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mathematics Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics ...
, an inner product space (or, rarely, a Hausdorff pre-Hilbert space) is a real vector space or a complex vector space with an
operation Operation or Operations may refer to: Arts, entertainment and media * ''Operation'' (game), a battery-operated board game that challenges dexterity * Operation (music), a term used in musical set theory * ''Operations'' (magazine), Multi-Man ...
called an inner product. The inner product of two vectors in the space is a scalar, often denoted with
angle brackets A bracket is either of two tall fore- or back-facing punctuation marks commonly used to isolate a segment of text or data from its surroundings. Typically deployed in symmetric pairs, an individual bracket may be identified as a 'left' or 'r ...
such as in \langle a, b \rangle. Inner products allow formal definitions of intuitive geometric notions, such as lengths,
angle In Euclidean geometry, an angle is the figure formed by two rays, called the '' sides'' of the angle, sharing a common endpoint, called the ''vertex'' of the angle. Angles formed by two rays lie in the plane that contains the rays. Angles ...
s, and orthogonality (zero inner product) of vectors. Inner product spaces generalize Euclidean vector spaces, in which the inner product is the dot product or ''scalar product'' of Cartesian coordinates. Inner product spaces of infinite
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 coord ...
are widely used in
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 (e.g. inner product, norm, topology, etc.) and the linear functions defi ...
. Inner product spaces over the field of
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 fo ...
s are sometimes referred to as unitary spaces. The first usage of the concept of a vector space with an inner product is due to Giuseppe Peano, in 1898. An inner product naturally induces an associated norm, (denoted , x, and , y, in the picture); so, every inner product space is a normed vector space. If this normed space is also
complete Complete may refer to: Logic * Completeness (logic) * Completeness of a theory, the property of a theory that every formula in the theory's language or its negation is provable Mathematics * The completeness of the real numbers, which implies t ...
(that is, a Banach space) then the inner product space is a Hilbert space. If an inner product space is not a Hilbert space, it can be ''extended'' by completion to a Hilbert space \overline. This means that H is a linear subspace of \overline, the inner product of H is the restriction of that of \overline, and H is dense in \overline for the
topology In mathematics, topology (from the Greek words , and ) is concerned with the properties of a geometric object that are preserved under continuous deformations, such as stretching, twisting, crumpling, and bending; that is, without closing ...
defined by the norm.


Definition

In this article, denotes a field that is either 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 distance, duration or temperature. Here, ''continuous'' means that values can have arbitrarily small variations. Every ...
s \R, or 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 fo ...
s \Complex. A scalar is thus an element of . A bar over an expression representing a scalar denotes the complex conjugate of this scalar. A zero vector is denoted \mathbf 0 for distinguishing it from the scalar . An ''inner product'' space is a vector space over the field together with an ''inner product'', that is a map : \langle \cdot, \cdot \rangle : V \times V \to F that satisfies the following three properties for all vectors x,y,z\in V and all scalars * ''Conjugate symmetry'': \langle x, y \rangle = \overline. As a = \overline if and only if is real, conjugate symmetry implies that \langle x, x \rangle is always a real number. If is \R, conjugate symmetry is just symmetry. * Linearity in the first argument:By combining the ''linear in the first argument'' property with the ''conjugate symmetry'' property you get ''conjugate-linear in the second argument'': \langle x,by \rangle = \langle x,y \rangle \overline . This is how the inner product was originally defined and is used in most mathematical contexts. A different convention has been adopted in theoretical physics and quantum mechanics, originating in the bra-ket notation of
Paul Dirac Paul Adrien Maurice Dirac (; 8 August 1902 – 20 October 1984) was an English theoretical physicist who is regarded as one of the most significant physicists of the 20th century. He was the Lucasian Professor of Mathematics at the Univer ...
, where the inner product is taken to be ''linear in the second argument'' and ''conjugate-linear in the first argument''; this convention is used in many other domains such as engineering and computer science.
\langle ax+by, z \rangle = a \langle x, z \rangle + b \langle y, z \rangle. * Positive-definiteness: if is not zero, then \langle x, x \rangle > 0 (conjugate symmetry implies that \langle x, x \rangle is real). If the positive-definiteness condition is replaced by merely requiring that \langle x, x \rangle \geq 0 for all , then one obtains the definition of ''positive semi-definite Hermitian form''. A positive semi-definite Hermitian form \langle \cdot, \cdot \rangle is an inner product if and only if for all ''x'', if \langle x, x \rangle = 0 then ''x = 0''.


Basic properties

In the following properties, which result almost immediately from the definition of an inner product, and are arbitrary vectors, and and are arbitrary scalars. *\langle \mathbf, x \rangle=\langle x,\mathbf\rangle=0. * \langle x, x \rangle is real and nonnegative. *\langle x, x \rangle = 0 if and only if x=\mathbf. *\langle x, ay+bz \rangle= \overline a \langle x, y \rangle + \overline b \langle x, z \rangle.
This implies that an inner product is a sesquilinear form. *\langle x + y, x + y \rangle = \langle x, x \rangle + 2\operatorname(\langle x, y \rangle) + \langle y, y \rangle, where \operatorname
denotes the real part of its argument. Over \R, conjugate-symmetry reduces to symmetry, and sesquilinearity reduces to bilinearity. Hence an inner product on a real vector space is a ''positive-definite symmetric bilinear form''. The binomial expansion of a square becomes : \langle x + y, x + y \rangle = \langle x, x \rangle + 2\langle x, y \rangle + \langle y, y \rangle .


Convention variant

Some authors, especially in
physics Physics is the natural science that studies matter, its 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 which ...
and
matrix algebra In abstract algebra, a matrix ring is a set of matrices with entries in a ring ''R'' that form a ring under matrix addition and matrix multiplication . The set of all matrices with entries in ''R'' is a matrix ring denoted M''n''(''R'')Lang, '' ...
, prefer to define inner products and sesquilinear forms with linearity in the second argument rather than the first. Then the first argument becomes conjugate linear, rather than the second.


Notation

Several notations are used for inner products, including \langle \cdot, \cdot \rangle , \left ( \cdot, \cdot \right ) , \langle \cdot , \cdot \rangle and \left ( \cdot , \cdot \right ) , as well as the usual dot product.


Some examples


Real and complex numbers

Among the simplest examples of inner product spaces are \R and \Complex. 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 distance, duration or temperature. Here, ''continuous'' means that values can have arbitrarily small variations. Every ...
s \R are a vector space over \R that becomes an inner product space with arithmetic multiplication as its inner product: \langle x, y \rangle := x y \quad \text x, y \in \R. 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 fo ...
s \Complex are a vector space over \Complex that becomes an inner product space with the inner product \langle x, y \rangle := x \overline \quad \text x, y \in \Complex. Unlike with the real numbers, the assignment (x, y) \mapsto x y does define a complex inner product on \Complex.


Euclidean vector space

More generally, the real n-space \R^n with the dot product is an inner product space, an example of a Euclidean vector space. \left\langle \begin x_1 \\ \vdots \\ x_n \end, \begin y_1 \\ \vdots \\ y_n \end \right\rangle = x^\textsf y = \sum_^n x_i y_i = x_1 y_1 + \cdots + x_n y_n, where x^ is the transpose of x. A function \langle \,\cdot, \cdot\, \rangle : \R^n \times \R^n \to \R is an inner product on \R^n if and only if there exists a symmetric positive-definite matrix \mathbf such that \langle x, y \rangle = x^ \mathbf y for all x, y \in \R^n. If \mathbf is the identity matrix then \langle x, y \rangle = x^ \mathbf y is the dot product. For another example, if n = 2 and \mathbf = \begin a & b \\ b & d \end is positive-definite (which happens if and only if \det \mathbf = a d - b^2 > 0 and one/both diagonal elements are positive) then for any x := \left _1, x_2\right, y := \left _1, y_2\right \in \R^2, \langle x, y \rangle := x^ \mathbf y = \left _1, x_2\right\begin a & b \\ b & d \end \begin y_1 \\ y_2 \end = a x_1 y_1 + b x_1 y_2 + b x_2 y_1 + d x_2 y_2. As mentioned earlier, every inner product on \R^2 is of this form (where b \in \R, a > 0 and d > 0 satisfy a d > b^2).


Complex coordinate space

The general form of an inner product on \Complex^n is known as the
Hermitian form In mathematics, a sesquilinear form is a generalization of a bilinear form that, in turn, is a generalization of the concept of the dot product of Euclidean space. A bilinear form is linear in each of its arguments, but a sesquilinear form allow ...
and is given by \langle x, y \rangle = y^\dagger \mathbf x = \overline, where M is any Hermitian positive-definite matrix and y^ is the conjugate transpose of y. For the real case, this corresponds to the dot product of the results of directionally-different scaling of the two vectors, with positive scale factors and orthogonal directions of scaling. It is a weighted-sum version of the dot product with positive weights—up to an orthogonal transformation.


Hilbert space

The article on Hilbert spaces has several examples of inner product spaces, wherein the metric induced by the inner product yields a complete metric space. An example of an inner product space which induces an incomplete metric is the space C( , b of continuous complex valued functions f and g on the interval , b The inner product is \langle f, g \rangle = \int_a^b f(t) \overline \, \mathrmt. This space is not complete; consider for example, for the interval the sequence of continuous "step" functions, \_k, defined by: f_k(t) = \begin 0 & t \in
1, 0 Onekama ( ) is a village in Manistee County in the U.S. state of Michigan. The population was 411 at the 2010 census. The village is located on the shores of Portage Lake and is surrounded by Onekama Township. The town's name is derived from "On ...
\\ 1 & t \in \left tfrac, 1\right\\ kt & t \in \left(0, \tfrac\right) \end This sequence is a Cauchy sequence for the norm induced by the preceding inner product, which does not converge to a function.


Random variables

For real random variables X and Y, the expected value of their product \langle X, Y \rangle = \mathbb Y/math> is an inner product. In this case, \langle X, X \rangle = 0 if and only if \mathbb = 0= 1 (that is, X = 0
almost surely In probability theory, an event is said to happen almost surely (sometimes abbreviated as a.s.) if it happens with probability 1 (or Lebesgue measure 1). In other words, the set of possible exceptions may be non-empty, but it has probability 0. ...
), where \mathbb denotes the
probability Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, ...
of the event. This definition of expectation as inner product can be extended to random vectors as well.


Complex matrices

The inner product for complex square matrices of the same size is the
Frobenius inner product In mathematics, the Frobenius inner product is a binary operation that takes two matrices and returns a scalar. It is often denoted \langle \mathbf,\mathbf \rangle_\mathrm. The operation is a component-wise inner product of two matrices as though ...
\langle A, B \rangle := \operatorname\left(AB^\right). Since trace and transposition are linear and the conjugation is on the second matrix, it is a sesquilinear operator. We further get Hermitian symmetry by, \langle A, B \rangle = \operatorname\left(AB^\right) = \overline = \overline Finally, since for A nonzero, \langle A, A\rangle = \sum_ \left, A_\^2 > 0 , we get that the Frobenius inner product is positive definite too, and so is an inner product.


Vector spaces with forms

On an inner product space, or more generally a vector space with a nondegenerate form (hence an isomorphism V \to V^*), vectors can be sent to covectors (in coordinates, via transpose), so that one can take the inner product and outer product of two vectors—not simply of a vector and a covector.


Basic results, terminology, and definitions


Norm properties

Every inner product space induces a norm, called its , that is defined by \, x\, = \sqrt. With this norm, every inner product space becomes a normed vector space. So, every general property of normed vector spaces applies to inner product spaces. In particular, one has the following properties:


Orthogonality


Real and complex parts of inner products

Suppose that \langle \cdot, \cdot \rangle is an inner product on V (so it is antilinear in its second argument). The polarization identity shows that the real part of the inner product is \operatorname \langle x, y \rangle = \frac \left(\, x + y\, ^2 - \, x - y\, ^2\right). If V is a real vector space then \langle x, y \rangle = \operatorname \langle x, y \rangle = \frac \left(\, x + y\, ^2 - \, x - y\, ^2\right) and the imaginary part (also called the ) of \langle \cdot, \cdot \rangle is always 0. Assume for the rest of this section that V is a complex vector space. The polarization identity for complex vector spaces shows that :\begin \langle x, \ y \rangle &= \frac \left(\, x + y\, ^2 - \, x - y\, ^2 + i\, x + iy\, ^2 - i\, x - iy\, ^2 \right) \\ &= \operatorname \langle x, y \rangle + i \operatorname \langle x, i y \rangle. \\ \end The map defined by \langle x \mid y \rangle = \langle y, x \rangle for all x, y \in V satisfies the axioms of the inner product except that it is antilinear in its , rather than its second, argument. The real part of both \langle x \mid y \rangle and \langle x, y \rangle are equal to \operatorname \langle x, y \rangle but the inner products differ in their complex part: :\begin \langle x \mid y \rangle &= \frac \left(\, x + y\, ^2 - \, x - y\, ^2 - i\, x + iy\, ^2 + i\, x - iy\, ^2 \right) \\ &= \operatorname \langle x, y \rangle - i \operatorname \langle x, i y \rangle. \\ \end The last equality is similar to the formula expressing a linear functional in terms of its real part. These formulas show that every complex inner product is completely determined by its real part. Moreover, this real part defines an inner product on V, considered as a real vector space. There is thus a one-to-one correspondence between complex inner products on a complex vector space V, and real inner products on V. For example, suppose that V = \Complex^n for some integer n > 0. When V is considered as a real vector space in the usual way (meaning that it is identified with the 2 n-dimensional real vector space \R^, with each \left(a_1 + i b_1, \ldots, a_n + i b_n\right) \in \Complex^n identified with \left(a_1, b_1, \ldots, a_n, b_n\right) \in \R^), then the dot product x \,\cdot\, y = \left(x_1, \ldots, x_\right) \, \cdot \, \left(y_1, \ldots, y_\right) := x_1 y_1 + \cdots + x_ y_ defines a real inner product on this space. The unique complex inner product \langle \,\cdot, \cdot\, \rangle on V = \C^n induced by the dot product is the map that sends c = \left(c_1, \ldots, c_n\right), d = \left(d_1, \ldots, d_n\right) \in \Complex^n to \langle c, d \rangle := c_1 \overline + \cdots + c_n \overline (because the real part of this map \langle \,\cdot, \cdot\, \rangle is equal to the dot product). Real vs. complex inner products Let V_ denote V considered as a vector space over the real numbers rather than complex numbers. The real part of the complex inner product \langle x, y \rangle is the map \langle x, y \rangle_ = \operatorname \langle x, y \rangle ~:~ V_ \times V_ \to \R, which necessarily forms a real inner product on the real vector space V_. Every inner product on a real vector space is a bilinear and
symmetric map In mathematics, symmetrization is a process that converts any function in n variables to a symmetric function in n variables. Similarly, antisymmetrization converts any function in n variables into an antisymmetric function. Two variables Let S ...
. For example, if V = \Complex with inner product \langle x, y \rangle = x \overline, where V is a vector space over the field \Complex, then V_ = \R^2 is a vector space over \R and \langle x, y \rangle_ is the dot product x \cdot y, where x = a + i b \in V = \Complex is identified with the point (a, b) \in V_ = \R^2 (and similarly for y); thus the standard inner product \langle x, y \rangle = x \overline, on \Complex is an "extension" the dot product . Also, had \langle x, y \rangle been instead defined to be the \langle x, y \rangle = x y (rather than the usual \langle x, y \rangle = x \overline) then its real part \langle x, y \rangle_ would be the dot product; furthermore, without the complex conjugate, if x \in \C but x \not\in \R then \langle x, x \rangle = x x = x^2 \not\in [0, \infty) so the assignment x \mapsto \sqrt would not define a norm. The next examples show that although real and complex inner products have many properties and results in common, they are not entirely interchangeable. For instance, if \langle x, y \rangle = 0 then \langle x, y \rangle_ = 0, but the next example shows that the converse is in general true. Given any x \in V, the vector i x (which is the vector x rotated by 90°) belongs to V and so also belongs to V_ (although scalar multiplication of x by i = \sqrt is not defined in V_, the vector in V denoted by i x is nevertheless still also an element of V_). For the complex inner product, \langle x, ix \rangle = -i \, x\, ^2, whereas for the real inner product the value is always \langle x, ix \rangle_ = 0. If \langle \,\cdot, \cdot\, \rangle is a complex inner product and A : V \to V is a continuous linear operator that satisfies \langle x, A x \rangle = 0 for all x \in V, then A = 0. This statement is no longer true if \langle \,\cdot, \cdot\, \rangle is instead a real inner product, as this next example shows. Suppose that V = \Complex has the inner product \langle x, y \rangle := x \overline mentioned above. Then the map A : V \to V defined by A x = ix is a linear map (linear for both V and V_) that denotes rotation by 90^ in the plane. Because x and A x perpendicular vectors and \langle x, Ax \rangle_ is just the dot product, \langle x, Ax \rangle_ = 0 for all vectors x; nevertheless, this rotation map A is certainly not identically 0. In contrast, using the complex inner product gives \langle x, Ax \rangle = -i \, x\, ^2, which (as expected) is not identically zero.


Orthonormal sequences

Let V be a finite dimensional inner product space of dimension n. Recall that every Basis (linear algebra), basis of V consists of exactly n linearly independent vectors. Using the Gram–Schmidt process we may start with an arbitrary basis and transform it into an orthonormal basis. That is, into a basis in which all the elements are orthogonal and have unit norm. In symbols, a basis \ is orthonormal if \langle e_i, e_j \rangle = 0 for every i \neq j and \langle e_i, e_i \rangle = \, e_a\, ^2 = 1 for each index i. This definition of orthonormal basis generalizes to the case of infinite-dimensional inner product spaces in the following way. Let V be any inner product space. Then a collection E = \left\_ is a for V if the subspace of V generated by finite linear combinations of elements of E is dense in V (in the norm induced by the inner product). Say that E is an for V if it is a basis and \left\langle e_, e_ \right\rangle = 0 if a \neq b and \langle e_a, e_a \rangle = \, e_a\, ^2 = 1 for all a, b \in A. Using an infinite-dimensional analog of the Gram-Schmidt process one may show: Theorem. Any separable inner product space has an orthonormal basis. Using the
Hausdorff maximal principle In mathematics, the Hausdorff maximal principle is an alternate and earlier formulation of Zorn's lemma proved by Felix Hausdorff in 1914 (Moore 1982:168). It states that in any partially ordered set, every totally ordered subset is contained in ...
and the fact that in a complete inner product space orthogonal projection onto linear subspaces is well-defined, one may also show that Theorem. Any complete inner product space has an orthonormal basis. The two previous theorems raise the question of whether all inner product spaces have an orthonormal basis. The answer, it turns out is negative. This is a non-trivial result, and is proved below. The following proof is taken from Halmos's ''A Hilbert Space Problem Book'' (see the references). : Parseval's identity leads immediately to the following theorem: Theorem. Let V be a separable inner product space and \left\_k an orthonormal basis of V. Then the map x \mapsto \bigl\_ is an isometric linear map V \mapsto \ell^2 with a dense image. This theorem can be regarded as an abstract form of Fourier series, in which an arbitrary orthonormal basis plays the role of the sequence of
trigonometric polynomial In the mathematical subfields of numerical analysis and mathematical analysis, a trigonometric polynomial is a finite linear combination of functions sin(''nx'') and cos(''nx'') with ''n'' taking on the values of one or more natural numbers. The c ...
s. Note that the underlying index set can be taken to be any countable set (and in fact any set whatsoever, provided \ell^2 is defined appropriately, as is explained in the article Hilbert space). In particular, we obtain the following result in the theory of Fourier series: Theorem. Let V be the inner product space C \pi, \pi Then the sequence (indexed on set of all integers) of continuous functions e_k(t) = \frac is an orthonormal basis of the space C \pi, \pi/math> with the L^2 inner product. The mapping f \mapsto \frac \left\_ is an isometric linear map with dense image. Orthogonality of the sequence \_k follows immediately from the fact that if k \neq j, then \int_^\pi e^ \, \mathrmt = 0. Normality of the sequence is by design, that is, the coefficients are so chosen so that the norm comes out to 1. Finally the fact that the sequence has a dense algebraic span, in the , follows from the fact that the sequence has a dense algebraic span, this time in the space of continuous periodic functions on \pi, \pi/math> with the uniform norm. This is the content of the Weierstrass theorem on the uniform density of trigonometric polynomials.


Operators on inner product spaces

Several types of linear maps A : V \to W between inner product spaces V and W are of relevance: * : A : V \to W is linear and continuous with respect to the metric defined above, or equivalently, A is linear and the set of non-negative reals \, where x ranges over the closed unit ball of V, is bounded. * : A : V \to W is linear and \langle Ax, y \rangle = \langle x, Ay \rangle for all x, y \in V. * : A : V \to W satisfies \, A x\, = \, x\, for all x \in V. A (resp. an ) is an isometry that is also a linear map (resp. an antilinear map). For inner product spaces, the polarization identity can be used to show that A is an isometry if and only if \langle Ax, Ay \rangle = \langle x, y \rangle for all x, y \in V. All isometries are injective. The
Mazur–Ulam theorem In mathematics, the Mazur–Ulam theorem states that if V and W are normed spaces over R and the mapping :f\colon V\to W is a surjective isometry, then f is affine. It was proved by Stanisław Mazur and Stanisław Ulam in response to a questio ...
establishes that every surjective isometry between two normed spaces is an affine transformation. Consequently, an isometry A between real inner product spaces is a linear map if and only if A(0) = 0. Isometries are morphisms between inner product spaces, and morphisms of real inner product spaces are orthogonal transformations (compare with orthogonal matrix). * : A : V \to W is an isometry which is surjective (and hence bijective). Isometrical isomorphisms are also known as unitary operators (compare with unitary matrix). From the point of view of inner product space theory, there is no need to distinguish between two spaces which are isometrically isomorphic. The spectral theorem provides a canonical form for symmetric, unitary and more generally normal operators on finite dimensional inner product spaces. A generalization of the spectral theorem holds for continuous normal operators in Hilbert spaces.


Generalizations

Any of the axioms of an inner product may be weakened, yielding generalized notions. The generalizations that are closest to inner products occur where bilinearity and conjugate symmetry are retained, but positive-definiteness is weakened.


Degenerate inner products

If V is a vector space and \langle \,\cdot\,, \,\cdot\, \rangle a semi-definite sesquilinear form, then the function: \, x\, = \sqrt makes sense and satisfies all the properties of norm except that \, x\, = 0 does not imply x = 0 (such a functional is then called a
semi-norm In mathematics, particularly in functional analysis, a seminorm is a vector space norm that need not be positive definite. Seminorms are intimately connected with convex sets: every seminorm is the Minkowski functional of some absorbing disk and ...
). We can produce an inner product space by considering the quotient W = V / \. The sesquilinear form \langle \,\cdot\,, \,\cdot\, \rangle factors through W. This construction is used in numerous contexts. The Gelfand–Naimark–Segal construction is a particularly important example of the use of this technique. Another example is the representation of semi-definite kernels on arbitrary sets.


Nondegenerate conjugate symmetric forms

Alternatively, one may require that the pairing be a nondegenerate form, meaning that for all non-zero x \neq 0 there exists some y such that \langle x, y \rangle \neq 0, though y need not equal x; in other words, the induced map to the dual space V \to V^* is injective. This generalization is important in differential geometry: a manifold whose tangent spaces have an inner product is a Riemannian manifold, while if this is related to nondegenerate conjugate symmetric form the manifold is a pseudo-Riemannian manifold. By Sylvester's law of inertia, just as every inner product is similar to the dot product with positive weights on a set of vectors, every nondegenerate conjugate symmetric form is similar to the dot product with weights on a set of vectors, and the number of positive and negative weights are called respectively the positive index and negative index. Product of vectors in Minkowski space is an example of indefinite inner product, although, technically speaking, it is not an inner product according to the standard definition above. Minkowski space has four dimensions and indices 3 and 1 (assignment of "+" and "−" to them differs depending on conventions). Purely algebraic statements (ones that do not use positivity) usually only rely on the nondegeneracy (the injective homomorphism V \to V^*) and thus hold more generally.


Related products

The term "inner product" is opposed to outer product, which is a slightly more general opposite. Simply, in coordinates, the inner product is the product of a 1 \times n with an n \times 1 vector, yielding a 1 \times 1 matrix (a scalar), while the outer product is the product of an m \times 1 vector with a 1 \times n covector, yielding an m \times n matrix. The outer product is defined for different dimensions, while the inner product requires the same dimension. If the dimensions are the same, then the inner product is the of the outer product (trace only being properly defined for square matrices). In an informal summary: "inner is horizontal times vertical and shrinks down, outer is vertical times horizontal and expands out". More abstractly, the outer product is the bilinear map W \times V^* \to \hom(V, W) sending a vector and a covector to a rank 1 linear transformation ( simple tensor of type (1, 1)), while the inner product is the bilinear evaluation map V^* \times V \to F given by evaluating a covector on a vector; the order of the domain vector spaces here reflects the covector/vector distinction. The inner product and outer product should not be confused with the interior product and exterior product, which are instead operations on vector fields and differential forms, or more generally on the exterior algebra. As a further complication, in geometric algebra the inner product and the (Grassmann) product are combined in the geometric product (the Clifford product in a Clifford algebra) – the inner product sends two vectors (1-vectors) to a scalar (a 0-vector), while the exterior product sends two vectors to a bivector (2-vector) – and in this context the exterior product is usually called the (alternatively, ). The inner product is more correctly called a product in this context, as the nondegenerate quadratic form in question need not be positive definite (need not be an inner product).


See also

* * * * * * * * *


Notes


References


Bibliography

* * * * * * * * * * * {{HilbertSpace Normed spaces Bilinear forms