A vector space over a F\mathbb F is a VV with operations () +:V×V→V+:V\times V\to V and ⋅:F×V→V\cdot:\mathbb F\times V\to V, and an element 0∈V0\in V, satisfying the following for all u,v,w∈Vu,v,w\in V and a,b∈Fa,b\in\mathbb F:

  • Commutativity: u+v=v+uu+v=v+u.
  • Associativity: (u+v)+w=u+(v+w)(u+v)+w=u+(v+w).
  • Additive identity: v+0=vv+0=v.
  • Additive inverse: for each vv, there is −v∈V-v\in V with v+(−v)=0v+(-v)=0.
  • Distributivity over vector addition: a⋅(u+v)=a⋅u+a⋅va\cdot(u+v)=a\cdot u+a\cdot v.
  • Distributivity over scalar addition: (a+b)⋅v=a⋅v+b⋅v(a+b)\cdot v=a\cdot v+b\cdot v.
  • Scalar associativity: (ab)⋅v=a⋅(b⋅v)(ab)\cdot v=a\cdot(b\cdot v).
  • Scalar identity: 1⋅v=v1\cdot v=v.
Context

Vector spaces are the basic objects studied via and invariants including the and of operators. A vector space over F\mathbb F is the same thing as a over the field F\mathbb F.

Examples

The zero vector space, consisting only of 00, is allowed. The field is part of the structure: the same additive group can be a vector space over more than one field, but the scalar multiplication must be specified.

  • Rn\mathbb{R}^n with componentwise addition and scalar multiplication is a vector space over R\mathbb{R}.
  • The set of polynomials F[x]\mathbb{F}[x] with the usual addition and scalar multiplication is a vector space over F\mathbb{F}.
  • The set of m×nm\times n matrices over F\mathbb{F} is a vector space over F\mathbb{F}.