TheInfoList

In
mathematics Mathematics (from Greek: ) includes the study of such topics as quantity (number theory), structure (algebra), space (geometry), and change (analysis). It has no generally accepted definition. Mathematicians seek and use patterns to formulate ...
, the spaces are
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 vector s ...
s defined using a natural generalization of the -norm for finite-dimensional
vector space#REDIRECT Vector space#REDIRECT Vector space {{Redirect category shell, 1= {{R for alternate capitalisation ...
{{Redirect category shell, 1= {{R for alternate capitalisation ...
s. They are sometimes called Lebesgue spaces, named after
Henri Lebesgue Henri Léon Lebesgue (; June 28, 1875 – July 26, 1941) was a French mathematician known for his theory of integration, which was a generalization of the 17th-century concept of integration—summing the area between an axis and the curve of a ...
, although according to the Bourbaki group they were first introduced by
Frigyes Riesz Frigyes Riesz ( hu, Riesz Frigyes, , sometimes spelled as Frederic; 22 January 1880 – 28 February 1956) was a HungarianEberhard Zeidler: Nonlinear Functional Analysis and Its Applications: Linear monotone operators. Springer, 199/ref> mathematician ...
. spaces form an important class of
Banach space In mathematics, more specifically in functional analysis, a Banach space (pronounced ) 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 ...
s in
functional analysis 200px, One of the possible modes of vibration of an idealized circular drum head. These modes are eigenfunctions of a linear operator on a function space, a common construction in functional analysis. Functional analysis is a branch of mathemat ...
, and 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 (an algebra ...
s. Because of their key role in the mathematical analysis of measure and probability spaces, Lebesgue spaces are used also in the theoretical discussion of problems in physics, statistics, finance, engineering, and other disciplines.

# Applications

## Statistics

In
statistics Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical ...
, measures of
central tendency In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution.Weisberg H.F (1992) ''Central Tendency and Variability'', Sage University Paper Series on Quantitative Applications in ...
and
statistical dispersion In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile ...
, such as the
mean There are several kinds of mean in mathematics, especially in statistics: For a data set, the arithmetic mean, also known as average or arithmetic average, is a central value of a finite set of numbers: specifically, the sum of the values divide ...
,
median In statistics and probability theory, the median is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. For a data set, it may be thought of as "the middle" value. The basic feat ...
, and
standard deviation In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a ...
, are defined in terms of metrics, and measures of central tendency can be characterized as Central tendency#Solutions to variational problems, solutions to variational problems. In penalized regression, "L1 penalty" and "L2 penalty" refer to penalizing either the Taxicab geometry, norm of a solution's vector of parameter values (i.e. the sum of its absolute values), or its norm (its Euclidean norm, Euclidean length). Techniques which use an L1 penalty, like LASSO, encourage solutions where many parameters are zero. Techniques which use an L2 penalty, like Tikhonov regularization, ridge regression, encourage solutions where most parameter values are small. Elastic net regularization uses a penalty term that is a combination of the norm and the norm of the parameter vector.

## Hausdorff–Young inequality

The Fourier transform for the real line (or, for periodic functions, see Fourier series), maps to (or to ) respectively, where and . This is a consequence of the Riesz–Thorin theorem, Riesz–Thorin interpolation theorem, and is made precise with the Hausdorff–Young inequality. By contrast, if , the Fourier transform does not map into .

## Hilbert spaces

Hilbert spaces are central to many applications, from quantum mechanics to stochastic calculus. The spaces and are both Hilbert spaces. In fact, by choosing a Hilbert basis (i.e., a maximal orthonormal subset of or any Hilbert space), one sees that all Hilbert spaces are isometric to , where is a set with an appropriate cardinality.

# The -norm in finite dimensions

s (see also superellipse) in based on different -norms (every vector from the origin to the unit circle has a length of one, the length being calculated with length-formula of the corresponding ). The length of a vector in the -dimensional real number, real
vector space#REDIRECT Vector space#REDIRECT Vector space {{Redirect category shell, 1= {{R for alternate capitalisation ...
{{Redirect category shell, 1= {{R for alternate capitalisation ...
is usually given by the Euclidean norm: :$\left\, x \right\, _2 = \left\left( ^2 + ^2 + \dotsb + ^2 \right\right)^ .$ The Euclidean distance between two points and is the length of the straight line between the two points. In many situations, the Euclidean distance is insufficient for capturing the actual distances in a given space. An analogy to this is suggested by taxi drivers in a grid street plan who should measure distance not in terms of the length of the straight line to their destination, but in terms of the taxicab geometry, rectilinear distance, which takes into account that streets are either orthogonal or parallel to each other. The class of -norms generalizes these two examples and has an abundance of applications in many parts of
mathematics Mathematics (from Greek: ) includes the study of such topics as quantity (number theory), structure (algebra), space (geometry), and change (analysis). It has no generally accepted definition. Mathematicians seek and use patterns to formulate ...
, physics, and computer science.

## Definition

For a real number , the -norm or -norm of is defined by :$\left\, x \right\, _p = \left\left( , x_1, ^p + , x_2, ^p + \dotsb + , x_n, ^p \right\right) ^ .$ The absolute value bars are unnecessary when is a rational number and, in reduced form, has an even numerator. The Euclidean norm from above falls into this class and is the -norm, and the -norm is the norm that corresponds to the taxicab geometry, rectilinear distance. The -norm or Chebyshev distance, maximum norm (or uniform norm) is the limit of the -norms for . It turns out that this limit is equivalent to the following definition: :$\left\, x \right\, _\infty = \max \left\$ See L-infinity, -infinity. For all , the -norms and maximum norm as defined above indeed satisfy the properties of a "length function" (or norm (mathematics), norm), which are that: *only the zero vector has zero length, *the length of the vector is positive homogeneous with respect to multiplication by a scalar (Euler's homogeneous function theorem, positive homogeneity), and *the length of the sum of two vectors is no larger than the sum of lengths of the vectors (triangle inequality). Abstractly speaking, this means that together with the -norm is a
Banach space In mathematics, more specifically in functional analysis, a Banach space (pronounced ) 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 ...
. This Banach space is the -space over .

### Relations between -norms

The grid distance or rectilinear distance (sometimes called the "Manhattan distance") between two points is never shorter than the length of the line segment between them (the Euclidean or "as the crow flies" distance). Formally, this means that the Euclidean norm of any vector is bounded by its 1-norm: :$\left\, x \right\, _2 \leq \left\, x \right\, _1 .$ This fact generalizes to -norms in that the -norm of any given vector does not grow with : : for any vector and real numbers and . (In fact this remains true for and .) For the opposite direction, the following relation between the -norm and the -norm is known: :$\left\, x \right\, _1 \leq \sqrt \left\, x \right\, _2 .$ This inequality depends on the dimension of the underlying vector space and follows directly from the Cauchy–Schwarz inequality. In general, for vectors in where : :$\left\, x \right\, _p \leq \left\, x \right\, _r \leq n^ \left\, x \right\, _p .$ This is a consequence of Hölder's inequality.

## When

Image:Astroid.svg, Astroid, unit circle in metric In for , the formula :$\, x\, _p = \left\left( , x_1, ^p + , x_2, ^p + \cdots + , x_n, ^p \right\right) ^$ defines an absolutely homogeneous function for ; however, the resulting function does not define a norm, because it is not subadditivity, subadditive. On the other hand, the formula :$, x_1, ^p + , x_2, ^p + \dotsb + , x_n, ^p$ defines a subadditive function at the cost of losing absolute homogeneity. It does define an F-space, F-norm, though, which is homogeneous of degree . Hence, the function :$d_p\left(x,y\right) = \sum_^n , x_i-y_i, ^p$ defines a metric space, metric. The metric space is denoted by . Although the -unit ball around the origin in this metric is "concave", the topology defined on by the metric is the usual vector space topology of , hence is a locally convex topological vector space. Beyond this qualitative statement, a quantitative way to measure the lack of convexity of is to denote by the smallest constant such that the multiple of the -unit ball contains the convex hull of , equal to . The fact that for fixed we have :$C_p\left(n\right) = n^ \to \infty, \qquad \text n \to \infty$ shows that the infinite-dimensional sequence space defined below, is no longer locally convex.

## When

There is one norm and another function called the "norm" (with quotation marks). The mathematical definition of the norm was established by Stefan Banach, Banach's ''Theory of Linear Operations''. The F-space, space of sequences has a complete metric topology provided by the F-space, F-norm :$\left(x_n\right) \mapsto \sum_n 2^ \frac,$ which is discussed by Stefan Rolewicz in ''Metric Linear Spaces''. The -normed space is studied in functional analysis, probability theory, and harmonic analysis. Another function was called the "norm" by David Donoho—whose quotation marks warn that this function is not a proper norm—is the number of non-zero entries of the vector . Many authors abuse of terminology, abuse terminology by omitting the quotation marks. Defining zero to the power of zero, , the zero "norm" of is equal to :$, x_1, ^0 + , x_2, ^0 + \cdots + , x_n, ^0 .$ This is not a norm (mathematics), norm because it is not Homogeneous function, homogeneous. For example, scaling the vector by a positive constant does not change the "norm". Despite these defects as a mathematical norm, the non-zero counting "norm" has uses in scientific computing, information theory, and
statistics Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical ...
–notably in compressed sensing in signal processing and computational harmonic analysis. The associated defective "metric" is known as Hamming distance.

# The -norm in infinite dimensions and spaces

## The sequence space

The -norm can be extended to vectors that have an infinite number of components (sequences), which yields the space . This contains as special cases: *, the space of sequences whose series is absolute convergence, absolutely convergent, *, the space of square-summable sequences, which is a Hilbert space, and *, the space of bounded sequences. The space of sequences has a natural vector space structure by applying addition and scalar multiplication coordinate by coordinate. Explicitly, the vector sum and the scalar action for infinite sequences of real (or complex number, complex) numbers are given by: :$\begin & \left(x_1, x_2, \ldots, x_n, x_,\ldots\right)+\left(y_1, y_2, \ldots, y_n, y_,\ldots\right) \\ = & \left(x_1+y_1, x_2+y_2, \ldots, x_n+y_n, x_+y_,\ldots\right), \\\left[6pt\right] & \lambda \cdot \left \left(x_1, x_2, \ldots, x_n, x_,\ldots \right\right) \\ = & \left(\lambda x_1, \lambda x_2, \ldots, \lambda x_n, \lambda x_,\ldots\right). \end$ Define the -norm: :$\left\, x \right\, _p = \left\left( , x_1, ^p + , x_2, ^p + \cdots +, x_n, ^p + , x_, ^p + \cdots \right\right) ^$ Here, a complication arises, namely that the series (mathematics), series on the right is not always convergent, so for example, the sequence made up of only ones, , will have an infinite -norm for . The space is then defined as the set of all infinite sequences of real (or complex) numbers such that the -norm is finite. One can check that as increases, the set grows larger. For example, the sequence :$\left\left(1, \frac, \ldots, \frac, \frac,\ldots\right\right)$ is not in , but it is in for , as the series :$1^p + \frac + \cdots + \frac + \frac+\cdots,$ diverges for (the harmonic series (mathematics), harmonic series), but is convergent for . One also defines the -norm using the supremum: :$\left\, x \right\, _\infty = \sup\left(, x_1, , , x_2, , \dotsc, , x_n, ,, x_, , \ldots\right)$ and the corresponding space of all bounded sequences. It turns out that :$\left\, x \right\, _\infty = \lim_ \left\, x \right\, _p$ if the right-hand side is finite, or the left-hand side is infinite. Thus, we will consider spaces for . The -norm thus defined on is indeed a norm, and together with this norm is a
Banach space In mathematics, more specifically in functional analysis, a Banach space (pronounced ) 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 ...
. The fully general space is obtained—as seen below—by considering vectors, not only with finitely or countably-infinitely many components, but with "''arbitrarily many components''"; in other words, function (mathematics), functions. An integral instead of a sum is used to define the -norm.

## General ℓ''p''-space

In complete analogy to the preceding definition one can define the space $\ell^p\left(I\right)$ over a general index set $I$ (and $1\leq p < \infty$) as :$\ell^p\left(I\right)=\left\\,$, where convergence on the right means that only countably many summands are nonzero (see also Unconditional convergence). With the norm :$\left\, x \right\, _p = \left\left( \sum_ , x_i, ^p \right\right) ^$ the space $\ell^p\left(I\right)$ becomes a Banach space. In the case where $I$ is finite with $n$ elements, this construction yields with the $p$-norm defined above. If $I$ is countably infinite, this is exactly the sequence space $\ell^p$ defined above. For uncountable sets $I$ this is a non-Separable space, separable Banach space which can be seen as the Locally convex topological vector space, locally convex direct limit of $\ell^p$-sequence spaces. The index set $I$ can be turned into a measure space by giving it the Σ-algebra#Simple set-based examples, discrete σ-algebra and the counting measure. Then the space $\ell^p\left(I\right)$ is just a special case of the more general $L^p$-space (see below).

# ''Lp'' spaces

An space may be defined as a space of measurable functions for which the $p$-th power of the absolute value is Lebesgue integrable, where functions which agree almost everywhere are identified. More generally, let and be a measure space. Consider the set of all measurable functions from to or whose absolute value raised to the -th power has a finite integral, or equivalently, that :$\, f\, _p \equiv \left\left( \int_S , f, ^p\;\mathrm\mu \right\right)^<\infty$ The set of such functions forms a
vector space#REDIRECT Vector space#REDIRECT Vector space {{Redirect category shell, 1= {{R for alternate capitalisation ...
{{Redirect category shell, 1= {{R for alternate capitalisation ...
, with the following natural operations: :$\begin \left(f+g\right)\left(x\right) &= f\left(x\right)+g\left(x\right), \\ \left(\lambda f\right)\left(x\right) &= \lambda f\left(x\right) \end$ for every scalar . That the sum of two -th power integrable functions is again -th power integrable follows from the inequality :$\, f + g\, _p^p \leq 2^ \left \left(\, f\, _p^p + \, g\, _p^p \right \right).$ (This comes from the convexity of $t\mapsto t^p$ for $p\ge1$.) In fact, more is true. ''Minkowski inequality, Minkowski's inequality'' says the triangle inequality holds for . Thus the set of -th power integrable functions, together with the function , is a seminormed vector space, which is denoted by $\mathcal^p\left(S,\, \mu\right)$. For , the space $\mathcal^\left(S,\mu\right)$ is the space of measurable functions bounded almost everywhere, with the essential supremum of its absolute value as a norm: :$\, f\, _\infty \equiv \inf \.$ As in the discrete case, if there exists such that , then :$\, f\, _\infty = \lim_\, f\, _p.$ $\mathcal^p\left(S,\, \mu\right)$ can be made into a normed vector space in a standard way; one simply takes the quotient space (topology), quotient space with respect to the kernel (set theory), kernel of . Since for any measurable function , we have that if and only if almost everywhere, the kernel of does not depend upon , :$\mathcal \equiv \ = \ker\left(\, \cdot\, _p\right) \qquad\forall\ 1\leq p < \infty$ In the quotient space, two functions and are identified if almost everywhere. The resulting normed vector space is, by definition, :$L^p\left(S, \mu\right) \equiv \mathcal^p\left(S, \mu\right) / \mathcal$ In general, this process cannot be reversed: there is no consistent way to recover a coset of $\mathcal$ from $L^p$. For $L^$, however, there is a Lifting theory, theory of lifts enabling such recovery. When the underlying measure space is understood, is often abbreviated , or just . For is a
Banach space In mathematics, more specifically in functional analysis, a Banach space (pronounced ) 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 ...
. The fact that is complete is often referred to as the Riesz-Fischer theorem, and can be proven using the convergence theorems for Lebesgue integrals. The above definitions generalize to Bochner spaces.

## Special cases

Similar to the spaces, is the only Hilbert space among spaces. In the complex case, the inner product on is defined by :$\langle f, g \rangle = \int_S f\left(x\right) \overline \, \mathrm\mu\left(x\right)$ The additional inner product structure allows for a richer theory, with applications to, for instance, Fourier series and quantum mechanics. Functions in are sometimes called quadratically integrable functions, square-integrable functions or square-summable functions, but sometimes these terms are reserved for functions that are square-integrable in some other sense, such as in the sense of a Riemann integral . If we use complex-valued functions, the space is a commutative C*-algebra with pointwise multiplication and conjugation. For many measure spaces, including all sigma-finite ones, it is in fact a commutative von Neumann algebra. An element of defines a bounded operator on any space by multiplication operator, multiplication. For the spaces are a special case of spaces, when , and is the counting measure on . More generally, if one considers any set with the counting measure, the resulting space is denoted . For example, the space is the space of all sequences indexed by the integers, and when defining the -norm on such a space, one sums over all the integers. The space , where is the set with elements, is with its -norm as defined above. As any Hilbert space, every space is linearly isometric to a suitable , where the cardinality of the set is the cardinality of an arbitrary Hilbertian basis for this particular .

# Properties of ''L''''p'' spaces

## Dual spaces

The Continuous dual, dual space (the Banach space of all continuous linear functionals) of for has a natural isomorphism with , where is such that (i.e. ). This isomorphism associates with the functional defined by :$f \mapsto \kappa_p\left(g\right)\left(f\right)=\int f g \, \mathrm\mu\ \$ for every $f \in L^p\left(\mu\right)$ The fact that is well defined and continuous follows from Hölder's inequality. is a linear mapping which is an isometry by the Hölder's inequality#Extremal equality, extremal case of Hölder's inequality. It is also possible to show (for example with the Radon–Nikodym theorem, see) that any can be expressed this way: i.e., that is ''onto''. Since is onto and isometric, it is an isomorphism of
Banach space In mathematics, more specifically in functional analysis, a Banach space (pronounced ) 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 ...
s. With this (isometric) isomorphism in mind, it is usual to say simply that is the dual Banach space of . For , the space is reflexive space, reflexive. Let be as above and let be the corresponding linear isometry. Consider the map from to , obtained by composing with the dual space#Transpose of a continuous linear map, transpose (or adjoint) of the inverse of : :$j_p : L^p\left(\mu\right) \overset L^q\left(\mu\right)^* \overset L^p\left(\mu\right)^$ This map coincides with the Reflexive space#Definitions, canonical embedding of into its bidual. Moreover, the map is onto, as composition of two onto isometries, and this proves reflexivity. If the measure on is sigma-finite, then the dual of is isometrically isomorphic to (more precisely, the map corresponding to is an isometry from onto ). The dual of is subtler. Elements of can be identified with bounded signed ''finitely'' additive measures on that are absolutely continuous with respect to . See ba space for more details. If we assume the axiom of choice, this space is much bigger than except in some trivial cases. However, Saharon Shelah proved that there are relatively consistent extensions of Zermelo–Fraenkel set theory (ZF + Axiom of dependent choice, DC + "Every subset of the real numbers has the Baire property") in which the dual of is . See Sections 14.77 and 27.44–47

## Embeddings

Colloquially, if , then contains functions that are more locally singular, while elements of can be more spread out. Consider the Lebesgue measure on the half line . A continuous function in might blow up near but must decay sufficiently fast toward infinity. On the other hand, continuous functions in need not decay at all but no blow-up is allowed. The precise technical result is the following. Suppose that . Then: # iff does not contain sets of finite but arbitrarily large measure, and # iff does not contain sets of non-zero but arbitrarily small measure. Neither condition holds for the real line with the Lebesgue measure. In both cases the embedding is continuous, in that the identity operator is a bounded linear map from to in the first case, and to in the second. (This is a consequence of the closed graph theorem and properties of spaces.) Indeed, if the domain has finite measure, one can make the following explicit calculation using Hölder's inequality :$\ \, \mathbff^\, _1 \le \, \mathbf\, _ \, f^\, _$ leading to :$\ \, f\, _p \le \mu\left(S\right)^ \, f\, _q$. The constant appearing in the above inequality is optimal, in the sense that the operator norm of the identity is precisely :$\, I\, _ = \mu\left(S\right)^$ the case of equality being achieved exactly when -a.e.

## Dense subspaces

Throughout this section we assume that: . Let be a measure space. An ''integrable simple function'' on is one of the form :$f = \sum_^n a_j \mathbf_$ where is scalar, has finite measure and $_$ is the indicator function of the set $A_j$, for . By construction of the Lebesgue integration, integral, the vector space of integrable simple functions is dense in . More can be said when is a Normal space, normal topological space and its Borel algebra, Borel –algebra, i.e., the smallest –algebra of subsets of containing the open sets. Suppose is an open set with . It can be proved that for every Borel set contained in , and for every , there exist a closed set and an open set such that :$F \subset A \subset U \subset V \quad \text \quad \mu\left(U\right) - \mu\left(F\right) = \mu\left(U \setminus F\right) < \varepsilon$ It follows that there exists a continuous Urysohn's lemma#Formal statement , Urysohn function on that is on and on , with :$\int_S , \mathbf_A - \varphi, \, \mathrm\mu < \varepsilon \ .$ If can be covered by an increasing sequence of open sets that have finite measure, then the space of –integrable continuous functions is dense in . More precisely, one can use bounded continuous functions that vanish outside one of the open sets . This applies in particular when and when is the Lebesgue measure. The space of continuous and compactly supported functions is dense in . Similarly, the space of integrable ''step functions'' is dense in ; this space is the linear span of indicator functions of bounded intervals when , of bounded rectangles when and more generally of products of bounded intervals. Several properties of general functions in are first proved for continuous and compactly supported functions (sometimes for step functions), then extended by density to all functions. For example, it is proved this way that translations are continuous on , in the following sense: :$\forall f \in L^p \left(\mathbf^d\right): \qquad \left \, \tau_t f - f \right \, _p \to 0, \quad \text \mathbf^d \ni t \to 0,$ where :$\left(\tau_t f\right)\left(x\right) = f\left(x - t\right).$

## , the space of measurable functions

The vector space of (equivalence classes of) measurable functions on is denoted . By definition, it contains all the , and is equipped with the topology of ''convergence in measure''. When is a probability measure (i.e., ), this mode of convergence is named ''convergence in probability''. The description is easier when is finite. If is a finite measure on , the function admits for the convergence in measure the following fundamental system of neighborhoods :$V_\varepsilon = \Bigl\, \qquad \varepsilon > 0$ The topology can be defined by any metric of the form :$d\left(f, g\right) = \int_S \varphi \bigl\left( , f\left(x\right) - g\left(x\right), \bigr\right) \, \mathrm\mu\left(x\right)$ where is bounded continuous concave and non-decreasing on , with and when (for example, . Such a metric is called Paul Lévy (mathematician), Lévy-metric for . Under this metric the space is complete (it is again an F-space). The space is in general not locally bounded, and not locally convex. For the infinite Lebesgue measure on , the definition of the fundamental system of neighborhoods could be modified as follows :$W_\varepsilon = \left\$ The resulting space coincides as topological vector space with , for any positive –integrable density .

# Generalizations and extensions

## Weak

Let be a measure space, and a measurable function with real or complex values on . The cumulative distribution function, distribution function of is defined for by :$\lambda_f\left(t\right) = \mu\left\$ If is in for some with , then by Markov's inequality, :$\lambda_f\left(t\right)\le \frac$ A function is said to be in the space weak , or , if there is a constant such that, for all , :$\lambda_f\left(t\right) \le \frac$ The best constant for this inequality is the -norm of , and is denoted by :$\, f\, _ = \sup_ ~ t \lambda_f^\left(t\right) .$ The weak coincide with the Lorentz spaces , so this notation is also used to denote them. The -norm is not a true norm, since the triangle inequality fails to hold. Nevertheless, for in , :$\, f\, _\le \, f\, _p$ and in particular . In fact, one has :$\, f \, ^p_ = \int , f\left(x\right), ^p d\mu\left(x\right) \geq \int_ t^p + \int_ , f, ^p \geq t^p \mu\left(\\right)$, and raising to power and taking the supremum in one has :$\, f\, _ \geq \sup_ t \; \mu\left(\\right)^ = \, f\, _ .$ Under the convention that two functions are equal if they are equal almost everywhere, then the spaces are complete . For any the expression :$, , , f , , , _=\sup_ \mu\left(E\right)^ \left\left(\int_E , f, ^r\,d\mu\right\right)^$ is comparable to the -norm. Further in the case , this expression defines a norm if . Hence for the weak spaces are
Banach space In mathematics, more specifically in functional analysis, a Banach space (pronounced ) 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 ...
s . A major result that uses the -spaces is the Marcinkiewicz interpolation, Marcinkiewicz interpolation theorem, which has broad applications to harmonic analysis and the study of singular integrals.

## Weighted spaces

As before, consider a measure space . Let be a measurable function. The -weighted space is defined as , where means the measure defined by :$\nu \left(A\right) \equiv \int_A w\left(x\right) \, \mathrm \mu \left(x\right), \qquad A \in \Sigma,$ or, in terms of the Radon–Nikodym theorem, Radon–Nikodym derivative, the norm (mathematics), norm for is explicitly :$\, u \, _ \equiv \left\left( \int_S w\left(x\right) , u\left(x\right), ^p \, \mathrm \mu \left(x\right) \right\right)^$ As -spaces, the weighted spaces have nothing special, since is equal to . But they are the natural framework for several results in harmonic analysis ; they appear for example in the Muckenhoupt weights, Muckenhoupt theorem: for , the classical Hilbert transform is defined on where denotes the unit circle and the Lebesgue measure; the (nonlinear) Hardy–Littlewood maximal operator is bounded on . Muckenhoupt's theorem describes weights such that the Hilbert transform remains bounded on and the maximal operator on .

## spaces on manifolds

One may also define spaces on a manifold, called the intrinsic spaces of the manifold, using Density on a manifold, densities.

## Vector-valued spaces

Given a measure space and a locally-convex space , one may also define a spaces of -integrable E-valued functions in a number of ways. The most common of these being the spaces of Bochner integral, Bochner integrable and Pettis integral, Pettis-integrable functions. Using the Topological tensor product, tensor product of locally convex spaces, these may be respectively defined as $L^p_\left\left(X,\Sigma,\mu\right\right)\otimes_ E$ and $L^p_\left\left(X,\Sigma,\mu\right\right)\otimes_ E$; where $\otimes_$ and $\otimes_$respectively denote the projective and injective tensor products of locally convex spaces. When is a nuclear space, Alexander Grothendieck, Grothendieck showed that these two constructions are indistinguishable.

*Birnbaum–Orlicz space *Hardy space *Riesz–Thorin theorem *Hölder mean *Hölder space *Root mean square *Locally integrable function $\left\left(\scriptstyle L^1_\right\right)$ *Pontryagin duality#Haar measure, $\scriptstyle L^p\left(G\right)$ spaces over a locally compact group $G$ *Minkowski distance *L-infinity *Lp sum

# References

* . * . * . * . * * . * . * * * * *