A vector space is a mathematical structure formed by a collection of Element (mathematics)|elements called vectors, which may be Vector addition|added together and Scalar multiplication|multiplied (scaled) by numbers, called scalar (mathematics)|scalars in this context. Scalars are often taken to be real numbers, but there are also vector spaces with scalar multiplication by complex numbers, rational numbers, or generally any field (mathematics)|field. The operations of vector addition and scalar multiplication must satisfy certain requirements, called axioms, listed #Definition|below. An example of a vector space is that of Euclidean vectors, which may be used to represent physics|physical quantities such as forces: any two forces (of the same type) can be added to yield a third, and the multiplication of a force vector by a real multiplier is another force vector. In the same vein, but in a more geometry|geometric sense, vectors representing displacements in the plane or in three-dimensional space also form vector spaces. Vectors in vector spaces do not necessarily have to be arrow-like objects as they appear in the mentioned examples; one should think of these vectors as abstract mathematical objects which hold specific properties and in some cases, they can be visualized as arrows.
Vector spaces are the subject of linear algebra and are well understood from this point of view, since vector spaces are characterized by their dimension (linear algebra)|dimension, which, roughly speaking, specifies the number of independent directions in the space. A vector space may be endowed with additional structure, such as a norm (mathematics)|norm or inner product. Such spaces arise naturally in mathematical analysis, mainly in the guise of infinite-dimensional function spaces whose vectors are function (mathematics)|functions. Analytical problems call for the ability to decide whether a sequence of vectors Limit of a sequence|converges to a given vector. This is accomplished by considering vector spaces with additional structure, mostly spaces endowed with a suitable topology, thus allowing the consideration of proximity and continuous function|continuity issues. These topological vector spaces, in particular Banach spaces and Hilbert spaces, have a richer theory.
Historically, the first ideas leading to vector spaces can be traced back as far as 17th century's analytic geometry, matrix (mathematics)|matrices, systems of linear equations, and Euclidean vectors. The modern, more abstract treatment, first formulated by Giuseppe Peano in the late 19th century, encompasses more general objects than Euclidean space, but much of the theory can be seen as an extension of classical geometric ideas like line (geometry)|lines, plane (geometry)|planes and their higher-dimensional analogs.
Today, vector spaces are applied throughout mathematics, science and engineering. They are the appropriate linear-algebraic notion to deal with system of linear equations|systems of linear equations; offer a framework for Fourier series|Fourier expansion, which is employed in image compression routines; or provide an environment that can be used for solution techniques for partial differential equations. Furthermore, vector spaces furnish an abstract, coordinate-free way of dealing with geometrical and physical objects such as tensors. This in turn allows the examination of local properties of manifold (mathematics)|manifolds by linearization techniques. Vector spaces may be generalized in several ways, leading to more advanced notions in geometry and abstract algebra.

A vector space is a mathematical structure formed by a collection of Element (mathematics)|elements called vectors, which may be Vector addition|added together and Scalar multiplication|multiplied (scaled) by numbers, called scalar (mathematics)|scalars in this context. Scalars are often taken to be real numbers, but there are also vector spaces with scalar multiplication by complex numbers, rational numbers, or generally any field (mathematics)|field. The operations of vector addition and scalar multiplication must satisfy certain requirements, called axioms, listed #Definition|below. An example of a vector space is that of Euclidean vectors, which may be used to represent physics|physical quantities such as forces: any two forces (of the same type) can be added to yield a third, and the multiplication of a force vector by a real multiplier is another force vector. In the same vein, but in a more geometry|geometric sense, vectors representing displacements in the plane or in...