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Inner product space

Encyclopedia : I : IN : INN : Inner product space

For the scalar product or dot product of spatial vectors see dot product.
In mathematics, an inner product space is a vector space with additional structure, an inner product (also called a scalar product), which allows us to introduce geometrical notions such as angles and lengths of vectors. Inner product spaces generalize Euclidean spaces (with the dot product as the inner product) and are studied in functional analysis.

An inner product space is sometimes also called a pre-Hilbert space, since its completion with respect to the metric induced by its inner product is a Hilbert space.

Inner product spaces were referred to as unitary spaces in earlier work, although this terminology is now rarely used.

Contents

Definitions

In the following article, the field of scalars denoted F is either the field of real numbers R or the field of complex numbers C. See below.

Formally, an inner product space is a vector space V over the field F together with a positive-definite nondegenerate sesquilinear form, called an inner product. For real vector spaces, this is actually a positive-definite nondegenerate symmetric bilinear form. Thus the inner product is a map

[ \langle \cdot, \cdot \rangle : V \times V \rightarrow \mathbf ]
satisfying the following axioms:

:[\forall x,y\in V,\ \langle x,y\rangle =\overline.]
This condition implies that [ \langle x,x\rangle \in \mathbf ] for all [ x \in V ], because [\langle x,x\rangle = \overline ].
(Conjugation is also often written with an asterisk, as in [ \langle y,x\rangle^ ], as is the conjugate transpose.)
:[\forall b\in F,\ \forall x,y\in V,\ \langle x,by\rangle= b \langle x,y\rangle.]
:[\forall x,y,z\in V,\ \langle x,y+z\rangle= \langle x,y\rangle+ \langle x,z\rangle.]
By combining these with conjugate symmetry, we get:
:[\forall a\in F,\ \forall x,y\in V,\ \langle ax,y\rangle= \overline \langle x,y\rangle.]
:[\forall x,y,z\in V,\ \langle x+y,z\rangle= \langle x,z\rangle+ \langle y,z\rangle.]
:[\forall x \in V,\ \langle x,x\rangle \ge 0.]
(This makes sense because [ \langle x,x\rangle \in \mathbf ] for all [ x\in V ].)
The map from V to the dual space V* given by [x\mapsto \langle x,\cdot\rangle] is an isomorphism. For a finite-dimensional vector space, it suffices to check injectivity:
:[ \langle x,y\rangle = 0 \; \forall y \in V \mbox x = 0. ]
Hence, the inner product is a Hermitian form.
The property of an inner product space [ V ] that
:[ \langle x+y,z\rangle= \langle x,z\rangle+ \langle y,z\rangle ] and [ \langle x,y+z\rangle = \langle x,y\rangle + \langle x,z\rangle ]
for all [x, y, z \in V ] is known as additivity.

Note that if F=R, then the conjugate symmetry property is simply symmetry of the inner product, i.e.

: [ \langle x,y\rangle=\langle y,x\rangle.]
In this case, sesquilinearity becomes standard linearity.

Remark. Many mathematical authors require an inner product to be linear in the first argument and conjugate-linear in the second argument, contrary to the convention adopted above. This change is immaterial, but the definition above ensures a smoother connection to the bra-ket notation used by physicists in quantum mechanics and is now often used by mathematicians as well. Some authors adopt the convention that < , > is linear in the first component while < | > is linear in the second component, although this is by no means universal. For instance (Emch [1972]) does not follow this convention.

There are various technical reasons why it is necessary to restrict the basefield to R and C in the definition. Briefly, the basefield has to contain an ordered subfield (in order for non-negativity to make sense) and therefore has to have characteristic equal to 0. This immediately excludes finite fields. The basefield has to have additional structure, such as a distinguished automorphism.

In some cases we need to consider non-negative semi-definite sesquilinear forms. This means that <x, x> is only required to be non-negative. We show how to treat these below.

Examples

A trivial example are the real numbers with the standard multiplication as the inner product
[\langle x,y\rangle := xy]
More generally any Euclidean space Rn with the dot product is an inner product space
[\langle (x_1,\ldots, x_n),(y_1,\ldots, y_n)\rangle := \sum_^ x_i y_i = x_1 y_1 + \cdots + x_n y_n]
The general form of an inner product on Cn is given by:
[\langle \mathbf,\mathbf\rangle := \mathbf^*\mathbf\mathbf]
with M any positive-definite matrix, and x* the conjugate transpose of x. For the real case this corresponds to the dot product of the results of directionally differential scaling of the two vectors, with positive scale factors and orthogonal directions of scaling. Apart from an orthogonal transformation it is a weighted-sum version of the dot product, with positive weights.

The article on Hilbert space 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 which induces an incomplete metric occurs with the space C[a, b] of continuous complex valued functions on the interval [a,b]. The inner product is

[ \langle f , g \rangle := \int_a^b \overline g(t) \, dt ]
This space is not complete; consider for example, for the interval [0,1] the sequence of functions k where This sequence is a Cauchy sequence which does not converge to a continuous function.

Norms on inner product spaces

Inner product spaces have a naturally defined norm

[ \|x\| =\sqrt.]
This is well defined by the nonnegativity axiom of the definition of inner product space. The norm is thought of as the length of the vector x. Directly from the axioms, we can prove the following:

: [ |\langle x,y\rangle| \leq \|x\| \cdot \|y\| ]
with equality if and only if x and y are linearly dependent. This is one of the most important inequalities in mathematics. It is also known in the Russian mathematical literature as the Cauchy-Bunyakowski-Schwarz inequality.
Because of its importance, its short proof should be noted. To prove this inequality note it is trivial in the case y = 0. Thus we may assume <y, y> is nonzero. Thus we may let
:[ \lambda = \langle y , y \rangle^ \langle y, x\rangle]
and it follows that
:[ 0 \leq \langle x -\lambda y, x -\lambda y \rangle = \langle x, x\rangle - \langle y , y \rangle^ | \langle x,y\rangle|^2. ]
multiplying out, the result follows.
Geometric interpretation of inner product
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Geometric interpretation of inner product

The geometric interpretation of the inner product in terms of angle and length, motivates much of the geometric terminology we use in regard to these spaces. Indeed, an immediate consequence of the Cauchy-Schwarz inequality is that it justifies defining the angle between two non-zero vectors x and y (at least in the case F = R) by the identity
[\operatorname(x,y) = \arccos \frac.]
We assume the value of the angle is chosen to be in the interval (−π, +π]. This is in analogy to the familiar situation in two-dimensional Euclidean space. Correspondingly, we will say that non-zero vectors x, y of V are orthogonal if and only if their inner product is zero.
:[ \|r \cdot x\| = |r| \cdot \| x\|.]
The homogeneity property is completely trivial to prove.
:[ \|x + y\| \leq \|x \| + \|y\|. ]
The last two properties show the function defined is indeed a norm.
Because of the triangle inequality and because of axiom 2, we see that ||·|| is a norm which turns V into a normed vector space and hence also into a metric space. The most important inner product spaces are the ones which are complete with respect to this metric; they are called Hilbert spaces. Every inner product V space is a dense subspace of some Hilbert space. This Hilbert space is essentially uniquely determined by V and is constructed by completing V.
:[ \|x + y\|^2 + \|x - y\|^2 = 2\|x\|^2 + 2\|y\|^2. ]
:[ \|x\|^2 + \|y\|^2 = \|x+y\|^2. ]
The proofs of both of these identities require only expressing the definition of norm in terms of the inner product and multiplying out, using the property of additivity of each component. The name Pythagorean theorem arises from the geometric interpretation of this result as an analogue of the theorem in synthetic geometry. Note that the proof of the Pythagorean theorem in synthetic geometry is considerably more elaborate because of the paucity of underlying structure. In this sense, the synthetic Pythagorean theorem, if correctly demonstrated is deeper than the version given above.
An easy induction on the Pythagorean theorem yields:
:[ \sum_^n \|x_i\|^2 = \left\|\sum_^n x_i \right\|^2. ]
In view of the Cauchy-Schwarz inequality, we also note that <·,·> is continuous from V × V to F. This allows us to extend Pythagoras' theorem to infinitely many summands:
:[ \sum_^\infty\|x_i\|^2 = \left\|\sum_^\infty x_i\right\|^2, ]
provided the infinite series on the left is convergent. Completeness of the space is needed to ensure that the sequence of partial sums
:[ S_k = \sum_^k x_i ]
which is easily shown to be a Cauchy sequence is convergent.

Orthonormal sequences

A sequence k is orthonormal if and only if it is orthogonal and each ek has norm 1. An orthonormal basis for an inner product space V is an orthonormal sequence whose algebraic span is V.

The Gram-Schmidt process is a canonical procedure that takes a linearly independent sequence k on an inner product space and produces an orthonormal sequence k such that for each n

[\operatorname\ = \operatorname\ ]
By the Gram-Schmidt orthonormalization process, one shows:

Theorem. Any separable inner product space V has an orthonormal basis.

Parseval's identity leads immediately to the following theorem:

Theorem. Let V be a separable inner product space and k an orthonormal basis of V. Then the map

[ x \mapsto \_} ]
is an isometric linear map V → l2 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 polynomials. Note that the underlying index set can be taken to be any countable set (and in fact any set whatsoever, provided l2 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) = (2 \pi)^e^]
is an orthonormal basis of the space [C[-pi,pi]] with the L2 inner product. The mapping
[ f \mapsto \frac} \left\^\pi f(t) e^ \, dt \right\}_} ]
is an isometric linear map with dense image.

Orthogonality of the sequence k follows immediately from the fact that if k ≠ j, then

[ \int_^\pi e^ \, dt = 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 inner product norm, follows from the fact that the sequence has a dense algebraic span, this time in the space of continuous periodic functions on [[-pi,pi]] 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 from an inner product space V to an inner product space W are of relevance: 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.

Degenerate inner products

If V is a vector space and < , > a semi-definite sesquilinear form, then the function ||x|| = <x, x>1/2 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.) We can produce an inner product space by considering the quotient W = V/. The sesquilinear form < , > 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.

See also

References

 

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