In

s. They are sometimes called Lebesgue spaces, named after 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
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...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

Instatistics
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, realvector space#REDIRECT Vector space#REDIRECT Vector space
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...is usually given by the Euclidean norm: :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_2\; =\; \backslash left(\; ^2\; +\; ^2\; +\; \backslash dotsb\; +\; ^2\; \backslash right)^\; .$ 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 :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_p\; =\; \backslash left(\; ,\; x\_1,\; ^p\; +\; ,\; x\_2,\; ^p\; +\; \backslash dotsb\; +\; ,\; x\_n,\; ^p\; \backslash right)\; ^\; .$ 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: :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_\backslash infty\; =\; \backslash max\; \backslash left\backslash $ 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 aBanach 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: :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_2\; \backslash leq\; \backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_1\; .$ 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: :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_1\; \backslash leq\; \backslash sqrt\; \backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_2\; .$ 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 : :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_p\; \backslash leq\; \backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_r\; \backslash leq\; n^\; \backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_p\; .$ This is a consequence of Hölder's inequality.When

Image:Astroid.svg, Astroid, unit circle in metric In for , the formula :$\backslash ,\; x\backslash ,\; \_p\; =\; \backslash left(\; ,\; x\_1,\; ^p\; +\; ,\; x\_2,\; ^p\; +\; \backslash cdots\; +\; ,\; x\_n,\; ^p\; \backslash right)\; ^$ 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 :$,\; x\_1,\; ^p\; +\; ,\; x\_2,\; ^p\; +\; \backslash dotsb\; +\; ,\; x\_n,\; ^p$ 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 :$d\_p(x,y)\; =\; \backslash sum\_^n\; ,\; x\_i-y\_i,\; ^p$ 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 :$C\_p(n)\; =\; n^\; \backslash to\; \backslash infty,\; \backslash qquad\; \backslash text\; n\; \backslash to\; \backslash infty$ 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 :$(x\_n)\; \backslash mapsto\; \backslash sum\_n\; 2^\; \backslash frac,$ 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 :$,\; x\_1,\; ^0\; +\; ,\; x\_2,\; ^0\; +\; \backslash cdots\; +\; ,\; x\_n,\; ^0\; .$ 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, andstatistics
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: :$\backslash begin\; \&\; (x\_1,\; x\_2,\; \backslash ldots,\; x\_n,\; x\_,\backslash ldots)+(y\_1,\; y\_2,\; \backslash ldots,\; y\_n,\; y\_,\backslash ldots)\; \backslash \backslash \; =\; \&\; (x\_1+y\_1,\; x\_2+y\_2,\; \backslash ldots,\; x\_n+y\_n,\; x\_+y\_,\backslash ldots),\; \backslash \backslash [6pt]\; \&\; \backslash lambda\; \backslash cdot\; \backslash left\; (x\_1,\; x\_2,\; \backslash ldots,\; x\_n,\; x\_,\backslash ldots\; \backslash right)\; \backslash \backslash \; =\; \&\; (\backslash lambda\; x\_1,\; \backslash lambda\; x\_2,\; \backslash ldots,\; \backslash lambda\; x\_n,\; \backslash lambda\; x\_,\backslash ldots).\; \backslash end$ Define the -norm: :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_p\; =\; \backslash left(\; ,\; x\_1,\; ^p\; +\; ,\; x\_2,\; ^p\; +\; \backslash cdots\; +,\; x\_n,\; ^p\; +\; ,\; x\_,\; ^p\; +\; \backslash cdots\; \backslash right)\; ^$ 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 :$\backslash left(1,\; \backslash frac,\; \backslash ldots,\; \backslash frac,\; \backslash frac,\backslash ldots\backslash right)$ is not in , but it is in for , as the series :$1^p\; +\; \backslash frac\; +\; \backslash cdots\; +\; \backslash frac\; +\; \backslash frac+\backslash cdots,$ diverges for (the harmonic series (mathematics), harmonic series), but is convergent for . One also defines the -norm using the supremum: :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_\backslash infty\; =\; \backslash sup(,\; x\_1,\; ,\; ,\; x\_2,\; ,\; \backslash dotsc,\; ,\; x\_n,\; ,,\; x\_,\; ,\; \backslash ldots)$ and the corresponding space of all bounded sequences. It turns out that :$\backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_\backslash infty\; =\; \backslash lim\_\; \backslash left\backslash ,\; x\; \backslash right\backslash ,\; \_p$ 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 aBanach 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

''L^{p}'' spaces

vector space#REDIRECT Vector space#REDIRECT Vector space
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..., with the following natural operations: :$\backslash begin\; (f+g)(x)\; \&=\; f(x)+g(x),\; \backslash \backslash \; (\backslash lambda\; f)(x)\; \&=\; \backslash lambda\; f(x)\; \backslash end$ for every scalar . That the sum of two -th power integrable functions is again -th power integrable follows from the inequality :$\backslash ,\; f\; +\; g\backslash ,\; \_p^p\; \backslash leq\; 2^\; \backslash left\; (\backslash ,\; f\backslash ,\; \_p^p\; +\; \backslash ,\; g\backslash ,\; \_p^p\; \backslash right\; ).$ (This comes from the convexity of $t\backslash mapsto\; t^p$ for $p\backslash ge1$.) 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 $\backslash mathcal^p(S,\backslash ,\; \backslash mu)$. For , the space $\backslash mathcal^(S,\backslash mu)$ is the space of measurable functions bounded almost everywhere, with the essential supremum of its absolute value as a norm: :$\backslash ,\; f\backslash ,\; \_\backslash infty\; \backslash equiv\; \backslash inf\; \backslash .$ As in the discrete case, if there exists such that , then :$\backslash ,\; f\backslash ,\; \_\backslash infty\; =\; \backslash lim\_\backslash ,\; f\backslash ,\; \_p.$ $\backslash mathcal^p(S,\backslash ,\; \backslash mu)$ 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 , :$\backslash mathcal\; \backslash equiv\; \backslash \; =\; \backslash ker(\backslash ,\; \backslash cdot\backslash ,\; \_p)\; \backslash qquad\backslash forall\backslash \; 1\backslash leq\; p\; <\; \backslash infty$ In the quotient space, two functions and are identified if almost everywhere. The resulting normed vector space is, by definition, :$L^p(S,\; \backslash mu)\; \backslash equiv\; \backslash mathcal^p(S,\; \backslash mu)\; /\; \backslash mathcal$ In general, this process cannot be reversed: there is no consistent way to recover a coset of $\backslash mathcal$ 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

Special cases

Similar to the spaces, is the only Hilbert space among spaces. In the complex case, the inner product on is defined by :$\backslash langle\; f,\; g\; \backslash rangle\; =\; \backslash int\_S\; f(x)\; \backslash overline\; \backslash ,\; \backslash mathrm\backslash mu(x)$ 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\; \backslash mapsto\; \backslash kappa\_p(g)(f)=\backslash int\; f\; g\; \backslash ,\; \backslash mathrm\backslash mu\backslash \; \backslash $ for every $f\; \backslash in\; L^p(\backslash mu)$ 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 ofEmbeddings

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 :$\backslash \; \backslash ,\; \backslash mathbff^\backslash ,\; \_1\; \backslash le\; \backslash ,\; \backslash mathbf\backslash ,\; \_\; \backslash ,\; f^\backslash ,\; \_$ leading to :$\backslash \; \backslash ,\; f\backslash ,\; \_p\; \backslash le\; \backslash mu(S)^\; \backslash ,\; f\backslash ,\; \_q$. The constant appearing in the above inequality is optimal, in the sense that the operator norm of the identity is precisely :$\backslash ,\; I\backslash ,\; \_\; =\; \backslash mu(S)^$ 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 :$f\; =\; \backslash sum\_^n\; a\_j\; \backslash mathbf\_$ 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 :$F\; \backslash subset\; A\; \backslash subset\; U\; \backslash subset\; V\; \backslash quad\; \backslash text\; \backslash quad\; \backslash mu(U)\; -\; \backslash mu(F)\; =\; \backslash mu(U\; \backslash setminus\; F)\; <\; \backslash varepsilon$ It follows that there exists a continuous Urysohn's lemma#Formal statement , Urysohn function on that is on and on , with :$\backslash int\_S\; ,\; \backslash mathbf\_A\; -\; \backslash varphi,\; \backslash ,\; \backslash mathrm\backslash mu\; <\; \backslash varepsilon\; \backslash \; .$ 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: :$\backslash forall\; f\; \backslash in\; L^p\; (\backslash mathbf^d):\; \backslash qquad\; \backslash left\; \backslash ,\; \backslash tau\_t\; f\; -\; f\; \backslash right\; \backslash ,\; \_p\; \backslash to\; 0,\; \backslash quad\; \backslash text\; \backslash mathbf^d\; \backslash ni\; t\; \backslash to\; 0,$ where :$(\backslash tau\_t\; f)(x)\; =\; f(x\; -\; t).$Let be a measure space. If , then can be defined as above: it is the vector space of those measurable functions such that :$N\_p(f)\; =\; \backslash int\_S\; ,\; f,\; ^p\backslash ,\; d\backslash mu\; <\; \backslash infty.$ As before, we may introduce the -norm , but does not satisfy the triangle inequality in this case, and defines only a quasi-norm. The inequality , valid for implies that :$N\_p(f+g)\backslash le\; N\_p(f)\; +\; N\_p(g)$ and so the function :$d\_p(f,g)\; =\; N\_p(f-g)\; =\; \backslash ,\; f\; -\; g\backslash ,\; \_p^p$ is a metric on . The resulting metric space is complete space, complete; the verification is similar to the familiar case when . In this setting satisfies a ''reverse Minkowski inequality'', that is for in :$\backslash ,\; ,\; u,\; +,\; v,\; \backslash ,\; \_p\backslash geq\; \backslash ,\; u\backslash ,\; \_p+\backslash ,\; v\backslash ,\; \_p$ This result may be used to prove Clarkson's inequalities, which are in turn used to establish the uniformly convex space, uniform convexity of the spaces for . The space for is an F-space: it admits a complete translation-invariant metric with respect to which the vector space operations are continuous. It is also locally bounded, much like the case . It is the prototypical example of an F-space that, for most reasonable measure spaces, is not locally convex: in or , every open convex set containing the function is unbounded for the -quasi-norm; therefore, the vector does not possess a fundamental system of convex neighborhoods. Specifically, this is true if the measure space contains an infinite family of disjoint measurable sets of finite positive measure. The only nonempty convex open set in is the entire space . As a particular consequence, there are no nonzero linear functionals on : the dual space is the zero space. In the case of the counting measure on the natural numbers (producing the sequence space ), the bounded linear functionals on are exactly those that are bounded on , namely those given by sequences in . Although does contain non-trivial convex open sets, it fails to have enough of them to give a base for the topology. The situation of having no linear functionals is highly undesirable for the purposes of doing analysis. In the case of the Lebesgue measure on , rather than work with for , it is common to work with the Hardy space whenever possible, as this has quite a few linear functionals: enough to distinguish points from one another. However, the Hahn–Banach theorem still fails in for .

, 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 :$V\_\backslash varepsilon\; =\; \backslash Bigl\backslash ,\; \backslash qquad\; \backslash varepsilon\; >\; 0$ The topology can be defined by any metric of the form :$d(f,\; g)\; =\; \backslash int\_S\; \backslash varphi\; \backslash bigl(\; ,\; f(x)\; -\; g(x),\; \backslash bigr)\; \backslash ,\; \backslash mathrm\backslash mu(x)$ 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\_\backslash varepsilon\; =\; \backslash left\backslash $ 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 :$\backslash lambda\_f(t)\; =\; \backslash mu\backslash left\backslash $ If is in for some with , then by Markov's inequality, :$\backslash lambda\_f(t)\backslash le\; \backslash frac$ A function is said to be in the space weak , or , if there is a constant such that, for all , :$\backslash lambda\_f(t)\; \backslash le\; \backslash frac$ The best constant for this inequality is the -norm of , and is denoted by :$\backslash ,\; f\backslash ,\; \_\; =\; \backslash sup\_\; ~\; t\; \backslash lambda\_f^(t)\; .$ 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 , :$\backslash ,\; f\backslash ,\; \_\backslash le\; \backslash ,\; f\backslash ,\; \_p$ and in particular . In fact, one has :$\backslash ,\; f\; \backslash ,\; ^p\_\; =\; \backslash int\; ,\; f(x),\; ^p\; d\backslash mu(x)\; \backslash geq\; \backslash int\_\; t^p\; +\; \backslash int\_\; ,\; f,\; ^p\; \backslash geq\; t^p\; \backslash mu(\backslash )$, and raising to power and taking the supremum in one has :$\backslash ,\; f\backslash ,\; \_\; \backslash geq\; \backslash sup\_\; t\; \backslash ;\; \backslash mu(\backslash )^\; =\; \backslash ,\; f\backslash ,\; \_\; .$ Under the convention that two functions are equal if they are equal almost everywhere, then the spaces are complete . For any the expression :$,\; ,\; ,\; f\; ,\; ,\; ,\; \_=\backslash sup\_\; \backslash mu(E)^\; \backslash left(\backslash int\_E\; ,\; f,\; ^r\backslash ,d\backslash mu\backslash right)^$ is comparable to the -norm. Further in the case , this expression defines a norm if . Hence for the weak spaces areWeighted spaces

As before, consider a measure space . Let be a measurable function. The -weighted space is defined as , where means the measure defined by :$\backslash nu\; (A)\; \backslash equiv\; \backslash int\_A\; w(x)\; \backslash ,\; \backslash mathrm\; \backslash mu\; (x),\; \backslash qquad\; A\; \backslash in\; \backslash Sigma,$ or, in terms of the Radon–Nikodym theorem, Radon–Nikodym derivative, the norm (mathematics), norm for is explicitly :$\backslash ,\; u\; \backslash ,\; \_\; \backslash equiv\; \backslash left(\; \backslash int\_S\; w(x)\; ,\; u(x),\; ^p\; \backslash ,\; \backslash mathrm\; \backslash mu\; (x)\; \backslash right)^$ 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 $L^p\_\backslash left(X,\backslash Sigma,\backslash mu\backslash right)\backslash otimes\_\; E$ and $L^p\_\backslash left(X,\backslash Sigma,\backslash mu\backslash right)\backslash otimes\_\; E$; where $\backslash otimes\_$ and $\backslash otimes\_$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.See also

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

References

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*Proof that ''L''

{{DEFAULTSORT:Lp Space Normed spaces Banach spaces Mathematical series Function spaces Measure theory