Norm (mathematics)

In mathematics, a norm is a function from a real or complex vector space to the nonnegative real numbers that behaves in certain ways like the distance from the origin: it commutes with scaling, obeys a form of the triangle inequality, and is zero only at the origin. In particular, the Euclidean distance of a vector from the origin is a norm, called the Euclidean norm, or 2-norm, which may also be defined as the square root of the inner product of a vector with itself.

A pseudonorm or seminorm satisfies the first two properties of a norm, but may be zero for other vectors than the origin.[1] A vector space with a specified norm is called a normed vector space. In a similar manner, a vector space with a seminorm is called a seminormed vector space.

Definition

Given a vector space over a subfield F of the complex numbers a norm on is a real-valued function with the following properties, where denotes the usual absolute value of a scalar :[2]

  1. Subadditivity/Triangle inequality: for all
  2. Absolute homogeneity: for all and all scalars
  3. Positive definiteness/Point-separating: for all if then
    • Because property (2) implies some authors replace property (3) with the equivalent condition: for every if and only if

A seminorm on is a function that has properties (1) and (2)[3] so that in particular, every norm is also a seminorm (and thus also a sublinear functional). However, there exist seminorms that are not norms. Properties (1) and (2) imply that if is a norm (or more generally, a seminorm) then and that also has the following property:

  1. Nonnegativity: for all

Some authors include non-negativity as part of the definition of "norm", although this is not necessary.

Equivalent norms

Suppose that p and q are two norms (or seminorms) on a vector space Then p and q are called equivalent, if there exist two real constants c and C with c > 0 such that for every vector

The norms p and q are equivalent if and only if they induce the same topology on [4] Any two norms on a finite-dimensional space are equivalent but this does not extend to infinite-dimensional spaces.[4]

Notation

If a norm is given on a vector space X, then the norm of a vector is usually denoted by enclosing it within double vertical lines: Such notation is also sometimes used if p is only a seminorm. For the length of a vector in Euclidean space (which is an example of a norm, as explained below), the notation with single vertical lines is also widespread.

In LaTeX and related markup languages, the double bar of norm notation is entered with the macro \|, which renders as . The double vertical line used to denote parallel lines, parallel operator and parallel addition is entered with \parallel and is rendered as Although looking similar, these two macros must not be confused as \| denotes a bracket and \parallel denotes an operator. Therefore, their size and the spaces around them are not computed in the same way. Similarly, the single vertical bar is coded as | when used as a bracket, and as \mid when used as an operator.

In Unicode, the representation of the "double vertical line" character is U+2016 DOUBLE VERTICAL LINE. The "double vertical line" symbol should not be confused with the "parallel to" symbol, U+2225 PARALLEL TO, which is intended to denote parallel lines and parallel operators. The double vertical line should also not be confused with U+01C1 ǁ LATIN LETTER LATERAL CLICK, aimed to denote lateral clicks in linguistics.

The single vertical line | has a Unicode representation U+007C | VERTICAL LINE.

Examples

Every (real or complex) vector space admits a norm: If is a Hamel basis for a vector space X then the real-valued map that sends x = iI sixiX (where all but finitely many of the scalars si are 0) to iI |si| is a norm on X.[5] There are also a large number of norms that exhibit additional properties that make them useful for specific problems.

Absolute-value norm

The absolute value

is a norm on the one-dimensional vector spaces formed by the real or complex numbers.[6]

Any norm p on a one-dimensional vector space X is equivalent (up to scaling) to the absolute value norm, meaning that there is a norm-preserving isomorphism of vector spaces where is either or and norm-preserving means that This isomorphism is given by sending to a vector of norm 1, which exists since such a vector is obtained by multiplying any nonzero vector by the inverse of its norm.

Euclidean norm

On the n-dimensional Euclidean space the intuitive notion of length of the vector x = (x1, x2, …, xn) is captured by the formula[7]

This is the Euclidean norm, which gives the ordinary distance from the origin to the point X—a consequence of the Pythagorean theorem. This operation may also be referred to as "SRSS", which is an acronym for the square root of the sum of squares.[8]

The Euclidean norm is by far the most commonly used norm on [7] but there are other norms on this vector space as will be shown below. However, all these norms are equivalent in the sense that they all define the same topology.

The inner product of two vectors of a Euclidean vector space is the dot product of their coordinate vectors over an orthonormal basis. Hence, the Euclidean norm can be written in a coordinate-free way as

The Euclidean norm is also called the L2 norm,[9] 2 norm, 2-norm, or square norm; see Lp space. It defines a distance function called the Euclidean length, L2 distance, or 2 distance.

The set of vectors in whose Euclidean norm is a given positive constant forms an n-sphere.

Euclidean norm of complex numbers

The Euclidean norm of a complex number is the absolute value (also called the modulus) of it, if the complex plane is identified with the Euclidean plane This identification of the complex number x + i y as a vector in the Euclidean plane, makes the quantity (as first suggested by Euler) the Euclidean norm associated with the complex number.

Quaternions and octonions

There are exactly four Euclidean Hurwitz algebras over the real numbers. These are the real numbers the complex numbers the quaternions and lastly the octonions where the dimensions of these spaces over the real numbers are 1, 2, 4, and 8, respectively. The canonical norms on and are their absolute value functions, as discussed previously.

The canonical norm on of quaternions is defined by

for every quaternion in This is the same as the Euclidean norm on considered as the vector space Similarly, the canonical norm on the octonions is just the Euclidean norm on

Finite-dimensional complex normed spaces

On an n-dimensional complex space the most common norm is

In this case, the norm can be expressed as the square root of the inner product of the vector and itself:

where is represented as a column vector ([x1; x2; …; xn]), and denotes its conjugate transpose.

This formula is valid for any inner product space, including Euclidean and complex spaces. For complex spaces, the inner product is equivalent to the complex dot product. Hence the formula in this case can also be written using the following notation:

Taxicab norm or Manhattan norm

The name relates to the distance a taxi has to drive in a rectangular street grid to get from the origin to the point x.

The set of vectors whose 1-norm is a given constant forms the surface of a cross polytope of dimension equivalent to that of the norm minus 1. The Taxicab norm is also called the norm. The distance derived from this norm is called the Manhattan distance or 1 distance.

The 1-norm is simply the sum of the absolute values of the columns.

In contrast,

is not a norm because it may yield negative results.

p-norm

Let p ≥ 1 be a real number. The p-norm (also called -norm) of vector is[7]

For p = 1, we get the taxicab norm,[6] for p = 2, we get the Euclidean norm, and as p approaches the p-norm approaches the infinity norm or maximum norm:

The p-norm is related to the generalized mean or power mean.

This definition is still of some interest for 0 < p < 1, but the resulting function does not define a norm,[10] because it violates the triangle inequality. What is true for this case of 0 < p < 1, even in the measurable analog, is that the corresponding Lp class is a vector space, and it is also true that the function

(without pth root) defines a distance that makes Lp(X) into a complete metric topological vector space. These spaces are of great interest in functional analysis, probability theory and harmonic analysis. However, aside from trivial cases, this topological vector space is not locally convex, and has no continuous non-zero linear forms. Thus the topological dual space contains only the zero functional.

The partial derivative of the p-norm is given by

The derivative with respect to x, therefore, is

where denotes Hadamard product and is used for absolute value of each component of the vector.

For the special case of p = 2, this becomes

or

Maximum norm (special case of: infinity norm, uniform norm, or supremum norm)

If is some vector such that then:

The set of vectors whose infinity norm is a given constant, c, forms the surface of a hypercube with edge length 2c.

Zero norm

In probability and functional analysis, the zero norm induces a complete metric topology for the space of measurable functions and for the F-space of sequences with F–norm [11] Here we mean by F-norm some real-valued function on an F-space with distance d, such that The F-norm described above is not a norm in the usual sense because it lacks the required homogeneity property.

Hamming distance of a vector from zero

In metric geometry, the discrete metric takes the value one for distinct points and zero otherwise. When applied coordinate-wise to the elements of a vector space, the discrete distance defines the Hamming distance, which is important in coding and information theory. In the field of real or complex numbers, the distance of the discrete metric from zero is not homogeneous in the non-zero point; indeed, the distance from zero remains one as its non-zero argument approaches zero. However, the discrete distance of a number from zero does satisfy the other properties of a norm, namely the triangle inequality and positive definiteness. When applied component-wise to vectors, the discrete distance from zero behaves like a non-homogeneous "norm", which counts the number of non-zero components in its vector argument; again, this non-homogeneous "norm" is discontinuous.

In signal processing and statistics, David Donoho referred to the zero "norm" with quotation marks. Following Donoho's notation, the zero "norm" of x is simply the number of non-zero coordinates of x, or the Hamming distance of the vector from zero. When this "norm" is localized to a bounded set, it is the limit of p-norms as p approaches 0. Of course, the zero "norm" is not truly a norm, because it is not positive homogeneous. Indeed, it is not even an F-norm in the sense described above, since it is discontinuous, jointly and severally, with respect to the scalar argument in scalar–vector multiplication and with respect to its vector argument. Abusing terminology, some engineers omit Donoho's quotation marks and inappropriately call the number-of-nonzeros function the L0 norm, echoing the notation for the Lebesgue space of measurable functions.

Infinite dimensions

The generalization of the above norms to an infinite number of components leads to p and Lp spaces, with norms

for complex-valued sequences and functions on respectively, which can be further generalized (see Haar measure).

Any inner product induces in a natural way the norm

Other examples of infinite-dimensional normed vector spaces can be found in the Banach space article.

Composite norms

Other norms on can be constructed by combining the above; for example

is a norm on

For any norm and any injective linear transformation A we can define a new norm of x, equal to

In 2D, with A a rotation by 45° and a suitable scaling, this changes the taxicab norm into the maximum norm. Each A applied to the taxicab norm, up to inversion and interchanging of axes, gives a different unit ball: a parallelogram of a particular shape, size, and orientation.

In 3D, this is similar but different for the 1-norm (octahedrons) and the maximum norm (prisms with parallelogram base).

There are examples of norms that are not defined by "entrywise" formulas. For instance, the Minkowski functional of a centrally-symmetric convex body in (centered at zero) defines a norm on (see § Classification of seminorms: absolutely convex absorbing sets below).

All the above formulas also yield norms on without modification.

There are also norms on spaces of matrices (with real or complex entries), the so-called matrix norms.

In abstract algebra

Let E be a finite extension of a field k of inseparable degree pμ, and let k have algebraic closure K. If the distinct embeddings of E are {σj}j, then the Galois-theoretic norm of an element αE is the value As that function is homogenous of degree [E:k], the Galois-theoretic norm is not a norm in the sense of this article. However, the [E:k]-th root of the norm (assuming that concept makes sense), is a norm.[12]

Composition algebras

The concept of norm in composition algebras does not share the usual properties of a norm as it may be negative or zero for z ≠ 0. A composition algebra (A, *, N) consists of an algebra over a field A, an involution *, and a quadratic form which is called the "norm".

The characteristic feature of composition algebras is the homomorphism property of N: for the product wz of two elements w and z of the composition algebra, its norm satisfies For and O the composition algebra norm is the square of the norm discussed above. In those cases the norm is a definite quadratic form. In other composition algebras the norm is an isotropic quadratic form.

Properties

For any norm on a vector space the reverse triangle inequality holds:

If is a continuous linear map between normed space, then the norm of and the norm of the transpose of are equal.[13]

For the Lp norms, we have Hölder's inequality[14]

A special case of this is the Cauchy–Schwarz inequality:[14]

Illustrations of unit circles in different norms.

Equivalence

The concept of unit circle (the set of all vectors of norm 1) is different in different norms: for the 1-norm, the unit circle is a square, for the 2-norm (Euclidean norm), it is the well-known unit circle, while for the infinity norm, it is a different square. For any p-norm, it is a superellipse with congruent axes (see the accompanying illustration). Due to the definition of the norm, the unit circle must be convex and centrally symmetric (therefore, for example, the unit ball may be a rectangle but cannot be a triangle, and for a p-norm).

In terms of the vector space, the seminorm defines a topology on the space, and this is a Hausdorff topology precisely when the seminorm can distinguish between distinct vectors, which is again equivalent to the seminorm being a norm. The topology thus defined (by either a norm or a seminorm) can be understood either in terms of sequences or open sets. A sequence of vectors is said to converge in norm to if as Equivalently, the topology consists of all sets that can be represented as a union of open balls. If is a normed space then[15]

Two norms and on a vector space are called equivalent if they induce the same topology,[4] which happens if and only if there exist positive real numbers C and D such that for all

For instance, if on then[16]

In particular,

That is,

If the vector space is a finite-dimensional real or complex one, all norms are equivalent. On the other hand, in the case of infinite-dimensional vector spaces, not all norms are equivalent.

Equivalent norms define the same notions of continuity and convergence and for many purposes do not need to be distinguished. To be more precise the uniform structure defined by equivalent norms on the vector space is uniformly isomorphic.

Classification of seminorms: absolutely convex absorbing sets

All seminorms on a vector space can be classified in terms of absolutely convex absorbing subsets A of To each such subset corresponds a seminorm pA called the gauge of A, defined as

where 'inf' is the infimum, with the property that

Conversely:

Any locally convex topological vector space has a local basis consisting of absolutely convex sets. A common method to construct such a basis is to use a family (p) of seminorms p that separates points: the collection of all finite intersections of sets {p < 1/n} turns the space into a locally convex topological vector space so that every p is continuous.

Such a method is used to design weak and weak* topologies.

norm case:

Suppose now that (p) contains a single p: since (p) is separating, p is a norm, and is its open unit ball. Then A is an absolutely convex bounded neighbourhood of 0, and is continuous.
The converse is due to Andrey Kolmogorov: any locally convex and locally bounded topological vector space is normable. Precisely:
If is an absolutely convex bounded neighbourhood of 0, the gauge (so that is a norm.

See also

  • Asymmetric norm  Generalization of the concept of a norm
  • F-seminorm
  • Gowers norm
  • Mahalanobis distance
  • Magnitude (mathematics)
  • Matrix norm  Norm on a vector space of matrices
  • Minkowski distance
  • Minkowski functional
  • Operator norm  Measure of the "size" of linear operators
  • Paranorm
  • Relation of norms and metrics
  • Seminorm
  • Sublinear function

References

  1. Knapp, A.W. (2005). Basic Real Analysis. Birkhäuser. p. . ISBN 978-0-817-63250-2.
  2. Pugh, C.C. (2015). Real Mathematical Analysis. Springer. p. page 28. ISBN 978-3-319-17770-0. Prugovečki, E. (1981). Quantum Mechanics in Hilbert Space. p. page 20.
  3. Rudin, W. (1991). Functional Analysis. p. 25.
  4. Conrad, Keith. "Equivalence of norms" (PDF). kconrad.math.uconn.edu. Retrieved September 7, 2020.
  5. Wilansky 2013, pp. 20-21.
  6. "Comprehensive List of Algebra Symbols". Math Vault. 2020-03-25. Retrieved 2020-08-24.
  7. Weisstein, Eric W. "Vector Norm". mathworld.wolfram.com. Retrieved 2020-08-24.
  8. Chopra, Anil (2012). Dynamics of Structures, 4th Ed. Prentice-Hall. ISBN 978-0-13-285803-8.
  9. Weisstein, Eric W. "Norm". mathworld.wolfram.com. Retrieved 2020-08-24.
  10. Except in where it coincides with the Euclidean norm, and where it is trivial.
  11. Rolewicz, Stefan (1987), Functional analysis and control theory: Linear systems, Mathematics and its Applications (East European Series), 29 (Translated from the Polish by Ewa Bednarczuk ed.), Dordrecht; Warsaw: D. Reidel Publishing Co.; PWN—Polish Scientific Publishers, pp. xvi, 524, doi:10.1007/978-94-015-7758-8, ISBN 90-277-2186-6, MR 0920371, OCLC 13064804
  12. Lang, Serge (2002) [1993]. Algebra (Revised 3rd ed.). New York: Springer Verlag. p. 284. ISBN 0-387-95385-X.
  13. Trèves 2006, pp. 242–243.
  14. Golub, Gene; Van Loan, Charles F. (1996). Matrix Computations (Third ed.). Baltimore: The Johns Hopkins University Press. p. 53. ISBN 0-8018-5413-X.
  15. Narici & Beckenstein 2011, pp. 107-113.
  16. "Relation between p-norms". Mathematics Stack Exchange.

Bibliography

  • Bourbaki, Nicolas (1987) [1981]. Sur certains espaces vectoriels topologiques [Topological Vector Spaces: Chapters 1–5]. Annales de l'Institut Fourier. Éléments de mathématique. 2. Translated by Eggleston, H.G.; Madan, S. Berlin New York: Springer-Verlag. ISBN 978-3-540-42338-6. OCLC 17499190.
  • Khaleelulla, S. M. (1982). Counterexamples in Topological Vector Spaces. Lecture Notes in Mathematics. 936. Berlin, Heidelberg, New York: Springer-Verlag. ISBN 978-3-540-11565-6. OCLC 8588370.
  • Narici, Lawrence; Beckenstein, Edward (2011). Topological Vector Spaces. Pure and applied mathematics (Second ed.). Boca Raton, FL: CRC Press. ISBN 978-1584888666. OCLC 144216834.
  • Schaefer, Helmut H.; Wolff, Manfred P. (1999). Topological Vector Spaces. GTM. 8 (Second ed.). New York, NY: Springer New York Imprint Springer. ISBN 978-1-4612-7155-0. OCLC 840278135.
  • Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322.
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