
MarcL said:
It seems so... useless. The axioms. Why would I need to verify x+y=y+x. Isn't that always true for additions?
You wouldn't call the operation "addition" (or denote it by +) if that axiom doesn't hold. But when you define a new operation, it's not obvious that the axiom holds. The verification isn't supposed to tell you anything about "addition". It's supposed to tell you if what you have defined deserves to be called addition.

MarcL said:
Or that c(x,y)=cx,cy.
That's a definition, not an axiom. You use the definition to prove that ordered pairs of real numbers satisfy vector space axioms. One of them says that "for all v in V, we have 1v=v". The proof goes like this (assuming that we have specified that V is the set of ordered pairs of real numbers, and that addition and scalar multiplication are defined the familiar way): Let v be an arbitrary element of V. Let x and y be the unique real numbers such that v=(x,y). We have ##1v=1(x,y)=(1x,1y)=(x,y)=v##.

MarcL said:
Or telling me that there is a zero vector in V so u+0=u+0=u.
Let's say that V is the set of functions from ℝ into ℝ. Is it obvious that there's a zero vector in V. Is it super trivial to verify that there's a 0 in V such that v+0=0+v=v for all v in V? (It's trivial to those of us with experience, but most students who try this for the first time in our homework forum get it very, very wrong).

Suppose that V is the set of positive real numbers, and we define the following operations instead: ##x\oplus y=xy## for all x,y in V, and ##kx=|k|x## for all k in ℝ and all x in V. Is it still obvious that there's a zero vector in V?

MarcL said:
Sorry if this sounds really dumb it just... doesn't make sense to me. It all sounds so redundant because those are basics vector operations and such no? Samething as a vector space, wouldn't it just be a plane?

I'm just all confused as to what this is... just need help clarifying things.
The set of ordered pairs of real numbers, with "addition" and "scalar multiplication" defined the way you have seen, is the simplest non-trivial example of a vector space. That vector space can be interpreted as a plane. The vector space axioms are statements that are easily seen to be satisfied by those two operations. This result is the inspiration for the definition of "vector space", which say that any set with two operations that satisfy those conditions is called a vector space. Every vector space has a lot in common with planes (specifically they satisfy the vector space axioms), but most vector spaces are not planes.
 
  • #3
MarcL said:
Samething as a vector space, wouldn't it just be a plane?

If you only think of one example of a vector space, you won't appreciate the need for proving things about vector spaces. You need to stretch your mind a little. For example, think of the set of polynomials in the single variable x with real coefficients or think of the set of continuous real valued functions on the unit interval. See if they can be made to fit the definition of a vector space. Think of some vector spaces with an infinite number of dimensions.
 

FAQ: Some help understanding vector space

What is a vector space?

A vector space is a mathematical structure that consists of a set of objects, called vectors, and a set of rules for combining these vectors through addition and multiplication by scalars. This allows for the representation and manipulation of geometric and algebraic quantities.

What are the properties of a vector space?

A vector space must satisfy several properties, including closure under addition and scalar multiplication, associativity, commutativity, existence of an additive identity element, and existence of additive inverses. These properties ensure that the space can be manipulated using familiar algebraic rules.

How is a vector space different from a Euclidean space?

A Euclidean space is a specific type of vector space that has a finite number of dimensions and follows the rules of Euclidean geometry. A vector space can have any number of dimensions and does not necessarily follow the rules of Euclidean geometry. However, all Euclidean spaces are vector spaces.

What are some real-life applications of vector spaces?

Vector spaces have a wide range of applications in fields such as physics, engineering, computer graphics, and economics. They can be used to model physical forces, electrical circuits, and economic systems, among others. In computer graphics, vector spaces are used to represent and manipulate objects in 3D space.

How can vector spaces be used in data analysis?

In data analysis, vector spaces are used to represent and analyze large datasets. By converting data points into vectors, techniques such as linear algebra and machine learning algorithms can be applied to find patterns and make predictions. Vector spaces are also used in dimensionality reduction techniques to simplify complex datasets for easier analysis.

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