What is the Definition of a Basis in a Vector Space?

In summary, the dimension of a vector space is equal to the number of vectors in a basis, which is also equal to the number of vectors in a canonic solution for a finite dimensional space like ##\mathcal R^n##.
  • #1
Jalo
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Homework Statement


Is it correct to say that the dimension of a given vector space is equal to the number of vectors of the canonic solution? For example:

Vector space |R3
Canonic solution = {[1 0 0],[0 1 0],[0 0 1]}

Therefore its dimension is 3.

Homework Equations





The Attempt at a Solution



I thought about it, and it made sense. I just want to make sure that I can solve my problems based on this assumption.

By the way, I'm not an english native speaker, therefore I don't know the word for the canonic solution.

Thanks in advance.
D.
 
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  • #2
I'm not familiar with the term "canonical solution" for a vector space. But certainly for a finite dimensional space like ##\mathcal R^n##, the number ##n## of the standard basis vectors is the dimension of the space.

[Edit]I didn't see your comment in the second section about canonical solution. Anyway, yes, they form a basis.
 
  • #3
A basis for a vector space is defined as set of vectors that both span the space and are independent. Essentially, one can show that a really "big" set will span the space and you if they are not independent, you can drop vectors and still span the space. On the other hand, a set containing a single (non-zero) vector must be independent so, it it doesn't span the space, you can add more vectors to the set and it will still be independent.

You can keep removing vectors from spanning sets and adding vector to independent sets until they "meet in the middle". Any two sets of vectors that both span the set and are independent- a basis- must contain the same number of vectors- the "dimension" of the space is defined as the number of vectors in a basis.
 

FAQ: What is the Definition of a Basis in a Vector Space?

What is the definition of a vector space?

A vector space is a mathematical structure that consists of a set of elements, called vectors, and two operations, vector addition and scalar multiplication, that satisfy certain axioms. These axioms include closure, associativity, commutativity, and distributivity, among others. Vector spaces are important in many areas of mathematics and physics, as they provide a framework for understanding and solving problems involving vectors.

How is the dimension of a vector space determined?

The dimension of a vector space is determined by the number of linearly independent vectors in the space. In other words, it is the minimum number of vectors needed to span the entire space. This can be visualized as the number of directions in which a vector can point within the space. For example, a 2-dimensional vector space can be spanned by two linearly independent vectors, while a 3-dimensional vector space requires three linearly independent vectors.

What is the difference between basis and dimension of a vector space?

A basis is a set of linearly independent vectors that can be used to express any vector in a vector space. The dimension of a vector space refers to the number of vectors in a basis. In other words, the basis is a set of vectors, while the dimension is a numerical property of the space.

Can the dimension of a vector space change?

No, the dimension of a vector space is a fundamental property of the space and cannot be changed. It is determined by the structure and properties of the vectors in the space. However, the basis of a vector space can change, which may affect the number of vectors needed to span the space.

How does the dimension of a vector space relate to its subspaces?

The dimension of a vector space is related to its subspaces in the sense that the dimension of a subspace cannot exceed the dimension of the original vector space. This is because the subspace is a subset of the original space and cannot contain more linearly independent vectors than the original space. In fact, the dimension of a subspace can be less than or equal to the dimension of the original space.

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