Dimension of a vector space

In mathematics, the dimension of a vector space V is the cardinality (i.e. the number of vectors) of a basis of V over its base field.

For every vector space there exists a basis, and all bases of a vector space have equal cardinality; as a result, the dimension of a vector space is uniquely defined. We say V is finite-dimensional if the dimension of V is , and infinite-dimensional if its dimension is infinite.

The dimension of the vector space V over the field F can be written as dim_F(V) or as [V : F], read "dimension of V over F". When F can be inferred from context, dim(V) is typically written.

The vector space R³ has

as a basis, and therefore we have dim_R(R³) = 3. More generally, dim_R(Rⁿ) = n, and even more generally, dim_F(Fⁿ) = n for any field F.

The complex numbers C are both a real and complex vector space; we have dim_R(C) = 2 and dim_C(C) = 1. So the dimension depends on the base field.

The only vector space with dimension 0 is {0}, the vector space consisting only of its zero element.

If W is a linear subspace of V, then dim(W) ≤ dim(V).

To show that two finite-dimensional vector spaces are equal, one often uses the following criterion: if V is a finite-dimensional vector space and W is a linear subspace of V with dim(W) = dim(V), then W = V.

Rⁿ has the standard basis {e₁, ..., e_n}, where e_i is the i-th column of the corresponding identity matrix. Therefore Rⁿ has dimension n.

Any two vector spaces over F having the same dimension are isomorphic. Any bijective map between their bases can be uniquely extended to a bijective linear map between the vector spaces. If B is some set, a vector space with dimension |B| over F can be constructed as follows: take the set F^(B) of all functions f : B → F such that f(b) = 0 for all but finitely many b in B. These functions can be added and multiplied with elements of F, and we obtain the desired F-vector space.

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