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mathematics Mathematics is a field of study that discovers and organizes methods, Mathematical theory, theories and theorems that are developed and Mathematical proof, proved for the needs of empirical sciences and mathematics itself. There are many ar ...
and
physics Physics is the scientific study of matter, its Elementary particle, fundamental constituents, its motion and behavior through space and time, and the related entities of energy and force. "Physical science is that department of knowledge whi ...
, a vector space (also called a linear space) is a set whose elements, often called ''vectors'', can be added together and multiplied ("scaled") by numbers called ''scalars''. The operations of vector addition and scalar multiplication must satisfy certain requirements, called ''vector
axiom An axiom, postulate, or assumption is a statement that is taken to be true, to serve as a premise or starting point for further reasoning and arguments. The word comes from the Ancient Greek word (), meaning 'that which is thought worthy or ...
s''. Real vector spaces and complex vector spaces are kinds of vector spaces based on different kinds of scalars:
real numbers In mathematics, a real number is a number that can be used to measurement, measure a continuous variable, continuous one-dimensional quantity such as a time, duration or temperature. Here, ''continuous'' means that pairs of values can have arbi ...
and complex numbers. Scalars can also be, more generally, elements of any field. Vector spaces generalize Euclidean vectors, which allow modeling of
physical quantities A physical quantity (or simply quantity) is a property of a material or system that can be quantified by measurement. A physical quantity can be expressed as a ''value'', which is the algebraic multiplication of a '' numerical value'' and a '' ...
(such as
force In physics, a force is an influence that can cause an Physical object, object to change its velocity unless counterbalanced by other forces. In mechanics, force makes ideas like 'pushing' or 'pulling' mathematically precise. Because the Magnitu ...
s and
velocity Velocity is a measurement of speed in a certain direction of motion. It is a fundamental concept in kinematics, the branch of classical mechanics that describes the motion of physical objects. Velocity is a vector (geometry), vector Physical q ...
) that have not only a magnitude, but also a direction. The concept of vector spaces is fundamental for
linear algebra Linear algebra is the branch of mathematics concerning linear equations such as :a_1x_1+\cdots +a_nx_n=b, linear maps such as :(x_1, \ldots, x_n) \mapsto a_1x_1+\cdots +a_nx_n, and their representations in vector spaces and through matrix (mathemat ...
, together with the concept of matrices, 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 In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coo ...
, which, roughly speaking, specifies the number of independent directions in the space. This means that, for two vector spaces over a given field and with the same dimension, the properties that depend only on the vector-space structure are exactly the same (technically the vector spaces are
isomorphic In mathematics, an isomorphism is a structure-preserving mapping or morphism between two structures of the same type that can be reversed by an inverse mapping. Two mathematical structures are isomorphic if an isomorphism exists between the ...
). A vector space is ''finite-dimensional'' if its dimension is a
natural number In mathematics, the natural numbers are the numbers 0, 1, 2, 3, and so on, possibly excluding 0. Some start counting with 0, defining the natural numbers as the non-negative integers , while others start with 1, defining them as the positive in ...
. Otherwise, it is ''infinite-dimensional'', and its dimension is an infinite cardinal. Finite-dimensional vector spaces occur naturally in
geometry Geometry (; ) is a branch of mathematics concerned with properties of space such as the distance, shape, size, and relative position of figures. Geometry is, along with arithmetic, one of the oldest branches of mathematics. A mathematician w ...
and related areas. Infinite-dimensional vector spaces occur in many areas of mathematics. For example,
polynomial ring In mathematics, especially in the field of algebra, a polynomial ring or polynomial algebra is a ring formed from the set of polynomials in one or more indeterminates (traditionally also called variables) with coefficients in another ring, ...
s are countably infinite-dimensional vector spaces, and many
function space In mathematics, a function space is a set of functions between two fixed sets. Often, the domain and/or codomain will have additional structure which is inherited by the function space. For example, the set of functions from any set into a ve ...
s 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 algebra In mathematics, a Lie algebra (pronounced ) is a vector space \mathfrak g together with an operation called the Lie bracket, an alternating bilinear map \mathfrak g \times \mathfrak g \rightarrow \mathfrak g, that satisfies the Jacobi ident ...
s. This is also the case of
topological vector space In mathematics, a topological vector space (also called a linear topological space and commonly abbreviated TVS or t.v.s.) is one of the basic structures investigated in functional analysis. A topological vector space is a vector space that is als ...
s, which include function spaces, inner product spaces, normed spaces, Hilbert spaces and
Banach space In mathematics, more specifically in functional analysis, a Banach space (, ) is a complete normed vector space. Thus, a Banach space is a vector space with a metric that allows the computation of vector length and distance between vectors and ...
s.


Definition and basic properties

In this article, vectors are represented in boldface to distinguish them from scalars.It is also common, especially in physics, to denote vectors with an arrow on top: \vec v. It is also common, especially in higher mathematics, to not use any typographical method for distinguishing vectors from other mathematical objects. A vector space over a field is a non-empty set  together with a
binary operation In mathematics, a binary operation or dyadic operation is a rule for combining two elements (called operands) to produce another element. More formally, a binary operation is an operation of arity two. More specifically, a binary operation ...
and a binary function that satisfy the eight
axiom An axiom, postulate, or assumption is a statement that is taken to be true, to serve as a premise or starting point for further reasoning and arguments. The word comes from the Ancient Greek word (), meaning 'that which is thought worthy or ...
s listed below. In this context, the elements of are commonly called ''vectors'', and the elements of  are called ''scalars''. * The binary operation, called ''vector addition'' or simply ''addition'' assigns to any two vectors  and in a third vector in which is commonly written as , and called the ''sum'' of these two vectors. * The binary function, called '' scalar multiplication'', assigns to any scalar  in and any vector  in another vector in , which is denoted .Scalar multiplication is not to be confused with the scalar product, which is an additional operation on some specific vector spaces, called inner product spaces. Scalar multiplication is the multiplication of a vector ''by'' a scalar that produces a vector, while the scalar product is a multiplication of two vectors that produces a scalar. To have a vector space, the eight following
axiom An axiom, postulate, or assumption is a statement that is taken to be true, to serve as a premise or starting point for further reasoning and arguments. The word comes from the Ancient Greek word (), meaning 'that which is thought worthy or ...
s must be satisfied for every , and in , and and in . When the scalar field is the
real number In mathematics, a real number is a number that can be used to measure a continuous one- dimensional quantity such as a duration or temperature. Here, ''continuous'' means that pairs of values can have arbitrarily small differences. Every re ...
s, the vector space is called a ''real vector space'', and when the scalar field is the
complex number In mathematics, a complex number is an element of a number system that extends the real numbers with a specific element denoted , called the imaginary unit and satisfying the equation i^= -1; every complex number can be expressed in the for ...
s, 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 are also commonly considered. Such a vector space is called an ''vector space'' or a ''vector space over ''. 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 into the endomorphism ring of this group. Subtraction of two vectors can be defined as \mathbf - \mathbf = \mathbf + (-\mathbf). Direct consequences of the axioms include that, for every s\in F and \mathbf v\in V, one has *0\mathbf v = \mathbf 0, *s\mathbf 0=\mathbf 0, *(-1)\mathbf v = -\mathbf v, *s\mathbf v = \mathbf 0 implies s=0 or \mathbf v= \mathbf 0. Even more concisely, a vector space is a module over a field.


Bases, vector coordinates, and subspaces

;
Linear combination In mathematics, a linear combination or superposition is an Expression (mathematics), expression constructed from a Set (mathematics), set of terms by multiplying each term by a constant and adding the results (e.g. a linear combination of ''x'' a ...
: Given a set of elements of a -vector space , a linear combination of elements of is an element of of the form a_1 \mathbf_1 + a_2 \mathbf_2 + \cdots + a_k \mathbf_k, where a_1, \ldots, a_k\in F and \mathbf_1, \ldots, \mathbf_k\in G. The scalars a_1, \ldots, a_k are called the ''coefficients'' of the linear combination. ;
Linear independence In the theory of vector spaces, a set (mathematics), set of vector (mathematics), vectors is said to be if there exists no nontrivial linear combination of the vectors that equals the zero vector. If such a linear combination exists, then th ...
:The elements of a subset of a -vector space are said to be ''linearly independent'' if no element of can be written as a linear combination of the other elements of . Equivalently, they are linearly independent if two linear combinations of elements of define the same element of if and only if they have the same coefficients. Also equivalently, they are linearly independent if a linear combination results in the zero vector if and only if all its coefficients are zero. ;
Linear subspace In mathematics, the term ''linear'' is used in two distinct senses for two different properties: * linearity of a ''function (mathematics), function'' (or ''mapping (mathematics), mapping''); * linearity of a ''polynomial''. An example of a li ...
:A ''linear subspace'' or ''vector subspace'' of a vector space is a non-empty subset of that is closed under vector addition and scalar multiplication; that is, the sum of two elements of and the product of an element of by a scalar belong to . This implies that every linear combination of elements of belongs to . A linear subspace is a vector space for the induced addition and scalar multiplication; this means that the closure property implies that the axioms of a vector space are satisfied.
The closure property also implies that ''every
intersection In mathematics, the intersection of two or more objects is another object consisting of everything that is contained in all of the objects simultaneously. For example, in Euclidean geometry, when two lines in a plane are not parallel, their ...
of linear subspaces is a linear subspace.'' ; Linear span :Given a subset of a vector space , the ''linear span'' or simply the ''span'' of is the smallest linear subspace of that contains , in the sense that it is the intersection of all linear subspaces that contain . The span of is also the set of all linear combinations of elements of .
If is the span of , one says that ''spans'' or ''generates'' , and that is a '' spanning set'' or a ''generating set'' of . ; Basis and
dimension In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coo ...
:A subset of a vector space is a ''basis'' if its elements are linearly independent and span the vector space. Every vector space has at least one basis, or many in general (see ). Moreover, all bases of a vector space have the same
cardinality The thumb is the first digit of the hand, next to the index finger. When a person is standing in the medical anatomical position (where the palm is facing to the front), the thumb is the outermost digit. The Medical Latin English noun for thum ...
, which is called the ''dimension'' of the vector space (see
Dimension theorem for vector spaces In mathematics, the dimension theorem for vector spaces states that all bases of a vector space have equally many elements. This number of elements may be finite or infinite (in the latter case, it is a cardinal number), and defines the dimension ...
). This is a fundamental property of vector spaces, which is detailed in the remainder of the section. ''Bases'' are a fundamental tool for the study of vector spaces, especially when the dimension is finite. In the infinite-dimensional case, the existence of infinite bases, often called Hamel bases, depends on the axiom of choice. It follows that, in general, no base can be explicitly described. For example, the
real number In mathematics, a real number is a number that can be used to measure a continuous one- dimensional quantity such as a duration or temperature. Here, ''continuous'' means that pairs of values can have arbitrarily small differences. Every re ...
s form an infinite-dimensional vector space over the
rational number In mathematics, a rational number is a number that can be expressed as the quotient or fraction of two integers, a numerator and a non-zero denominator . For example, is a rational number, as is every integer (for example, The set of all ...
s, for which no specific basis is known. Consider a basis (\mathbf_1, \mathbf_2 , \ldots, \mathbf_n) of a vector space of dimension over a field . The definition of a basis implies that every \mathbf v \in V may be written \mathbf v = a_1 \mathbf b_1 + \cdots + a_n \mathbf b_n, with a_1,\dots, a_n in , and that this decomposition is unique. The scalars a_1, \ldots, a_n are called the ''coordinates'' of on the basis. They are also said to be the ''coefficients'' of the decomposition of on the basis. One also says that the -
tuple In mathematics, a tuple is a finite sequence or ''ordered list'' of numbers or, more generally, mathematical objects, which are called the ''elements'' of the tuple. An -tuple is a tuple of elements, where is a non-negative integer. There is o ...
of the coordinates is the coordinate vector of on the basis, since the set F^n of the -tuples of elements of is a vector space for componentwise addition and scalar multiplication, whose dimension is . The one-to-one correspondence between vectors and their coordinate vectors maps vector addition to vector addition and scalar multiplication to scalar multiplication. It is thus a vector space isomorphism, which allows translating reasonings and computations on vectors into reasonings and computations on their coordinates.


History

Vector spaces stem from
affine geometry In mathematics, affine geometry is what remains of Euclidean geometry when ignoring (mathematicians often say "forgetting") the metric notions of distance and angle. As the notion of '' parallel lines'' is one of the main properties that is i ...
, via the introduction of coordinates in the plane or three-dimensional space. Around 1636, French mathematicians
René Descartes René Descartes ( , ; ; 31 March 1596 – 11 February 1650) was a French philosopher, scientist, and mathematician, widely considered a seminal figure in the emergence of modern philosophy and Modern science, science. Mathematics was paramou ...
and
Pierre de Fermat Pierre de Fermat (; ; 17 August 1601 – 12 January 1665) was a French mathematician who is given credit for early developments that led to infinitesimal calculus, including his technique of adequality. In particular, he is recognized for his d ...
founded analytic geometry by identifying solutions to an equation of two variables with points on a plane
curve In mathematics, a curve (also called a curved line in older texts) is an object similar to a line, but that does not have to be straight. Intuitively, a curve may be thought of as the trace left by a moving point. This is the definition that ...
. To achieve geometric solutions without using coordinates, Bolzano introduced, in 1804, certain operations on points, lines, and planes, which are predecessors of vectors. introduced the notion of barycentric coordinates. introduced an equivalence relation on directed line segments that share the same length and direction which he called equipollence. A Euclidean vector is then an equivalence class of that relation. Vectors were reconsidered with the presentation of complex numbers by Argand and Hamilton and the inception of
quaternion In mathematics, the quaternion number system extends the complex numbers. Quaternions were first described by the Irish mathematician William Rowan Hamilton in 1843 and applied to mechanics in three-dimensional space. The algebra of quater ...
s by the latter. They are elements in R2 and R4; treating them using
linear combination In mathematics, a linear combination or superposition is an Expression (mathematics), expression constructed from a Set (mathematics), set of terms by multiplying each term by a constant and adding the results (e.g. a linear combination of ''x'' a ...
s goes back to Laguerre in 1867, who also defined systems of linear equations. In 1857, Cayley introduced the matrix notation which allows for harmonization and simplification of
linear map In mathematics, and more specifically in linear algebra, a linear map (also called a linear mapping, linear transformation, vector space homomorphism, or in some contexts linear function) is a mapping V \to W between two vector spaces that p ...
s. Around the same time,
Grassmann Hermann Günther Grassmann (, ; 15 April 1809 – 26 September 1877) was a German polymath known in his day as a linguistics, linguist and now also as a mathematician. He was also a physicist, general scholar, and publisher. His mathematical w ...
studied the barycentric calculus initiated by Möbius. He envisaged sets of abstract objects endowed with operations. In his work, the concepts of
linear independence In the theory of vector spaces, a set (mathematics), set of vector (mathematics), vectors is said to be if there exists no nontrivial linear combination of the vectors that equals the zero vector. If such a linear combination exists, then th ...
and
dimension In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coo ...
, as well as scalar products are present. Grassmann's 1844 work exceeds the framework of vector spaces as well since his considering multiplication led him to what are today called algebras. Italian mathematician Peano was the first to give the modern definition of vector spaces and linear maps in 1888, although he called them "linear systems". Peano's axiomatization allowed for vector spaces with infinite dimension, but Peano did not develop that theory further. In 1897, Salvatore Pincherle adopted Peano's axioms and made initial inroads into the theory of infinite-dimensional vector spaces. An important development of vector spaces is due to the construction of function spaces by Henri Lebesgue. This was later formalized by Banach and
Hilbert David Hilbert (; ; 23 January 1862 – 14 February 1943) was a German mathematician and philosophy of mathematics, philosopher of mathematics and one of the most influential mathematicians of his time. Hilbert discovered and developed a broad ...
, around 1920. At that time,
algebra Algebra is a branch of mathematics that deals with abstract systems, known as algebraic structures, and the manipulation of expressions within those systems. It is a generalization of arithmetic that introduces variables and algebraic ope ...
and the new field of
functional analysis Functional analysis is a branch of mathematical analysis, the core of which is formed by the study of vector spaces endowed with some kind of limit-related structure (for example, Inner product space#Definition, inner product, Norm (mathematics ...
began to interact, notably with key concepts such as spaces of ''p''-integrable functions and Hilbert spaces.


Examples


Arrows in the plane

The first example of a vector space consists of
arrow An arrow is a fin-stabilized projectile launched by a bow. A typical arrow usually consists of a long, stiff, straight shaft with a weighty (and usually sharp and pointed) arrowhead attached to the front end, multiple fin-like stabilizers c ...
s in a fixed plane, starting at one fixed point. This is used in physics to describe
force In physics, a force is an influence that can cause an Physical object, object to change its velocity unless counterbalanced by other forces. In mechanics, force makes ideas like 'pushing' or 'pulling' mathematically precise. Because the Magnitu ...
s or velocities. Given any two such arrows, and , the parallelogram spanned by these two arrows contains one diagonal arrow that starts at the origin, too. This new arrow is called the ''sum'' of the two arrows, and is denoted . In the special case of two arrows on the same line, their sum is the arrow on this line whose length is the sum or the difference of the lengths, depending on whether the arrows have the same direction. Another operation that can be done with arrows is scaling: given any positive
real number In mathematics, a real number is a number that can be used to measure a continuous one- dimensional quantity such as a duration or temperature. Here, ''continuous'' means that pairs of values can have arbitrarily small differences. Every re ...
, the arrow that has the same direction as , but is dilated or shrunk by multiplying its length by , is called ''multiplication'' of by . It is denoted . When is negative, is defined as the arrow pointing in the opposite direction instead. The following shows a few examples: if , the resulting vector has the same direction as , but is stretched to the double length of (the second image). Equivalently, is the sum . Moreover, has the opposite direction and the same length as (blue vector pointing down in the second image).


Ordered pairs of numbers

A second key example of a vector space is provided by pairs of real numbers and . The order of the components and is significant, so such a pair is also called an ordered pair. Such a pair is written as . The sum of two such pairs and the multiplication of a pair with a number is defined as follows: \begin (x_1 , y_1) + (x_2 , y_2) &= (x_1 + x_2, y_1 + y_2), \\ a(x, y) &= (ax, ay). \end The first example above reduces to this example if an arrow is represented by a pair of Cartesian coordinates of its endpoint.


Coordinate space

The simplest example of a vector space over a field is the field itself with its addition viewed as vector addition and its multiplication viewed as scalar multiplication. More generally, all -tuples (sequences of length ) (a_1, a_2, \dots, a_n) of elements of form a vector space that is usually denoted and called a coordinate space. The case is the above-mentioned simplest example, in which the field is also regarded as a vector space over itself. The case and (so R2) reduces to the previous example.


Complex numbers and other field extensions

The set of complex numbers , numbers that can be written in the form for
real numbers In mathematics, a real number is a number that can be used to measurement, measure a continuous variable, continuous one-dimensional quantity such as a time, duration or temperature. Here, ''continuous'' means that pairs of values can have arbi ...
and where is the imaginary unit, form a vector space over the reals with the usual addition and multiplication: and for real numbers , , , and . The various axioms of a vector space follow from the fact that the same rules hold for complex number arithmetic. The example of complex numbers is essentially the same as (that is, it is ''isomorphic'' to) the vector space of ordered pairs of real numbers mentioned above: if we think of the complex number as representing the ordered pair in the complex plane then we see that the rules for addition and scalar multiplication correspond exactly to those in the earlier example. More generally, field extensions provide another class of examples of vector spaces, particularly in algebra and
algebraic number theory Algebraic number theory is a branch of number theory that uses the techniques of abstract algebra to study the integers, rational numbers, and their generalizations. Number-theoretic questions are expressed in terms of properties of algebraic ob ...
: a field containing a smaller field is an -vector space, by the given multiplication and addition operations of . For example, the complex numbers are a vector space over , and the field extension \mathbf(i\sqrt) is a vector space over .


Function spaces

Functions from any fixed set to a field also form vector spaces, by performing addition and scalar multiplication pointwise. That is, the sum of two functions and is the function (f + g) given by (f + g)(w) = f(w) + g(w), and similarly for multiplication. Such function spaces occur in many geometric situations, when is the
real line A number line is a graphical representation of a straight line that serves as spatial representation of numbers, usually graduated like a ruler with a particular origin (geometry), origin point representing the number zero and evenly spaced mark ...
or an interval, or other
subset In mathematics, a Set (mathematics), set ''A'' is a subset of a set ''B'' if all Element (mathematics), elements of ''A'' are also elements of ''B''; ''B'' is then a superset of ''A''. It is possible for ''A'' and ''B'' to be equal; if they a ...
s of . Many notions in topology and analysis, such as continuity, integrability or differentiability are well-behaved with respect to linearity: sums and scalar multiples of functions possessing such a property still have that property. Therefore, the set of such functions are vector spaces, whose study belongs to
functional analysis Functional analysis is a branch of mathematical analysis, the core of which is formed by the study of vector spaces endowed with some kind of limit-related structure (for example, Inner product space#Definition, inner product, Norm (mathematics ...
.


Linear equations

Systems of homogeneous linear equations are closely tied to vector spaces. For example, the solutions of \begin && a \,&&+\, 3 b \,&\, + &\, & c & \,= 0 \\ 4 && a \,&&+\, 2 b \,&\, + &\, 2 & c & \,= 0 \\ \end are given by triples with arbitrary a, b = a / 2, and c = -5 a / 2. They form a vector space: sums and scalar multiples of such triples still satisfy the same ratios of the three variables; thus they are solutions, too. Matrices can be used to condense multiple linear equations as above into one vector equation, namely
A \mathbf = \mathbf,
where A = \begin 1 & 3 & 1 \\ 4 & 2 & 2\end is the matrix containing the coefficients of the given equations, \mathbf is the vector (a, b, c), A \mathbf denotes the matrix product, and \mathbf = (0, 0) is the zero vector. In a similar vein, the solutions of homogeneous ''linear differential equations'' form vector spaces. For example,
f^(x) + 2 f^\prime(x) + f(x) = 0
yields f(x) = a e^ + b x e^, where a and b are arbitrary constants, and e^x is the natural exponential function.


Linear maps and matrices

The relation of two vector spaces can be expressed by ''linear map'' or '' linear transformation''. They are functions that reflect the vector space structure, that is, they preserve sums and scalar multiplication: \begin f(\mathbf + \mathbf) &= f(\mathbf) + f(\mathbf), \\ f(a \cdot \mathbf) &= a \cdot f(\mathbf) \end for all \mathbf and \mathbf in V, all a in F. An ''
isomorphism In mathematics, an isomorphism is a structure-preserving mapping or morphism between two structures of the same type that can be reversed by an inverse mapping. Two mathematical structures are isomorphic if an isomorphism exists between the ...
'' is a linear map such that there exists an inverse map , which is a map such that the two possible compositions and are identity maps. Equivalently, is both one-to-one ( injective) and onto ( surjective). If there exists an isomorphism between and , the two spaces are said to be ''isomorphic''; they are then essentially identical as vector spaces, since all identities holding in are, via , transported to similar ones in , and vice versa via . For example, the arrows in the plane and the ordered pairs of numbers vector spaces in the introduction above (see ) are isomorphic: a planar arrow departing at the origin of some (fixed) coordinate system can be expressed as an ordered pair by considering the - and -component of the arrow, as shown in the image at the right. Conversely, given a pair , the arrow going by to the right (or to the left, if is negative), and up (down, if is negative) turns back the arrow . Linear maps between two vector spaces form a vector space , also denoted , or . The space of linear maps from to is called the '' dual vector space'', denoted . Via the injective natural map , any vector space can be embedded into its ''bidual''; the map is an isomorphism if and only if the space is finite-dimensional. Once a basis of is chosen, linear maps are completely determined by specifying the images of the basis vectors, because any element of is expressed uniquely as a linear combination of them. If , a 1-to-1 correspondence between fixed bases of and gives rise to a linear map that maps any basis element of to the corresponding basis element of . It is an isomorphism, by its very definition. Therefore, two vector spaces over a given field are isomorphic if their dimensions agree and vice versa. Another way to express this is that any vector space over a given field is ''completely classified'' ( up to isomorphism) by its dimension, a single number. In particular, any ''n''-dimensional -vector space is isomorphic to . However, there is no "canonical" or preferred isomorphism; an isomorphism is equivalent to the choice of a basis of , by mapping the standard basis of to , via .


Matrices

''Matrices'' are a useful notion to encode linear maps. They are written as a rectangular array of scalars as in the image at the right. Any -by- matrix A gives rise to a linear map from to , by the following \mathbf x = (x_1, x_2, \ldots, x_n) \mapsto \left(\sum_^n a_x_j, \sum_^n a_x_j, \ldots, \sum_^n a_x_j \right), where \sum denotes
summation In mathematics, summation is the addition of a sequence of numbers, called ''addends'' or ''summands''; the result is their ''sum'' or ''total''. Beside numbers, other types of values can be summed as well: functions, vectors, matrices, pol ...
, or by using the matrix multiplication of the matrix A with the coordinate vector \mathbf:
\mathbf \mapsto A \mathbf.
Moreover, after choosing bases of and , ''any'' linear map is uniquely represented by a matrix via this assignment. The
determinant In mathematics, the determinant is a Scalar (mathematics), scalar-valued function (mathematics), function of the entries of a square matrix. The determinant of a matrix is commonly denoted , , or . Its value characterizes some properties of the ...
of a square matrix is a scalar that tells whether the associated map is an isomorphism or not: to be so it is sufficient and necessary that the determinant is nonzero. The linear transformation of corresponding to a real ''n''-by-''n'' matrix is orientation preserving if and only if its determinant is positive.


Eigenvalues and eigenvectors

Endomorphisms, linear maps , are particularly important since in this case vectors can be compared with their image under , . Any nonzero vector satisfying , where is a scalar, is called an ''eigenvector'' of with ''eigenvalue'' . Equivalently, is an element of the kernel of the difference (where Id is the
identity map Graph of the identity function on the real numbers In mathematics, an identity function, also called an identity relation, identity map or identity transformation, is a function that always returns the value that was used as its argument, unc ...
. If is finite-dimensional, this can be rephrased using determinants: having eigenvalue is equivalent to \det(f - \lambda \cdot \operatorname) = 0. By spelling out the definition of the determinant, the expression on the left hand side can be seen to be a polynomial function in , called the characteristic polynomial of . If the field is large enough to contain a zero of this polynomial (which automatically happens for algebraically closed, such as ) any linear map has at least one eigenvector. The vector space may or may not possess an eigenbasis, a basis consisting of eigenvectors. This phenomenon is governed by the Jordan canonical form of the map. The set of all eigenvectors corresponding to a particular eigenvalue of forms a vector space known as the ''eigenspace'' corresponding to the eigenvalue (and ) in question.


Basic constructions

In addition to the above concrete examples, there are a number of standard linear algebraic constructions that yield vector spaces related to given ones.


Subspaces and quotient spaces

A nonempty
subset In mathematics, a Set (mathematics), set ''A'' is a subset of a set ''B'' if all Element (mathematics), elements of ''A'' are also elements of ''B''; ''B'' is then a superset of ''A''. It is possible for ''A'' and ''B'' to be equal; if they a ...
W of a vector space V that is closed under addition and scalar multiplication (and therefore contains the \mathbf-vector of V) is called a ''linear subspace'' of V , or simply a ''subspace'' of V , when the ambient space is unambiguously a vector space.This is typically the case when a vector space is also considered as an
affine space In mathematics, an affine space is a geometric structure that generalizes some of the properties of Euclidean spaces in such a way that these are independent of the concepts of distance and measure of angles, keeping only the properties relat ...
. In this case, a linear subspace contains the zero vector, while an affine subspace does not necessarily contain it.
Subspaces of V are vector spaces (over the same field) in their own right. The intersection of all subspaces containing a given set S of vectors is called its span, and it is the smallest subspace of V containing the set S. Expressed in terms of elements, the span is the subspace consisting of all the
linear combination In mathematics, a linear combination or superposition is an Expression (mathematics), expression constructed from a Set (mathematics), set of terms by multiplying each term by a constant and adding the results (e.g. a linear combination of ''x'' a ...
s of elements of S. Linear subspace of dimension 1 and 2 are referred to as a ''line'' (also ''vector line''), and a ''plane'' respectively. If ''W'' is an ''n''-dimensional vector space, any subspace of dimension 1 less, i.e., of dimension n-1 is called a ''
hyperplane In geometry, a hyperplane is a generalization of a two-dimensional plane in three-dimensional space to mathematical spaces of arbitrary dimension. Like a plane in space, a hyperplane is a flat hypersurface, a subspace whose dimension is ...
''. The counterpart to subspaces are ''quotient vector spaces''. Given any subspace W \subseteq V, the quotient space V / W ("V
modulo In computing and mathematics, the modulo operation returns the remainder or signed remainder of a division, after one number is divided by another, the latter being called the '' modulus'' of the operation. Given two positive numbers and , mo ...
W") is defined as follows: as a set, it consists of \mathbf + W = \, where \mathbf is an arbitrary vector in V. The sum of two such elements \mathbf_1 + W and \mathbf_2 + W is \left(\mathbf_1 + \mathbf_2\right) + W, and scalar multiplication is given by a \cdot (\mathbf + W) = (a \cdot \mathbf) + W. The key point in this definition is that \mathbf_1 + W = \mathbf_2 + W
if and only if In logic and related fields such as mathematics and philosophy, "if and only if" (often shortened as "iff") is paraphrased by the biconditional, a logical connective between statements. The biconditional is true in two cases, where either bo ...
the difference of \mathbf_1 and \mathbf_2 lies in W.Some authors, such as , choose to start with this equivalence relation and derive the concrete shape of V / W from this. This way, the quotient space "forgets" information that is contained in the subspace W. The kernel \ker(f) of a linear map f : V \to W consists of vectors \mathbf that are mapped to \mathbf in W. The kernel and the
image An image or picture is a visual representation. An image can be Two-dimensional space, two-dimensional, such as a drawing, painting, or photograph, or Three-dimensional space, three-dimensional, such as a carving or sculpture. Images may be di ...
\operatorname(f) = \ are subspaces of V and W, respectively. An important example is the kernel of a linear map \mathbf \mapsto A \mathbf for some fixed matrix A. The kernel of this map is the subspace of vectors \mathbf such that A \mathbf = \mathbf, which is precisely the set of solutions to the system of homogeneous linear equations belonging to A. This concept also extends to linear differential equations a_0 f + a_1 \frac + a_2 \frac + \cdots + a_n \frac = 0, where the coefficients a_i are functions in x, too. In the corresponding map f \mapsto D(f) = \sum_^n a_i \frac, the
derivative In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input. The derivative of a function of a single variable at a chosen input value, when it exists, is t ...
s of the function f appear linearly (as opposed to f^(x)^2, for example). Since differentiation is a linear procedure (that is, (f + g)^\prime = f^\prime + g^\prime and (c \cdot f)^\prime = c \cdot f^\prime for a constant c) this assignment is linear, called a linear differential operator. In particular, the solutions to the differential equation D(f) = 0 form a vector space (over or ). The existence of kernels and images is part of the statement that the category of vector spaces (over a fixed field F) is an abelian category, that is, a corpus of mathematical objects and structure-preserving maps between them (a category) that behaves much like the
category of abelian groups In mathematics, the category Ab has the abelian groups as objects and group homomorphisms as morphisms. This is the prototype of an abelian category: indeed, every small abelian category can be embedded in Ab. Properties The zero object o ...
. Because of this, many statements such as the first isomorphism theorem (also called rank–nullity theorem in matrix-related terms) V / \ker(f) \; \equiv \; \operatorname(f) and the second and third isomorphism theorem can be formulated and proven in a way very similar to the corresponding statements for groups.


Direct product and direct sum

The ''direct product'' of vector spaces and the ''direct sum'' of vector spaces are two ways of combining an indexed family of vector spaces into a new vector space. The ''direct product'' \textstyle of a family of vector spaces V_i consists of the set of all tuples \left(\mathbf_i\right)_, which specify for each index i in some index set I an element \mathbf_i of V_i. Addition and scalar multiplication is performed componentwise. A variant of this construction is the ''direct sum'' \bigoplus_ V_i (also called coproduct and denoted \coprod_V_i), where only tuples with finitely many nonzero vectors are allowed. If the index set I is finite, the two constructions agree, but in general they are different.


Tensor product

The ''tensor product'' V \otimes_F W, or simply V \otimes W, of two vector spaces V and W is one of the central notions of multilinear algebra which deals with extending notions such as linear maps to several variables. A map g : V \times W \to X from the
Cartesian product In mathematics, specifically set theory, the Cartesian product of two sets and , denoted , is the set of all ordered pairs where is an element of and is an element of . In terms of set-builder notation, that is A\times B = \. A table c ...
V \times W is called bilinear if g is linear in both variables \mathbf and \mathbf. That is to say, for fixed \mathbf the map \mathbf \mapsto g(\mathbf, \mathbf) is linear in the sense above and likewise for fixed \mathbf. The tensor product is a particular vector space that is a ''universal'' recipient of bilinear maps g, as follows. It is defined as the vector space consisting of finite (formal) sums of symbols called
tensor In mathematics, a tensor is an algebraic object that describes a multilinear relationship between sets of algebraic objects associated with a vector space. Tensors may map between different objects such as vectors, scalars, and even other ...
s \mathbf_1 \otimes \mathbf_1 + \mathbf_2 \otimes \mathbf_2 + \cdots + \mathbf_n \otimes \mathbf_n, subject to the rules \begin a \cdot (\mathbf \otimes \mathbf) ~&=~ (a \cdot \mathbf) \otimes \mathbf ~=~ \mathbf \otimes (a \cdot \mathbf), && ~~\text a \text \\ (\mathbf_1 + \mathbf_2) \otimes \mathbf ~&=~ \mathbf_1 \otimes \mathbf + \mathbf_2 \otimes \mathbf && \\ \mathbf \otimes (\mathbf_1 + \mathbf_2) ~&=~ \mathbf \otimes \mathbf_1 + \mathbf \otimes \mathbf_2. && \\ \end These rules ensure that the map f from the V \times W to V \otimes W that maps a
tuple In mathematics, a tuple is a finite sequence or ''ordered list'' of numbers or, more generally, mathematical objects, which are called the ''elements'' of the tuple. An -tuple is a tuple of elements, where is a non-negative integer. There is o ...
(\mathbf, \mathbf) to \mathbf \otimes \mathbf is bilinear. The universality states that given ''any'' vector space X and ''any'' bilinear map g : V \times W \to X, there exists a unique map u, shown in the diagram with a dotted arrow, whose composition with f equals g: u(\mathbf \otimes \mathbf) = g(\mathbf, \mathbf). This is called the universal property of the tensor product, an instance of the method—much used in advanced abstract algebra—to indirectly define objects by specifying maps from or to this object.


Vector spaces with additional structure

From the point of view of linear algebra, vector spaces are completely understood insofar as any vector space over a given field is characterized, up to isomorphism, by its dimension. However, vector spaces ''per se'' do not offer a framework to deal with the question—crucial to analysis—whether a sequence of functions converges to another function. Likewise, linear algebra is not adapted to deal with infinite series, since the addition operation allows only finitely many terms to be added. Therefore, the needs of
functional analysis Functional analysis is a branch of mathematical analysis, the core of which is formed by the study of vector spaces endowed with some kind of limit-related structure (for example, Inner product space#Definition, inner product, Norm (mathematics ...
require considering additional structures.
A vector space may be given a partial order \,\leq,\, under which some vectors can be compared. For example, n-dimensional real space \mathbf^n can be ordered by comparing its vectors componentwise. Ordered vector spaces, for example Riesz spaces, are fundamental to Lebesgue integration, which relies on the ability to express a function as a difference of two positive functions f = f^+ - f^-. where f^+ denotes the positive part of f and f^- the negative part.


Normed vector spaces and inner product spaces

"Measuring" vectors is done by specifying a norm, a datum which measures lengths of vectors, or by an inner product, which measures angles between vectors. Norms and inner products are denoted , \mathbf v, and respectively. The datum of an inner product entails that lengths of vectors can be defined too, by defining the associated norm Vector spaces endowed with such data are known as ''normed vector spaces'' and ''inner product spaces'', respectively. Coordinate space F^n can be equipped with the standard
dot product In mathematics, the dot product or scalar productThe term ''scalar product'' means literally "product with a Scalar (mathematics), scalar as a result". It is also used for other symmetric bilinear forms, for example in a pseudo-Euclidean space. N ...
: \lang \mathbf x , \mathbf y \rang = \mathbf x \cdot \mathbf y = x_1 y_1 + \cdots + x_n y_n. In \mathbf^2, this reflects the common notion of the angle between two vectors \mathbf and \mathbf, by the law of cosines: \mathbf x \cdot \mathbf y = \cos\left(\angle (\mathbf x, \mathbf y)\right) \cdot , \mathbf x, \cdot , \mathbf y, . Because of this, two vectors satisfying \lang \mathbf x , \mathbf y \rang = 0 are called
orthogonal In mathematics, orthogonality (mathematics), orthogonality is the generalization of the geometric notion of ''perpendicularity''. Although many authors use the two terms ''perpendicular'' and ''orthogonal'' interchangeably, the term ''perpendic ...
. An important variant of the standard dot product is used in Minkowski space: \mathbf^4 endowed with the Lorentz product \lang \mathbf x , \mathbf y \rang = x_1 y_1 + x_2 y_2 + x_3 y_3 - x_4 y_4. In contrast to the standard dot product, it is not positive definite: \lang \mathbf x , \mathbf x \rang also takes negative values, for example, for \mathbf x = (0, 0, 0, 1). Singling out the fourth coordinate— corresponding to time, as opposed to three space-dimensions—makes it useful for the mathematical treatment of
special relativity In physics, the special theory of relativity, or special relativity for short, is a scientific theory of the relationship between Spacetime, space and time. In Albert Einstein's 1905 paper, Annus Mirabilis papers#Special relativity, "On the Ele ...
. Note that in other conventions time is often written as the first, or "zeroeth" component so that the Lorentz product is written \lang \mathbf x , \mathbf y \rang = - x_0 y_0+x_1 y_1 + x_2 y_2 + x_3 y_3.


Topological vector spaces

Convergence questions are treated by considering vector spaces V carrying a compatible
topology Topology (from the Greek language, Greek words , and ) is the branch of mathematics concerned with the properties of a Mathematical object, geometric object that are preserved under Continuous function, continuous Deformation theory, deformat ...
, a structure that allows one to talk about elements being close to each other. Compatible here means that addition and scalar multiplication have to be continuous maps. Roughly, if \mathbf and \mathbf in V, and a in F vary by a bounded amount, then so do \mathbf + \mathbf and a \mathbf.This requirement implies that the topology gives rise to a uniform structure, , loc = ch. II. To make sense of specifying the amount a scalar changes, the field F also has to carry a topology in this context; a common choice is the reals or the complex numbers. In such ''topological vector spaces'' one can consider series of vectors. The infinite sum \sum_^\infty f_i ~=~ \lim_ f_1 + \cdots + f_n denotes the limit of the corresponding finite partial sums of the sequence f_1, f_2, \ldots of elements of V. For example, the f_i could be (real or complex) functions belonging to some
function space In mathematics, a function space is a set of functions between two fixed sets. Often, the domain and/or codomain will have additional structure which is inherited by the function space. For example, the set of functions from any set into a ve ...
V, in which case the series is a function series. The mode of convergence of the series depends on the topology imposed on the function space. In such cases, pointwise convergence and uniform convergence are two prominent examples. A way to ensure the existence of limits of certain infinite series is to restrict attention to spaces where any Cauchy sequence has a limit; such a vector space is called complete. Roughly, a vector space is complete provided that it contains all necessary limits. For example, the vector space of polynomials on the unit interval , 1 equipped with the topology of uniform convergence is not complete because any continuous function on , 1/math> can be uniformly approximated by a sequence of polynomials, by the Weierstrass approximation theorem. In contrast, the space of ''all'' continuous functions on , 1/math> with the same topology is complete. A norm gives rise to a topology by defining that a sequence of vectors \mathbf_n converges to \mathbf if and only if \lim_ , \mathbf v_n - \mathbf v, = 0. Banach and Hilbert spaces are complete topological vector spaces whose topologies are given, respectively, by a norm and an inner product. Their study—a key piece of
functional analysis Functional analysis is a branch of mathematical analysis, the core of which is formed by the study of vector spaces endowed with some kind of limit-related structure (for example, Inner product space#Definition, inner product, Norm (mathematics ...
—focuses on infinite-dimensional vector spaces, since all norms on finite-dimensional topological vector spaces give rise to the same notion of convergence. The image at the right shows the equivalence of the 1-norm and \infty-norm on \mathbf^2: as the unit "balls" enclose each other, a sequence converges to zero in one norm if and only if it so does in the other norm. In the infinite-dimensional case, however, there will generally be inequivalent topologies, which makes the study of topological vector spaces richer than that of vector spaces without additional data. From a conceptual point of view, all notions related to topological vector spaces should match the topology. For example, instead of considering all linear maps (also called functionals) V \to W, maps between topological vector spaces are required to be continuous. In particular, the (topological) dual space V^* consists of continuous functionals V \to \mathbf (or to \mathbf). The fundamental
Hahn–Banach theorem In functional analysis, the Hahn–Banach theorem is a central result that allows the extension of bounded linear functionals defined on a vector subspace of some vector space to the whole space. The theorem also shows that there are sufficient ...
is concerned with separating subspaces of appropriate topological vector spaces by continuous functionals.


Banach spaces

''
Banach space In mathematics, more specifically in functional analysis, a Banach space (, ) is a complete normed vector space. Thus, a Banach space is a vector space with a metric that allows the computation of vector length and distance between vectors and ...
s'', introduced by Stefan Banach, are complete normed vector spaces. A first example is the vector space \ell^p consisting of infinite vectors with real entries \mathbf = \left(x_1, x_2, \ldots, x_n, \ldots\right) whose p-norm (1 \leq p \leq \infty) given by \, \mathbf\, _\infty := \sup_i , x_i, \qquad \text p = \infty, \text \, \mathbf\, _p := \left(\sum_i , x_i, ^p\right)^\frac \qquad \text p < \infty. The topologies on the infinite-dimensional space \ell^p are inequivalent for different p. For example, the sequence of vectors \mathbf_n = \left(2^, 2^, \ldots, 2^, 0, 0, \ldots\right), in which the first 2^n components are 2^ and the following ones are 0, converges to the zero vector for p = \infty, but does not for p = 1: \, \mathbf_n\, _\infty = \sup (2^, 0) = 2^ \to 0, but \, \mathbf_n\, _1 = \sum_^ 2^ = 2^n \cdot 2^ = 1. More generally than sequences of real numbers, functions f : \Omega \to \Reals are endowed with a norm that replaces the above sum by the Lebesgue integral \, f\, _p := \left(\int_ , f(x), ^p \, \right)^\frac. The space of integrable functions on a given domain \Omega (for example an interval) satisfying \, f\, _p < \infty, and equipped with this norm are called Lebesgue spaces, denoted L^(\Omega).The triangle inequality for \, f + g\, _p \leq \, f\, _p + \, g\, _p is provided by the Minkowski inequality. For technical reasons, in the context of functions one has to identify functions that agree almost everywhere to get a norm, and not only a
seminorm In mathematics, particularly in functional analysis, a seminorm is like a Norm (mathematics), norm but need not be positive definite. Seminorms are intimately connected with convex sets: every seminorm is the Minkowski functional of some Absorbing ...
.
These spaces are complete. (If one uses the Riemann integral instead, the space is complete, which may be seen as a justification for Lebesgue's integration theory. "Many functions in L^2 of Lebesgue measure, being unbounded, cannot be integrated with the classical Riemann integral. So spaces of Riemann integrable functions would not be complete in the L^2 norm, and the orthogonal decomposition would not apply to them. This shows one of the advantages of Lebesgue integration.", , §5.3, p. 125.) Concretely this means that for any sequence of Lebesgue-integrable functions f_1, f_2, \ldots, f_n, \ldots with \, f_n\, _p < \infty, satisfying the condition \lim_ \int_ \left, f_k(x) - f_n(x)\^p \, = 0 there exists a function f(x) belonging to the vector space L^(\Omega) such that \lim_ \int_ \left, f(x) - f_k(x)\^p \, = 0. Imposing boundedness conditions not only on the function, but also on its
derivative In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input. The derivative of a function of a single variable at a chosen input value, when it exists, is t ...
s leads to Sobolev spaces.


Hilbert spaces

Complete inner product spaces are known as ''Hilbert spaces'', in honor of David Hilbert. The Hilbert space L^2(\Omega), with inner product given by \langle f\ , \ g \rangle = \int_\Omega f(x) \overline \, dx, where \overline denotes the complex conjugate of g(x),For p \neq 2, L^p(\Omega) is not a Hilbert space. is a key case. By definition, in a Hilbert space, any Cauchy sequence converges to a limit. Conversely, finding a sequence of functions f_n with desirable properties that approximate a given limit function is equally crucial. Early analysis, in the guise of the Taylor approximation, established an approximation of differentiable functions f by polynomials. By the Stone–Weierstrass theorem, every continuous function on , b/math> can be approximated as closely as desired by a polynomial. A similar approximation technique by trigonometric functions is commonly called Fourier expansion, and is much applied in engineering. More generally, and more conceptually, the theorem yields a simple description of what "basic functions", or, in abstract Hilbert spaces, what basic vectors suffice to generate a Hilbert space H, in the sense that the '' closure'' of their span (that is, finite linear combinations and limits of those) is the whole space. Such a set of functions is called a ''basis'' of H, its cardinality is known as the Hilbert space dimension.A basis of a Hilbert space is not the same thing as a basis of a linear algebra. For distinction, a linear algebra basis for a Hilbert space is called a Hamel basis. Not only does the theorem exhibit suitable basis functions as sufficient for approximation purposes, but also together with the Gram–Schmidt process, it enables one to construct a basis of orthogonal vectors. Such orthogonal bases are the Hilbert space generalization of the coordinate axes in finite-dimensional
Euclidean space Euclidean space is the fundamental space of geometry, intended to represent physical space. Originally, in Euclid's ''Elements'', it was the three-dimensional space of Euclidean geometry, but in modern mathematics there are ''Euclidean spaces ...
. The solutions to various differential equations can be interpreted in terms of Hilbert spaces. For example, a great many fields in physics and engineering lead to such equations, and frequently solutions with particular physical properties are used as basis functions, often orthogonal. As an example from physics, the time-dependent Schrödinger equation in
quantum mechanics Quantum mechanics is the fundamental physical Scientific theory, theory that describes the behavior of matter and of light; its unusual characteristics typically occur at and below the scale of atoms. Reprinted, Addison-Wesley, 1989, It is ...
describes the change of physical properties in time by means of a
partial differential equation In mathematics, a partial differential equation (PDE) is an equation which involves a multivariable function and one or more of its partial derivatives. The function is often thought of as an "unknown" that solves the equation, similar to ho ...
, whose solutions are called wavefunctions. Definite values for physical properties such as energy, or momentum, correspond to eigenvalues of a certain (linear) differential operator and the associated wavefunctions are called eigenstates. The spectral theorem decomposes a linear compact operator acting on functions in terms of these eigenfunctions and their eigenvalues.


Algebras over fields

General vector spaces do not possess a multiplication between vectors. A vector space equipped with an additional
bilinear operator In mathematics, a bilinear map is a function combining elements of two vector spaces to yield an element of a third vector space, and is linear in each of its arguments. Matrix multiplication is an example. A bilinear map can also be defined fo ...
defining the multiplication of two vectors is an ''algebra over a field'' (or ''F''-algebra if the field ''F'' is specified). For example, the set of all polynomials p(t) forms an algebra known as the
polynomial ring In mathematics, especially in the field of algebra, a polynomial ring or polynomial algebra is a ring formed from the set of polynomials in one or more indeterminates (traditionally also called variables) with coefficients in another ring, ...
: using that the sum of two polynomials is a polynomial, they form a vector space; they form an algebra since the product of two polynomials is again a polynomial. Rings of polynomials (in several variables) and their quotients form the basis of
algebraic geometry Algebraic geometry is a branch of mathematics which uses abstract algebraic techniques, mainly from commutative algebra, to solve geometry, geometrical problems. Classically, it studies zero of a function, zeros of multivariate polynomials; th ...
, because they are rings of functions of algebraic geometric objects. Another crucial example are ''
Lie algebra In mathematics, a Lie algebra (pronounced ) is a vector space \mathfrak g together with an operation called the Lie bracket, an alternating bilinear map \mathfrak g \times \mathfrak g \rightarrow \mathfrak g, that satisfies the Jacobi ident ...
s'', which are neither commutative nor associative, but the failure to be so is limited by the constraints ( , y/math> denotes the product of x and y): * , y= - , x/math> (
anticommutativity In mathematics, anticommutativity is a specific property of some non-commutative mathematical operations. Swapping the position of two arguments of an antisymmetric operation yields a result which is the ''inverse'' of the result with unswapped ...
), and * , [y, z + [y, [z, x">,_z.html" ;"title=", [y, z">, [y, z + [y, [z, x + [z, [x, y">,_z">,_[y,_z<_a>_+_[y,_[z,_x.html" ;"title=",_z.html" ;"title=", [y, z">, [y, z + [y, [z, x">,_z.html" ;"title=", [y, z">, [y, z + [y, [z, x + [z, [x, y = 0 (Jacobi identity). Examples include the vector space of n-by-n matrices, with , y= x y - y x, the commutator of two matrices, and \mathbf^3, endowed with the
cross product In mathematics, the cross product or vector product (occasionally directed area product, to emphasize its geometric significance) is a binary operation on two vectors in a three-dimensional oriented Euclidean vector space (named here E), and ...
. The tensor algebra \operatorname(V) is a formal way of adding products to any vector space V to obtain an algebra. As a vector space, it is spanned by symbols, called simple
tensor In mathematics, a tensor is an algebraic object that describes a multilinear relationship between sets of algebraic objects associated with a vector space. Tensors may map between different objects such as vectors, scalars, and even other ...
s \mathbf_1 \otimes \mathbf_2 \otimes \cdots \otimes \mathbf_n, where the degree n varies. The multiplication is given by concatenating such symbols, imposing the distributive law under addition, and requiring that scalar multiplication commute with the tensor product ⊗, much the same way as with the tensor product of two vector spaces introduced in the above section on tensor products. In general, there are no relations between \mathbf_1 \otimes \mathbf_2 and \mathbf_2 \otimes \mathbf_1. Forcing two such elements to be equal leads to the symmetric algebra, whereas forcing \mathbf_1 \otimes \mathbf_2 = - \mathbf_2 \otimes \mathbf_1 yields the exterior algebra.


Related structures


Vector bundles

A ''vector bundle'' is a family of vector spaces parametrized continuously by a
topological space In mathematics, a topological space is, roughly speaking, a Geometry, geometrical space in which Closeness (mathematics), closeness is defined but cannot necessarily be measured by a numeric Distance (mathematics), distance. More specifically, a to ...
''X''. More precisely, a vector bundle over ''X'' is a topological space ''E'' equipped with a continuous map \pi : E \to X such that for every ''x'' in ''X'', the
fiber Fiber (spelled fibre in British English; from ) is a natural or artificial substance that is significantly longer than it is wide. Fibers are often used in the manufacture of other materials. The strongest engineering materials often inco ...
π−1(''x'') is a vector space. The case dim is called a
line bundle In mathematics, a line bundle expresses the concept of a line that varies from point to point of a space. For example, a curve in the plane having a tangent line at each point determines a varying line: the ''tangent bundle'' is a way of organis ...
. For any vector space ''V'', the projection makes the product into a "trivial" vector bundle. Vector bundles over ''X'' are required to be locally a product of ''X'' and some (fixed) vector space ''V'': for every ''x'' in ''X'', there is a
neighborhood A neighbourhood (Commonwealth English) or neighborhood (American English) is a geographically localized community within a larger town, city, suburb or rural area, sometimes consisting of a single street and the buildings lining it. Neigh ...
''U'' of ''x'' such that the restriction of π to π−1(''U'') is isomorphicThat is, there is a
homeomorphism In mathematics and more specifically in topology, a homeomorphism ( from Greek roots meaning "similar shape", named by Henri Poincaré), also called topological isomorphism, or bicontinuous function, is a bijective and continuous function ...
from π−1(''U'') to which restricts to linear isomorphisms between fibers.
to the trivial bundle . Despite their locally trivial character, vector bundles may (depending on the shape of the underlying space ''X'') be "twisted" in the large (that is, the bundle need not be (globally isomorphic to) the trivial bundle ). For example, the
Möbius strip In mathematics, a Möbius strip, Möbius band, or Möbius loop is a Surface (topology), surface that can be formed by attaching the ends of a strip of paper together with a half-twist. As a mathematical object, it was discovered by Johann Bened ...
can be seen as a line bundle over the circle ''S''1 (by identifying open intervals with the real line). It is, however, different from the cylinder , because the latter is orientable whereas the former is not. Properties of certain vector bundles provide information about the underlying topological space. For example, the
tangent bundle A tangent bundle is the collection of all of the tangent spaces for all points on a manifold, structured in a way that it forms a new manifold itself. Formally, in differential geometry, the tangent bundle of a differentiable manifold M is ...
consists of the collection of tangent spaces parametrized by the points of a differentiable manifold. The tangent bundle of the circle ''S''1 is globally isomorphic to , since there is a global nonzero vector field on ''S''1.A line bundle, such as the tangent bundle of ''S''1 is trivial if and only if there is a section that vanishes nowhere, see , Corollary 8.3. The sections of the tangent bundle are just vector fields. In contrast, by the
hairy ball theorem The hairy ball theorem of algebraic topology (sometimes called the hedgehog theorem in Europe) states that there is no nonvanishing continuous function, continuous tangent vector field on even-dimensional n‑sphere, ''n''-spheres. For the ord ...
, there is no (tangent) vector field on the
2-sphere A sphere (from Greek , ) is a surface analogous to the circle, a curve. In solid geometry, a sphere is the set of points that are all at the same distance from a given point in three-dimensional space.. That given point is the ''center' ...
''S''2 which is everywhere nonzero. K-theory studies the isomorphism classes of all vector bundles over some topological space. In addition to deepening topological and geometrical insight, it has purely algebraic consequences, such as the classification of finite-dimensional real division algebras: R, C, the
quaternion In mathematics, the quaternion number system extends the complex numbers. Quaternions were first described by the Irish mathematician William Rowan Hamilton in 1843 and applied to mechanics in three-dimensional space. The algebra of quater ...
s H and the octonions O. The cotangent bundle of a differentiable manifold consists, at every point of the manifold, of the dual of the tangent space, the cotangent space. Sections of that bundle are known as differential one-forms.


Modules

''Modules'' are to rings what vector spaces are to fields: the same axioms, applied to a ring ''R'' instead of a field ''F'', yield modules. The theory of modules, compared to that of vector spaces, is complicated by the presence of ring elements that do not have
multiplicative inverse In mathematics, a multiplicative inverse or reciprocal for a number ''x'', denoted by 1/''x'' or ''x''−1, is a number which when Multiplication, multiplied by ''x'' yields the multiplicative identity, 1. The multiplicative inverse of a ra ...
s. For example, modules need not have bases, as the Z-module (that is, abelian group) Z/2Z shows; those modules that do (including all vector spaces) are known as free modules. Nevertheless, a vector space can be compactly defined as a module over a ring which is a field, with the elements being called vectors. Some authors use the term ''vector space'' to mean modules over a division ring. The algebro-geometric interpretation of commutative rings via their
spectrum A spectrum (: spectra or spectrums) is a set of related ideas, objects, or properties whose features overlap such that they blend to form a continuum. The word ''spectrum'' was first used scientifically in optics to describe the rainbow of co ...
allows the development of concepts such as
locally free module In mathematics, particularly in algebra, the Class (set theory), class of projective modules enlarges the class of free modules (that is, module (mathematics), modules with basis vectors) over a ring (mathematics), ring, keeping some of the main pr ...
s, the algebraic counterpart to vector bundles.


Affine and projective spaces

Roughly, ''affine spaces'' are vector spaces whose origins are not specified. More precisely, an affine space is a set with a free transitive vector space action. In particular, a vector space is an affine space over itself, by the map V \times V \to W, \; (\mathbf, \mathbf) \mapsto \mathbf + \mathbf. If ''W'' is a vector space, then an affine subspace is a subset of ''W'' obtained by translating a linear subspace ''V'' by a fixed vector ; this space is denoted by (it is a coset of ''V'' in ''W'') and consists of all vectors of the form for An important example is the space of solutions of a system of inhomogeneous linear equations A \mathbf = \mathbf generalizing the homogeneous case discussed in the above section on linear equations, which can be found by setting \mathbf = \mathbf in this equation. The space of solutions is the affine subspace where x is a particular solution of the equation, and ''V'' is the space of solutions of the homogeneous equation (the nullspace of ''A''). The set of one-dimensional subspaces of a fixed finite-dimensional vector space ''V'' is known as ''projective space''; it may be used to formalize the idea of parallel lines intersecting at infinity. Grassmannians and flag manifolds generalize this by parametrizing linear subspaces of fixed dimension ''k'' and
flags A flag is a piece of fabric (most often rectangular) with distinctive colours and design. It is used as a symbol, a signalling device, or for decoration. The term ''flag'' is also used to refer to the graphic design employed, and flags have ...
of subspaces, respectively.


Notes


Citations


References


Algebra

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Analysis

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Historical references

* * * . * * * * , reprint: * * * * * * Peano, G. (1901)
Formulario mathematico ''Formulario Mathematico'' (Latino sine flexione: ''Formulary for Mathematics'') is a book by Giuseppe Peano which expresses fundamental theorems of mathematics in a Symbolic language (mathematics), symbolic language developed by Peano. The autho ...

vct axioms
via
Internet Archive The Internet Archive is an American 501(c)(3) organization, non-profit organization founded in 1996 by Brewster Kahle that runs a digital library website, archive.org. It provides free access to collections of digitized media including web ...


Further references

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External links

* {{DEFAULTSORT:Vector Space Concepts in physics Group theory Mathematical structures Vectors (mathematics and physics)