Vector Spaces: A Comparison of Two Bases in V3(R)

In summary, a vector space is a mathematical structure consisting of a set of objects called vectors and operations such as addition and scalar multiplication. It must satisfy axioms such as closure and distributivity. The basic properties of vector spaces include the existence of a zero vector and additive inverses, as well as the ability to scale vectors by a scalar. Bases are sets of vectors used to represent any vector in the space through linear combinations and can also define dimensions and coordinate systems. A vector space may have multiple bases, and it is related to other mathematical concepts such as matrices, linear transformations, and inner product spaces. It is also used in various fields of mathematics and physics.
  • #1
ashnicholls
50
0
Are these two sets:

A = {(0,2,2)^T, (1,0,1)^T, (1,2,1)^T}

B= {(1,2,0)^T, (2,0,1)^T, (2,2,0)^T}

Bases of V3(R)

I have found equations that show that they span V3(R)

And that both set are linearly independant.

So am I right in saying that they are both bases of V3(R).

Cheers Ash
 
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  • #2
If you have three vectors in R^3 that span, then they must be linearly independent, and if you have three linearly independent vectors in R^3 then they must span. That follows from the definition of span and basis.
 
  • #3


Yes, you are correct in saying that both sets A and B are bases of V3(R). In order for a set to be considered a basis of a vector space, it must satisfy two conditions: linear independence and spanning the space.

From the information provided, we can see that both sets A and B are linearly independent, meaning that none of the vectors in each set can be written as a linear combination of the other vectors. This is an important property for a basis because it ensures that there is no redundancy in the set and each vector is necessary to span the entire space.

Additionally, both sets A and B span V3(R), meaning that any vector in V3(R) can be written as a linear combination of the vectors in the set. This is also a crucial property for a basis because it ensures that the set can generate all possible vectors in the space.

Therefore, based on the information provided, we can conclude that both sets A and B are indeed bases of V3(R).
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects called vectors, along with operations (such as addition and scalar multiplication) that can be performed on these vectors. A vector space must satisfy a set of axioms, including closure, associativity, commutativity, and distributivity, in order to be considered a valid vector space.

2. What are the basic properties of vector spaces?

The basic properties of vector spaces include the existence of a zero vector, the existence of additive inverses, and the ability to scale vectors by a scalar. Additionally, vector spaces must be closed under addition and scalar multiplication, and the operations must be associative, commutative, and distributive.

3. How are bases used in vector spaces?

A basis is a set of vectors that can be used to represent any vector in a vector space through linear combinations. Bases are important in vector spaces because they allow for a more compact and efficient representation of vectors, and they can also be used to define dimensions and coordinate systems within the space.

4. Can a vector space have multiple bases?

Yes, a vector space can have multiple bases. In fact, any set of linearly independent vectors in a vector space can be considered a basis for that space. However, different bases may have different numbers of vectors or different combinations of vectors, depending on the specific vector space.

5. How are vector spaces related to other mathematical concepts?

Vector spaces are closely related to other mathematical concepts such as matrices, linear transformations, and inner product spaces. They are also used in various fields of mathematics and physics, including linear algebra, calculus, and quantum mechanics.

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