In mathematics and physics, a vector space (also called a linear space) is a set whose elements, often called vectors, may be added together and multiplied ("scaled") by numbers called scalars. Scalars are often real numbers, but can be complex numbers or, more generally, elements of any field. The operations of vector addition and scalar multiplication must satisfy certain requirements, called vector axioms. The terms real vector space and complex vector space are often used to specify the nature of the scalars: real coordinate space or complex coordinate space.
Vector spaces generalize Euclidean vectors, which allow modeling of physical quantities, such as forces and velocity, that have not only a magnitude, but also a direction. The concept of vector spaces is fundamental for linear algebra, together with the concept of matrix, which allows computing in vector spaces. This provides a concise and synthetic way for manipulating and studying systems of linear equations.
Vector spaces are characterized by their dimension, which, roughly speaking, specifies the number of independent directions in the space. This means that, for two vector spaces with the same dimension, the properties that depend only on the vector-space structure are exactly the same (technically the vector spaces are isomorphic). A vector space is finite-dimensional if its dimension is a natural number. Otherwise, it is infinite-dimensional, and its dimension is an infinite cardinal. Finite-dimensional vector spaces occur naturally in geometry and related areas. Infinite-dimensional vector spaces occur in many areas of mathematics. For example, polynomial rings are countably infinite-dimensional vector spaces, and many function spaces have the cardinality of the continuum as a dimension.
Many vector spaces that are considered in mathematics are also endowed with other structures. This is the case of algebras, which include field extensions, polynomial rings, associative algebras and Lie algebras. This is also the case of topological vector spaces, which include function spaces, inner product spaces, normed spaces, Hilbert spaces and Banach spaces.
Definition and basic properties
In this article, vectors are represented in boldface to distinguish them from scalars.
A vector space over a field F is a set V together with two binary operations that satisfy the eight axioms listed below. In this context, the elements of V are commonly called vectors, and the elements of F are called scalars.
The first operation, called vector addition or simply addition assigns to any two vectors v and w in V a third vector in V which is commonly written as v + w, and called the sum of these two vectors.
The second operation, called scalar multiplication，assigns to any scalar a in F and any vector v in V another vector in V, which is denoted av.
For having a vector space, the eight following axioms must be satisfied for every u, v and w in V, and a and b in F.
|Associativity of vector addition||u + (v + w) = (u + v) + w|
|Commutativity of vector addition||u + v = v + u|
|Identity element of vector addition||There exists an element 0 ∈ V, called the zero vector, such that v + 0 = v for all v ∈ V.|
|Inverse elements of vector addition||For every v ∈ V, there exists an element −v ∈ V, called the additive inverse of v, such that v + (−v) = 0.|
|Compatibility of scalar multiplication with field multiplication||a(bv) = (ab)v [nb 3]|
|Identity element of scalar multiplication||1v = v, where 1 denotes the multiplicative identity in F.|
|Distributivity of scalar multiplication with respect to vector addition||a(u + v) = au + av|
|Distributivity of scalar multiplication with respect to field addition||(a + b)v = av + bv|
When the scalar field is the real numbers the vector space is called a real vector space. When the scalar field is the complex numbers, the vector space is called a complex vector space. These two cases are the most common ones, but vector spaces with scalars in an arbitrary field F are also commonly considered. Such a vector space is called an F-vector space or a vector space over F.
An equivalent definition of a vector space can be given, which is much more concise but less elementary: the first four axioms (related to vector addition) say that a vector space is an abelian group under addition, and the four remaining axioms (related to the scalar multiplication), say that this operation defines a ring homomorphism from the field F into the endomorphism ring of this group.
Subtraction of two vectors can be defined as
v - w = v + (- w )
Direct consequences of the axioms include that, for every s ∈ in F and v ∈ F one has
- 0v= 0
- s0 = 0
- (-1)v = -v
- sv = implies that s=0 or v=0