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# Lp space

## From Wikipedia, the free encyclopedia

In mathematics, the Lp spaces are function spaces defined using a natural generalization of the p-norm for finite-dimensional vector spaces. They are sometimes called Lebesgue spaces, named after Henri Lebesgue (Dunford & Schwartz 1958, III.3), although according to the Bourbaki group (Bourbaki 1987) they were first introduced by Frigyes Riesz (Riesz 1910).

Lp spaces form an important class of Banach spaces in functional analysis, and of topological vector spaces. 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, economics, finance, engineering, and other disciplines.

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• 7.2 - L-p Spaces - Part 1
• The Lp Norm for Vectors and Functions

## Applications

### Statistics

In statistics, measures of central tendency and statistical dispersion, such as the mean, median, and standard deviation, are defined in terms of ${\displaystyle L^{p}}$ metrics, and measures of central tendency can be characterized as solutions to variational problems.

In penalized regression, "L1 penalty" and "L2 penalty" refer to penalizing either the ${\displaystyle L^{1}}$ norm of a solution's vector of parameter values (i.e. the sum of its absolute values), or its ${\displaystyle L^{2}}$ norm (its Euclidean length). Techniques which use an L1 penalty, like LASSO, encourage solutions where many parameters are zero. Techniques which use an L2 penalty, like ridge regression, encourage solutions where most parameter values are small. Elastic net regularization uses a penalty term that is a combination of the ${\displaystyle L^{1}}$ norm and the ${\displaystyle L^{2}}$ norm of the parameter vector.

### Hausdorff–Young inequality

The Fourier transform for the real line (or, for periodic functions, see Fourier series), maps ${\displaystyle L^{p}(\mathbb {R} )}$ to ${\displaystyle L^{q}(\mathbb {R} )}$ (or ${\displaystyle L^{p}(\mathbf {T} )}$ to ${\displaystyle \ell ^{q}}$) respectively, where ${\displaystyle 1\leq p\leq 2}$ and ${\displaystyle {\tfrac {1}{p}}+{\tfrac {1}{q}}=1.}$ This is a consequence of the Riesz–Thorin interpolation theorem, and is made precise with the Hausdorff–Young inequality.

By contrast, if ${\displaystyle p>2,}$ the Fourier transform does not map into ${\displaystyle L^{q}.}$

### Hilbert spaces

Hilbert spaces are central to many applications, from quantum mechanics to stochastic calculus. The spaces ${\displaystyle L^{2}}$ and ${\displaystyle \ell ^{2}}$ are both Hilbert spaces. In fact, by choosing a Hilbert basis ${\displaystyle E,}$ i.e., a maximal orthonormal subset of ${\displaystyle L^{2}}$ or any Hilbert space, one sees that every Hilbert space is isometrically isomorphic to ${\displaystyle \ell ^{2}(E)}$ (same ${\displaystyle E}$ as above), i.e., a Hilbert space of type ${\displaystyle \ell ^{2}.}$

## The p-norm in finite dimensions

Illustrations of unit circles (see also superellipse) in ${\displaystyle \mathbb {R} ^{2}}$ based on different ${\displaystyle p}$-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 ${\displaystyle p}$).

The length of a vector ${\displaystyle x=(x_{1},x_{2},\dots ,x_{n})}$ in the ${\displaystyle n}$-dimensional real vector space ${\displaystyle \mathbb {R} ^{n}}$ is usually given by the Euclidean norm:

${\displaystyle \|x\|_{2}=\left({x_{1}}^{2}+{x_{2}}^{2}+\dotsb +{x_{n}}^{2}\right)^{1/2}.}$

The Euclidean distance between two points ${\displaystyle x}$ and ${\displaystyle y}$ is the length ${\displaystyle \|x-y\|_{2}}$ 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 rectilinear distance, which takes into account that streets are either orthogonal or parallel to each other. The class of ${\displaystyle p}$-norms generalizes these two examples and has an abundance of applications in many parts of mathematics, physics, and computer science.

### Definition

For a real number ${\displaystyle p\geq 1,}$ the ${\displaystyle p}$-norm or ${\displaystyle L^{p}}$-norm of ${\displaystyle x}$ is defined by

${\displaystyle \|x\|_{p}=\left(|x_{1}|^{p}+|x_{2}|^{p}+\dotsb +|x_{n}|^{p}\right)^{1/p}.}$
The absolute value bars can be dropped when ${\displaystyle p}$ is a rational number with an even numerator in its reduced form, and ${\displaystyle x}$ is drawn from the set of real numbers, or one of its subsets.

The Euclidean norm from above falls into this class and is the ${\displaystyle 2}$-norm, and the ${\displaystyle 1}$-norm is the norm that corresponds to the rectilinear distance.

The ${\displaystyle L^{\infty }}$-norm or maximum norm (or uniform norm) is the limit of the ${\displaystyle L^{p}}$-norms for ${\displaystyle p\to \infty .}$ It turns out that this limit is equivalent to the following definition:

${\displaystyle \|x\|_{\infty }=\max \left\{|x_{1}|,|x_{2}|,\dotsc ,|x_{n}|\right\}}$

See L-infinity.

For all ${\displaystyle p\geq 1,}$ the ${\displaystyle p}$-norms and maximum norm as defined above indeed satisfy the properties of a "length function" (or 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 (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 ${\displaystyle \mathbb {R} ^{n}}$ together with the ${\displaystyle p}$-norm is a normed vector space. Moreover, it turns out that this space is complete, thus making it a Banach space. This Banach space is the ${\displaystyle L^{p}}$-space over ${\displaystyle \{1,2,\ldots ,n\}.}$

#### Relations between ${\displaystyle p}$-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:

${\displaystyle \|x\|_{2}\leq \|x\|_{1}.}$

This fact generalizes to ${\displaystyle p}$-norms in that the ${\displaystyle p}$-norm ${\displaystyle \|x\|_{p}}$ of any given vector ${\displaystyle x}$ does not grow with ${\displaystyle p}$:

${\displaystyle \|x\|_{p+a}\leq \|x\|_{p}}$ for any vector ${\displaystyle x}$ and real numbers ${\displaystyle p\geq 1}$ and ${\displaystyle a\geq 0.}$ (In fact this remains true for ${\displaystyle 0 and ${\displaystyle a\geq 0}$ .)

For the opposite direction, the following relation between the ${\displaystyle 1}$-norm and the ${\displaystyle 2}$-norm is known:

${\displaystyle \|x\|_{1}\leq {\sqrt {n}}\|x\|_{2}~.}$

This inequality depends on the dimension ${\displaystyle n}$ of the underlying vector space and follows directly from the Cauchy–Schwarz inequality.

In general, for vectors in ${\displaystyle \mathbb {C} ^{n}}$ where ${\displaystyle 0

${\displaystyle \|x\|_{p}\leq \|x\|_{r}\leq n^{{\frac {1}{r}}-{\frac {1}{p}}}\|x\|_{p}~.}$

This is a consequence of Hölder's inequality.

### When ${\displaystyle 0

Astroid, unit circle in ${\displaystyle p={\tfrac {2}{3}}}$ metric

In ${\displaystyle \mathbb {R} ^{n}}$ for ${\displaystyle n>1,}$ the formula

${\displaystyle \|x\|_{p}=\left(|x_{1}|^{p}+|x_{2}|^{p}+\cdots +|x_{n}|^{p}\right)^{1/p}}$
defines an absolutely homogeneous function for ${\displaystyle 0 however, the resulting function does not define a norm, because it is not subadditive. On the other hand, the formula
${\displaystyle |x_{1}|^{p}+|x_{2}|^{p}+\dotsb +|x_{n}|^{p}}$
defines a subadditive function at the cost of losing absolute homogeneity. It does define an F-norm, though, which is homogeneous of degree ${\displaystyle p.}$

Hence, the function

${\displaystyle d_{p}(x,y)=\sum _{i=1}^{n}|x_{i}-y_{i}|^{p}}$
defines a metric. The metric space ${\displaystyle (\mathbb {R} ^{n},d_{p})}$ is denoted by ${\displaystyle \ell _{n}^{p}.}$

Although the ${\displaystyle p}$-unit ball ${\displaystyle B_{n}^{p}}$ around the origin in this metric is "concave", the topology defined on ${\displaystyle \mathbb {R} ^{n}}$ by the metric ${\displaystyle B_{p}}$ is the usual vector space topology of ${\displaystyle \mathbb {R} ^{n},}$ hence ${\displaystyle \ell _{n}^{p}}$ is a locally convex topological vector space. Beyond this qualitative statement, a quantitative way to measure the lack of convexity of ${\displaystyle \ell _{n}^{p}}$ is to denote by ${\displaystyle C_{p}(n)}$ the smallest constant ${\displaystyle C}$ such that the scalar multiple ${\displaystyle C\,B_{n}^{p}}$ of the ${\displaystyle p}$-unit ball contains the convex hull of ${\displaystyle B_{n}^{p},}$ which is equal to ${\displaystyle B_{n}^{1}.}$ The fact that for fixed ${\displaystyle p<1}$ we have

${\displaystyle C_{p}(n)=n^{{\tfrac {1}{p}}-1}\to \infty ,\quad {\text{as }}n\to \infty }$
shows that the infinite-dimensional sequence space ${\displaystyle \ell ^{p}}$ defined below, is no longer locally convex.[citation needed]

### When p = 0

There is one ${\displaystyle \ell _{0}}$ norm and another function called the ${\displaystyle \ell _{0}}$ "norm" (with quotation marks).

The mathematical definition of the ${\displaystyle \ell _{0}}$ norm was established by Banach's Theory of Linear Operations. The space of sequences has a complete metric topology provided by the F-norm

${\displaystyle (x_{n})\mapsto \sum _{n}2^{-n}{\frac {|x_{n}|}{1+|x_{n}|}},}$
which is discussed by Stefan Rolewicz in Metric Linear Spaces.[1] The ${\displaystyle \ell _{0}}$-normed space is studied in functional analysis, probability theory, and harmonic analysis.

Another function was called the ${\displaystyle \ell _{0}}$ "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 ${\displaystyle x.}$[citation needed] Many authors abuse terminology by omitting the quotation marks. Defining ${\displaystyle 0^{0}=0,}$ the zero "norm" of ${\displaystyle x}$ is equal to

${\displaystyle |x_{1}|^{0}+|x_{2}|^{0}+\cdots +|x_{n}|^{0}.}$
An animated gif of p-norms 0.1 through 2 with a step of 0.05.

This is not a norm because it is not homogeneous. For example, scaling the vector ${\displaystyle x}$ 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–notably in compressed sensing in signal processing and computational harmonic analysis. Despite not being a norm, the associated metric, known as Hamming distance, is a valid distance, since homogeneity is not required for distances.

## The p-norm in infinite dimensions and ℓp spaces

### The sequence space ℓp

The ${\displaystyle p}$-norm can be extended to vectors that have an infinite number of components (sequences), which yields the space ${\displaystyle \ell ^{p}.}$This contains as special cases:

• ${\displaystyle \ell ^{1},}$ the space of sequences whose series is absolutely convergent,
• ${\displaystyle \ell ^{2},}$ the space of square-summable sequences, which is a Hilbert space, and
• ${\displaystyle \ell ^{\infty },}$ 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) numbers are given by:

{\displaystyle {\begin{aligned}&(x_{1},x_{2},\ldots ,x_{n},x_{n+1},\ldots )+(y_{1},y_{2},\ldots ,y_{n},y_{n+1},\ldots )\\={}&(x_{1}+y_{1},x_{2}+y_{2},\ldots ,x_{n}+y_{n},x_{n+1}+y_{n+1},\ldots ),\\[6pt]&\lambda \cdot \left(x_{1},x_{2},\ldots ,x_{n},x_{n+1},\ldots \right)\\={}&(\lambda x_{1},\lambda x_{2},\ldots ,\lambda x_{n},\lambda x_{n+1},\ldots ).\end{aligned}}}

Define the ${\displaystyle p}$-norm:

${\displaystyle \|x\|_{p}=\left(|x_{1}|^{p}+|x_{2}|^{p}+\cdots +|x_{n}|^{p}+|x_{n+1}|^{p}+\cdots \right)^{1/p}}$

Here, a complication arises, namely that the series on the right is not always convergent, so for example, the sequence made up of only ones, ${\displaystyle (1,1,1,\ldots ),}$ will have an infinite ${\displaystyle p}$-norm for ${\displaystyle 1\leq p<\infty .}$ The space ${\displaystyle \ell ^{p}}$ is then defined as the set of all infinite sequences of real (or complex) numbers such that the ${\displaystyle p}$-norm is finite.

One can check that as ${\displaystyle p}$ increases, the set ${\displaystyle \ell ^{p}}$ grows larger. For example, the sequence

${\displaystyle \left(1,{\frac {1}{2}},\ldots ,{\frac {1}{n}},{\frac {1}{n+1}},\ldots \right)}$
is not in ${\displaystyle \ell ^{1},}$ but it is in ${\displaystyle \ell ^{p}}$ for ${\displaystyle p>1,}$ as the series
${\displaystyle 1^{p}+{\frac {1}{2^{p}}}+\cdots +{\frac {1}{n^{p}}}+{\frac {1}{(n+1)^{p}}}+\cdots ,}$
diverges for ${\displaystyle p=1}$ (the harmonic series), but is convergent for ${\displaystyle p>1.}$

One also defines the ${\displaystyle \infty }$-norm using the supremum:

${\displaystyle \|x\|_{\infty }=\sup(|x_{1}|,|x_{2}|,\dotsc ,|x_{n}|,|x_{n+1}|,\ldots )}$
and the corresponding space ${\displaystyle \ell ^{\infty }}$ of all bounded sequences. It turns out that[2]
${\displaystyle \|x\|_{\infty }=\lim _{p\to \infty }\|x\|_{p}}$
if the right-hand side is finite, or the left-hand side is infinite. Thus, we will consider ${\displaystyle \ell ^{p}}$ spaces for ${\displaystyle 1\leq p\leq \infty .}$

The ${\displaystyle p}$-norm thus defined on ${\displaystyle \ell ^{p}}$ is indeed a norm, and ${\displaystyle \ell ^{p}}$ together with this norm is a Banach space. The fully general ${\displaystyle L^{p}}$ 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, functions. An integral instead of a sum is used to define the ${\displaystyle p}$-norm.

### General ℓp-space

In complete analogy to the preceding definition one can define the space ${\displaystyle \ell ^{p}(I)}$ over a general index set ${\displaystyle I}$ (and ${\displaystyle 1\leq p<\infty }$) as

${\displaystyle \ell ^{p}(I)=\left\{(x_{i})_{i\in I}\in \mathbb {K} ^{I}:\sum _{i\in I}|x_{i}|^{p}<+\infty \right\},}$
where convergence on the right means that only countably many summands are nonzero (see also Unconditional convergence). With the norm
${\displaystyle \|x\|_{p}=\left(\sum _{i\in I}|x_{i}|^{p}\right)^{1/p}}$
the space ${\displaystyle \ell ^{p}(I)}$ becomes a Banach space. In the case where ${\displaystyle I}$ is finite with ${\displaystyle n}$ elements, this construction yields ${\displaystyle \mathbb {R} ^{n}}$ with the ${\displaystyle p}$-norm defined above. If ${\displaystyle I}$ is countably infinite, this is exactly the sequence space ${\displaystyle \ell ^{p}}$ defined above. For uncountable sets ${\displaystyle I}$ this is a non-separable Banach space which can be seen as the locally convex direct limit of ${\displaystyle \ell ^{p}}$-sequence spaces.[3]

For ${\displaystyle p=2,}$ the ${\displaystyle \|\,\cdot \,\|_{2}}$-norm is even induced by a canonical inner product ${\displaystyle \langle \,\cdot ,\,\cdot \rangle ,}$ called the Euclidean inner product, which means that ${\displaystyle \|\mathbf {x} \|_{2}={\sqrt {\langle \mathbf {x} ,\mathbf {x} \rangle }}}$ holds for all vectors ${\displaystyle \mathbf {x} .}$ This inner product can expressed in terms of the norm by using the polarization identity. On ${\displaystyle \ell ^{2},}$ it can be defined by

${\displaystyle \langle \left(x_{i}\right)_{i},\left(y_{n}\right)_{i}\rangle _{\ell ^{2}}~=~\sum _{i}x_{i}{\overline {y_{i}}}}$
while for the space ${\displaystyle L^{2}(X,\mu )}$ associated with a measure space ${\displaystyle (X,\Sigma ,\mu ),}$ which consists of all square-integrable functions, it is
${\displaystyle \langle f,g\rangle _{L^{2}}=\int _{X}f(x){\overline {g(x)}}\,\mathrm {d} x.}$

Now consider the case ${\displaystyle p=\infty .}$ Define[note 1]

${\displaystyle \ell ^{\infty }(I)=\{x\in \mathbb {K} ^{I}:\sup \operatorname {range} |x|<+\infty \},}$
where for all ${\displaystyle x}$[4][note 2]
${\displaystyle \|x\|_{\infty }\equiv \inf\{C\in \mathbb {R} _{\geq 0}:|x_{i}|\leq C{\text{ for all }}i\in I\}={\begin{cases}\sup \operatorname {range} |x|&{\text{if }}X\neq \varnothing ,\\0&{\text{if }}X=\varnothing .\end{cases}}}$

The index set ${\displaystyle I}$ can be turned into a measure space by giving it the discrete σ-algebra and the counting measure. Then the space ${\displaystyle \ell ^{p}(I)}$ is just a special case of the more general ${\displaystyle L^{p}}$-space (defined below).

## Lp spaces and Lebesgue integrals

An ${\displaystyle L^{p}}$ space may be defined as a space of measurable functions for which the ${\displaystyle p}$-th power of the absolute value is Lebesgue integrable, where functions which agree almost everywhere are identified. More generally, let ${\displaystyle 1\leq p<\infty }$ and ${\displaystyle (S,\Sigma ,\mu )}$ be a measure space. Consider the set ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu )}$ of all measurable functions from ${\displaystyle S}$ to ${\displaystyle \mathbb {C} }$ or ${\displaystyle \mathbb {R} }$ whose absolute value raised to the ${\displaystyle p}$-th power has a finite integral, or equivalently, that

${\displaystyle \|f\|_{p}=\left(\int _{S}|f|^{p}\;\mathrm {d} \mu \right)^{1/p}<\infty .}$

For ${\displaystyle p=\infty ,}$ the space ${\displaystyle {\mathcal {L}}^{\infty }(S,\mu )}$ is the space of measurable functions ${\displaystyle f}$ bounded almost everywhere, whose seminorm ${\displaystyle \|f\|_{\infty }}$ is the infimum of (the absolute values of) these bounds, which when ${\displaystyle \mu (S)\neq 0}$ is the same as the essential supremum of its absolute value:[note 3]

${\displaystyle \|f\|_{\infty }=\inf\{C\in \mathbb {R} _{\geq 0}:|f(x)|\leq C{\text{ for almost every }}x\}={\begin{cases}\operatorname {esssup} |f|&{\text{if }}0<\mu (S),\\0&{\text{if }}0=\mu (S).\end{cases}}}$
Two functions ${\displaystyle f}$ and ${\displaystyle g}$ defined on ${\displaystyle S}$ are said to be equal almost everywhere, written ${\displaystyle f=g}$ a.e., if the set ${\displaystyle \{s\in S:f(s)\neq g(s)\}}$ is measurable and has measure zero. If ${\displaystyle f}$ is a measurable function that is equal to ${\displaystyle 0}$ almost everywhere[note 4] then ${\displaystyle \|f\|_{p}=0}$ for every ${\displaystyle p}$ and thus ${\displaystyle f\in {\mathcal {L}}^{p}(S,\,\mu )}$ for all ${\displaystyle p.}$

Seminormed space of ${\displaystyle p}$-th power integrable functions

Each set of functions ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu )}$ forms a vector space when addition and scalar multiplication are defined pointwise.[note 5] That the sum of two ${\displaystyle p}$-th power integrable functions ${\displaystyle f}$ and ${\displaystyle g}$ is again ${\displaystyle p}$-th power integrable follows from ${\textstyle \|f+g\|_{p}^{p}\leq 2^{p-1}\left(\|f\|_{p}^{p}+\|g\|_{p}^{p}\right),}$[proof 1] although it is also a consequence of Minkowski's inequality

${\displaystyle \|f+g\|_{p}\leq \|f\|_{p}+\|g\|_{p}}$
which establishes that ${\displaystyle \|\cdot \|_{p}}$ satisfies the triangle inequality. That ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu )}$ is closed under scalar multiplication is due to ${\displaystyle \|\cdot \|_{p}}$ being absolutely homogeneous, which means that ${\displaystyle \|sf\|_{p}=|s|\|f\|_{p}}$ for every scalar ${\displaystyle s}$ and every function ${\displaystyle f.}$

Absolute homogeneity, the triangle inequality, and non-negativity are the defining properties of a seminorm. Thus ${\displaystyle \|\cdot \|_{p}}$ is a seminorm and the set ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu )}$ of ${\displaystyle p}$-th power integrable functions together with the function ${\displaystyle \|\cdot \|_{p}}$ defines a seminormed vector space. In general, the seminorm ${\displaystyle \|\cdot \|_{p}}$ is not a norm because there might exist measurable functions ${\displaystyle f}$ that satisfy ${\displaystyle \|f\|_{p}=0}$ but are not identically equal to ${\displaystyle 0}$[note 4] (${\displaystyle \|\cdot \|_{p}}$ is a norm if and only if no such ${\displaystyle f}$ exists).

Quotient vector space

Like every seminorm, the seminorm ${\displaystyle \|\cdot \|_{p}}$ induces a norm (defined shortly) on the quotient of ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu )}$ by the vector subspace ${\displaystyle \ker \|\cdot \|_{p}\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;\{f\in {\mathcal {L}}^{p}(S,\,\mu ):\|f\|_{p}=0\}.}$ This normed quotient space is called Lebesgue space and it is the subject of this article. If the seminorm ${\displaystyle \|\cdot \|_{p}}$ happens to be a norm then the normed quotient space that will now be defined is linearly isometrically isomorphic to ${\displaystyle \left({\mathcal {L}}^{p}(S,\,\mu ),\|\cdot \|_{p}\right);}$ they will be, up to a linear isometry, the same normed space and so they may both be called "${\displaystyle L^{p}}$ space".

If ${\displaystyle f}$ is any measurable function, then ${\displaystyle \|f\|_{p}=0}$ if and only if ${\displaystyle f=0}$ almost everywhere. Since the right hand side (${\displaystyle f=0}$ a.e.) does not mention ${\displaystyle p,}$ it follows that all seminorms ${\displaystyle \|\cdot \|_{p}}$ have the same zero set/kernel (it does not depend on ${\displaystyle p}$). So denote this common vector subspace by

${\displaystyle {\mathcal {N}}\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;\{f:f=0\ \mu {\text{-almost everywhere}}\}=\{f:\|f\|_{p}=0\}\qquad \forall \ p.}$
Given any ${\displaystyle f\in {\mathcal {L}}^{p}(S,\,\mu ),}$ the coset ${\displaystyle f+{\mathcal {N}}\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;\{f+h:h\in {\mathcal {N}}\}}$ consists of all measurable functions ${\displaystyle g}$ that are equal to ${\displaystyle f}$ almost everywhere. Two cosets are equal ${\displaystyle f+{\mathcal {N}}=g+{\mathcal {N}}}$ if and only if ${\displaystyle f=g}$ almost everywhere, which happens if and only if ${\displaystyle g\in f+{\mathcal {N}}.}$ The set of all cosets
${\displaystyle L^{p}(S,\mu )~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~{\mathcal {L}}^{p}(S,\mu )/{\mathcal {N}}~=~\{f+{\mathcal {N}}:f\in {\mathcal {L}}^{p}(S,\mu )\}}$
forms a vector space when vector addition and scalar multiplication are defined by ${\displaystyle (f+{\mathcal {N}})+(g+{\mathcal {N}})\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;(f+g)+{\mathcal {N}}}$ and ${\displaystyle s(f+{\mathcal {N}})\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;(sf)+{\mathcal {N}}.}$ This space is the canonical quotient space of ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu )}$ with respect to ${\displaystyle {\mathcal {N}}=\ker \|\cdot \|_{p}.}$ In the quotient space, two functions ${\displaystyle f}$ and ${\displaystyle g}$ are identified if ${\displaystyle f+{\mathcal {N}}=g+{\mathcal {N}}}$ (or equivalently, if ${\displaystyle f-g\in {\mathcal {N}}}$), which happens if and only if ${\displaystyle f=g}$ almost everywhere.

The ${\displaystyle p}$-norm on the quotient vector space

Given any ${\displaystyle f\in {\mathcal {L}}^{p}(S,\,\mu ),}$ the value of the seminorm ${\displaystyle \|\cdot \|_{p}}$ on the coset ${\displaystyle f+{\mathcal {N}}=\{f+h:h\in {\mathcal {N}}\}}$ is constant and equal to ${\displaystyle \|f\|_{p};}$ denote this unique value by ${\displaystyle \|f+{\mathcal {N}}\|_{p},}$ so that:

${\displaystyle \|f+{\mathcal {N}}\|_{p}\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;\|f\|_{p}.}$
This assignment ${\displaystyle f+{\mathcal {N}}\mapsto \|f+{\mathcal {N}}\|_{p}}$ defines a map, which will also be denoted by ${\displaystyle \|\cdot \|_{p},}$ on the quotient vector space ${\displaystyle L^{p}(S,\mu )={\mathcal {L}}^{p}(S,\mu )/{\mathcal {N}}=\{f+{\mathcal {N}}:f\in {\mathcal {L}}^{p}(S,\mu )\}.}$ This map is a norm on ${\displaystyle L^{p}(S,\mu )}$ called the ${\displaystyle p}$-norm. The value ${\displaystyle \|f+{\mathcal {N}}\|_{p}}$ of a coset ${\displaystyle f+{\mathcal {N}}}$ is independent of the particular function ${\displaystyle f}$ that was chosen to represent the coset since if ${\displaystyle g}$ is any other function then ${\displaystyle g\in f+{\mathcal {N}}}$ if and only if ${\displaystyle f+{\mathcal {N}}=g+{\mathcal {N}}}$ if and only if ${\displaystyle f=g}$ almost everywhere, in which case ${\displaystyle \|f\|_{p}=\|g\|_{p}}$ and so ${\displaystyle \|f+{\mathcal {N}}\|_{p}\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;\|f\|_{p}}$ does indeed equal ${\displaystyle \|g+{\mathcal {N}}\|_{p}\;{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\;\|g\|_{p}.}$

The Lebesgue ${\displaystyle L^{p}}$ space

The normed vector space ${\displaystyle \left(L^{p}(S,\mu ),\|\cdot \|_{p}\right)}$ is called ${\displaystyle L^{p}}$ space or the Lebesgue space of ${\displaystyle p}$-th power integrable functions and it is a Banach space for every ${\displaystyle 1\leq p\leq \infty }$ (meaning that it is a complete metric space, a result that is sometimes called the Riesz–Fischer theorem). When the underlying measure space ${\displaystyle S}$ is understood then ${\displaystyle L^{p}(S,\mu )}$ is often abbreviated ${\displaystyle L^{p}(\mu ),}$ or even just ${\displaystyle L^{p}.}$ Depending on the author, the subscript notation ${\displaystyle L_{p}}$ might denote either ${\displaystyle L^{p}(S,\mu )}$ or ${\displaystyle L^{1/p}(S,\mu ).}$

The above definitions generalize to Bochner spaces.

In general, this process cannot be reversed: there is no consistent way to define a "canonical" representative of each coset of ${\displaystyle {\mathcal {N}}}$ in ${\displaystyle L^{p}.}$ For ${\displaystyle L^{\infty },}$ however, there is a theory of lifts enabling such recovery.

### Special cases

Similar to the ${\displaystyle \ell ^{p}}$ spaces, ${\displaystyle L^{2}}$ is the only Hilbert space among ${\displaystyle L^{p}}$ spaces. In the complex case, the inner product on ${\displaystyle L^{2}}$ is defined by

${\displaystyle \langle f,g\rangle =\int _{S}f(x){\overline {g(x)}}\,\mathrm {d} \mu (x)}$

The additional inner product structure allows for a richer theory, with applications to, for instance, Fourier series and quantum mechanics. Functions in ${\displaystyle L^{2}}$ are sometimes called square-integrable functions, quadratically 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 (Titchmarsh 1976).

If we use complex-valued functions, the space ${\displaystyle L^{\infty }}$ 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 ${\displaystyle L^{\infty }}$ defines a bounded operator on any ${\displaystyle L^{p}}$ space by multiplication.

For ${\displaystyle 1\leq p\leq \infty }$ the ${\displaystyle \ell ^{p}}$ spaces are a special case of ${\displaystyle L^{p}}$ spaces, when ${\displaystyle S=\mathbf {N} )}$ consists of the natural numbers and ${\displaystyle \mu }$ is the counting measure on ${\displaystyle \mathbf {N} .}$More generally, if one considers any set ${\displaystyle S}$ with the counting measure, the resulting ${\displaystyle L^{p}}$ space is denoted ${\displaystyle \ell ^{p}(S).}$ For example, the space ${\displaystyle \ell ^{p}(\mathbf {Z} )}$is the space of all sequences indexed by the integers, and when defining the ${\displaystyle p}$-norm on such a space, one sums over all the integers. The space ${\displaystyle \ell ^{p}(n),}$ where ${\displaystyle n}$ is the set with ${\displaystyle n}$ elements, is ${\displaystyle \mathbb {R} ^{n}}$ with its ${\displaystyle p}$-norm as defined above. As any Hilbert space, every space ${\displaystyle L^{2}}$ is linearly isometric to a suitable ${\displaystyle \ell ^{2}(I),}$ where the cardinality of the set ${\displaystyle I}$ is the cardinality of an arbitrary Hilbertian basis for this particular ${\displaystyle L^{2}.}$

## Properties of Lp spaces

As in the discrete case, if there exists ${\displaystyle q<\infty }$ such that ${\displaystyle f\in L^{\infty }(S,\mu )\cap L^{q}(S,\mu ),}$ then

${\displaystyle \|f\|_{\infty }=\lim _{p\to \infty }\|f\|_{p}.}$

Hölder's inequality

Suppose ${\displaystyle p,q,r\in [1,\infty ]}$ satisfy ${\displaystyle {\tfrac {1}{p}}+{\tfrac {1}{q}}={\tfrac {1}{r}}}$ (where ${\displaystyle {\tfrac {1}{\infty }}:=0}$). If ${\displaystyle f\in L^{p}(S,\mu )}$ and ${\displaystyle g\in L^{q}(S,\mu )}$ then ${\displaystyle fg\in L^{r}(S,\mu )}$ and[5]

${\displaystyle \|fg\|_{r}~\leq ~\|f\|_{p}\,\|g\|_{q}.}$

This inequality, called Hölder's inequality, is in some sense optimal[5] since if ${\displaystyle r=1}$ (so ${\displaystyle {\tfrac {1}{p}}+{\tfrac {1}{q}}=1}$) and ${\displaystyle f}$ is a measurable function such that

${\displaystyle \sup _{\|g\|_{q}\leq 1}\,\int _{S}|fg|\,\mathrm {d} \mu ~<~\infty }$
where the supremum is taken over the closed unit ball of ${\displaystyle L^{q}(S,\mu ),}$ then ${\displaystyle f\in L^{p}(S,\mu )}$ and
${\displaystyle \|f\|_{p}~=~\sup _{\|g\|_{q}\leq 1}\,\int _{S}fg\,\mathrm {d} \mu .}$

Minkowski inequality

Minkowski inequality, which states that ${\displaystyle \|\cdot \|_{p}}$ satisfies the triangle inequality, can be generalized: If the measurable function ${\displaystyle F:S_{1}\times S_{2}\to \mathbb {R} }$ is non-negative then for all ${\displaystyle 1\leq p\leq q\leq \infty ,}$[6]

${\displaystyle \left\|\left\|F(\,\cdot ,s_{2})\right\|_{L^{p}(S_{1},\mu _{1})}\right\|_{L^{q}(S_{2},\mu _{2})}~\leq ~\left\|\left\|F(s_{1},\cdot )\right\|_{L^{q}(S_{2},\mu _{2})}\right\|_{L^{p}(S_{1},\mu _{1})}\ .}$

### Atomic decomposition

If ${\displaystyle 1\leq p<\infty }$ then every non-negative ${\displaystyle f\in L^{p}(\mu )}$ has an atomic decomposition,[7] meaning that there exist a sequence ${\displaystyle (r_{n})_{n\in \mathbb {Z} }}$ of non-negative real numbers and a sequence of non-negative functions ${\displaystyle (f_{n})_{n\in \mathbb {Z} },}$ called the atoms, whose supports ${\displaystyle \left(\operatorname {supp} f_{n}\right)_{n\in \mathbb {Z} }}$ are pairwise disjoint sets of measure ${\displaystyle \mu \left(\operatorname {supp} f_{n}\right)\leq 2^{n+1},}$ such that

${\displaystyle f~=~\sum _{n\in \mathbb {Z} }r_{n}\,f_{n}\,,}$
and for every integer ${\displaystyle n\in \mathbb {Z} ,}$
${\displaystyle \|f_{n}\|_{\infty }~\leq ~2^{-{\tfrac {n}{p}}}\,,}$
and
${\displaystyle {\tfrac {1}{2}}\|f\|_{p}^{p}~\leq ~\sum _{n\in \mathbb {Z} }r_{n}^{p}~\leq ~2\|f\|_{p}^{p}\,,}$
and where moreover, the sequence of functions ${\displaystyle (r_{n}f_{n})_{n\in \mathbb {Z} }}$ depends only on ${\displaystyle f}$ (it is independent of ${\displaystyle p}$).[7] These inequalities guarantee that ${\displaystyle \|f_{n}\|_{p}^{p}\leq 2}$ for all integers ${\displaystyle n}$ while the supports of ${\displaystyle (f_{n})_{n\in \mathbb {Z} }}$ being pairwise disjoint implies[7]
${\displaystyle \|f\|_{p}^{p}~=~\sum _{n\in \mathbb {Z} }r_{n}^{p}\,\|f_{n}\|_{p}^{p}\,.}$

An atomic decomposition can be explicitly given by first defining for every integer ${\displaystyle n\in \mathbb {Z} ,}$[7]

${\displaystyle t_{n}=\inf\{t\in \mathbb {R} :\mu (f>t)<2^{n}\}}$
(this infimum is attained by ${\displaystyle t_{n};}$ that is, ${\displaystyle \mu (f>t_{n})<2^{n}}$ holds) and then letting
${\displaystyle r_{n}~=~2^{n/p}\,t_{n}~{\text{ and }}\quad f_{n}~=~{\frac {f}{r_{n}}}\,\mathbf {1} _{(t_{n+1}
where ${\displaystyle \mu (f>t)=\mu (\{s:f(s)>t\})}$ denotes the measure of the set ${\displaystyle (f>t):=\{s\in S:f(s)>t\}}$ and ${\displaystyle \mathbf {1} _{(t_{n+1} denotes the indicator function of the set ${\displaystyle (t_{n+1} The sequence ${\displaystyle (t_{n})_{n\in \mathbb {Z} }}$ is decreasing and converges to ${\displaystyle 0}$ as ${\displaystyle n\to \infty .}$[7] Consequently, if ${\displaystyle t_{n}=0}$ then ${\displaystyle t_{n+1}=0}$ and ${\displaystyle (t_{n+1} so that ${\displaystyle f_{n}={\frac {1}{r_{n}}}\,f\,\mathbf {1} _{(t_{n+1} is identically equal to ${\displaystyle 0}$ (in particular, the division ${\displaystyle {\tfrac {1}{r_{n}}}}$ by ${\displaystyle r_{n}=0}$ causes no issues).

The complementary cumulative distribution function ${\displaystyle t\in \mathbb {R} \mapsto \mu (|f|>t)}$ of ${\displaystyle |f|=f}$ that was used to define the ${\displaystyle t_{n}}$ also appears in the definition of the weak ${\displaystyle L^{p}}$-norm (given below) and can be used to express the ${\displaystyle p}$-norm ${\displaystyle \|\cdot \|_{p}}$ (for ${\displaystyle 1\leq p<\infty }$) of ${\displaystyle f\in L^{p}(S,\mu )}$ as the integral[7]

${\displaystyle \|f\|_{p}^{p}~=~p\,\int _{0}^{\infty }t^{p-1}\mu (|f|>t)\,\mathrm {d} t\,,}$
where the integration is with respect to the usual Lebesgue measure on ${\displaystyle (0,\infty ).}$

### Dual spaces

The dual space (the Banach space of all continuous linear functionals) of ${\displaystyle L^{p}(\mu )}$ for ${\displaystyle 1 has a natural isomorphism with ${\displaystyle L^{q}(\mu ),}$ where ${\displaystyle q}$ is such that ${\displaystyle {\tfrac {1}{p}}+{\tfrac {1}{q}}=1}$ (i.e. ${\displaystyle q={\tfrac {p}{p-1}}}$). This isomorphism associates ${\displaystyle g\in L^{q}(\mu )}$ with the functional ${\displaystyle \kappa _{p}(g)\in L^{p}(\mu )^{*}}$ defined by

${\displaystyle f\mapsto \kappa _{p}(g)(f)=\int fg\,\mathrm {d} \mu }$
for every ${\displaystyle f\in L^{p}(\mu ).}$

The fact that ${\displaystyle \kappa _{p}(g)}$ is well defined and continuous follows from Hölder's inequality. ${\displaystyle \kappa _{p}:L^{q}(\mu )\to L^{p}(\mu )^{*}}$ is a linear mapping which is an isometry by the extremal case of Hölder's inequality. It is also possible to show (for example with the Radon–Nikodym theorem, see[8]) that any ${\displaystyle G\in L^{p}(\mu )^{*}}$ can be expressed this way: i.e., that ${\displaystyle \kappa _{p}}$ is onto. Since ${\displaystyle \kappa _{p}}$ is onto and isometric, it is an isomorphism of Banach spaces. With this (isometric) isomorphism in mind, it is usual to say simply that ${\displaystyle L^{q}(\mu )}$ is the continuous dual space of ${\displaystyle L^{p}(\mu ).}$

For ${\displaystyle 1 the space ${\displaystyle L^{p}(\mu )}$ is reflexive. Let ${\displaystyle \kappa _{p}}$ be as above and let ${\displaystyle \kappa _{q}:L^{p}(\mu )\to L^{q}(\mu )^{*}}$ be the corresponding linear isometry. Consider the map from ${\displaystyle L^{p}(\mu )}$ to ${\displaystyle L^{p}(\mu )^{**},}$ obtained by composing ${\displaystyle \kappa _{q}}$ with the transpose (or adjoint) of the inverse of ${\displaystyle \kappa _{p}:}$

${\displaystyle j_{p}:L^{p}(\mu )\mathrel {\overset {\kappa _{q}}{\longrightarrow }} L^{q}(\mu )^{*}\mathrel {\overset {\left(\kappa _{p}^{-1}\right)^{*}}{\longrightarrow }} L^{p}(\mu )^{**}}$

This map coincides with the canonical embedding ${\displaystyle J}$ of ${\displaystyle L^{p}(\mu )}$ into its bidual. Moreover, the map ${\displaystyle j_{p}}$ is onto, as composition of two onto isometries, and this proves reflexivity.

If the measure ${\displaystyle \mu }$ on ${\displaystyle S}$ is sigma-finite, then the dual of ${\displaystyle L^{1}(\mu )}$ is isometrically isomorphic to ${\displaystyle L^{\infty }(\mu )}$ (more precisely, the map ${\displaystyle \kappa _{1}}$ corresponding to ${\displaystyle p=1}$ is an isometry from ${\displaystyle L^{\infty }(\mu )}$ onto ${\displaystyle L^{1}(\mu )^{*}.}$

The dual of ${\displaystyle L^{\infty }(\mu )}$ is subtler. Elements of ${\displaystyle L^{\infty }(\mu )^{*}}$ can be identified with bounded signed finitely additive measures on ${\displaystyle S}$ that are absolutely continuous with respect to ${\displaystyle \mu .}$ See ba space for more details. If we assume the axiom of choice, this space is much bigger than ${\displaystyle L^{1}(\mu )}$ except in some trivial cases. However, Saharon Shelah proved that there are relatively consistent extensions of Zermelo–Fraenkel set theory (ZF + DC + "Every subset of the real numbers has the Baire property") in which the dual of ${\displaystyle \ell ^{\infty }}$ is ${\displaystyle \ell ^{1}.}$[9]

### Embeddings

Colloquially, if ${\displaystyle 1\leq p then ${\displaystyle L^{p}(S,\mu )}$ contains functions that are more locally singular, while elements of ${\displaystyle L^{q}(S,\mu )}$ can be more spread out. Consider the Lebesgue measure on the half line ${\displaystyle (0,\infty ).}$ A continuous function in ${\displaystyle L^{1}}$ might blow up near ${\displaystyle 0}$ but must decay sufficiently fast toward infinity. On the other hand, continuous functions in ${\displaystyle L^{\infty }}$ need not decay at all but no blow-up is allowed. The precise technical result is the following.[10] Suppose that ${\displaystyle 0 Then:

1. ${\displaystyle L^{q}(S,\mu )\subseteq L^{p}(S,\mu )}$ if and only if ${\displaystyle S}$ does not contain sets of finite but arbitrarily large measure, and
2. ${\displaystyle L^{p}(S,\mu )\subseteq L^{q}(S,\mu )}$ if and only if ${\displaystyle S}$ 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 ${\displaystyle L^{q}}$ to ${\displaystyle L^{p}}$ in the first case, and ${\displaystyle L^{p}}$ to ${\displaystyle L^{q}}$ in the second. (This is a consequence of the closed graph theorem and properties of ${\displaystyle L^{p}}$ spaces.) Indeed, if the domain ${\displaystyle S}$ has finite measure, one can make the following explicit calculation using Hölder's inequality

${\displaystyle \ \|\mathbf {1} f^{p}\|_{1}\leq \|\mathbf {1} \|_{q/(q-p)}\|f^{p}\|_{q/p}}$
leading to
${\displaystyle \ \|f\|_{p}\leq \mu (S)^{1/p-1/q}\|f\|_{q}.}$

The constant appearing in the above inequality is optimal, in the sense that the operator norm of the identity ${\displaystyle I:L^{q}(S,\mu )\to L^{p}(S,\mu )}$ is precisely

${\displaystyle \|I\|_{q,p}=\mu (S)^{1/p-1/q}}$
the case of equality being achieved exactly when ${\displaystyle f=1}$ ${\displaystyle \mu }$-almost-everywhere.

### Dense subspaces

Throughout this section we assume that ${\displaystyle 1\leq p<\infty .}$

Let ${\displaystyle (S,\Sigma ,\mu )}$ be a measure space. An integrable simple function ${\displaystyle f}$ on ${\displaystyle S}$ is one of the form

${\displaystyle f=\sum _{j=1}^{n}a_{j}\mathbf {1} _{A_{j}}}$
where ${\displaystyle a_{j}}$ are scalars, ${\displaystyle A_{j}\in \Sigma }$ has finite measure and ${\displaystyle {\mathbf {1} }_{A_{j}}}$ is the indicator function of the set ${\displaystyle A_{j},}$ for ${\displaystyle j=1,\dots ,n.}$ By construction of the integral, the vector space of integrable simple functions is dense in ${\displaystyle L^{p}(S,\Sigma ,\mu ).}$

More can be said when ${\displaystyle S}$ is a normal topological space and ${\displaystyle \Sigma }$ its Borel 𝜎–algebra, i.e., the smallest 𝜎–algebra of subsets of ${\displaystyle S}$ containing the open sets.

Suppose ${\displaystyle V\subseteq S}$ is an open set with ${\displaystyle \mu (V)<\infty .}$ It can be proved that for every Borel set ${\displaystyle A\in \Sigma }$ contained in ${\displaystyle V,}$ and for every ${\displaystyle \varepsilon >0,}$ there exist a closed set ${\displaystyle F}$ and an open set ${\displaystyle U}$ such that

${\displaystyle F\subseteq A\subseteq U\subseteq V\quad {\text{and}}\quad \mu (U)-\mu (F)=\mu (U\setminus F)<\varepsilon }$

It follows that there exists a continuous Urysohn function ${\displaystyle 0\leq \varphi \leq 1}$ on ${\displaystyle S}$ that is ${\displaystyle 1}$ on ${\displaystyle F}$ and ${\displaystyle 0}$ on ${\displaystyle S\setminus U,}$ with

${\displaystyle \int _{S}|\mathbf {1} _{A}-\varphi |\,\mathrm {d} \mu <\varepsilon \,.}$

If ${\displaystyle S}$ can be covered by an increasing sequence ${\displaystyle (V_{n})}$ of open sets that have finite measure, then the space of ${\displaystyle p}$–integrable continuous functions is dense in ${\displaystyle L^{p}(S,\Sigma ,\mu ).}$ More precisely, one can use bounded continuous functions that vanish outside one of the open sets ${\displaystyle V_{n}.}$

This applies in particular when ${\displaystyle S=\mathbb {R} ^{d}}$ and when ${\displaystyle \mu }$ is the Lebesgue measure. The space of continuous and compactly supported functions is dense in ${\displaystyle L^{p}(\mathbb {R} ^{d}).}$ Similarly, the space of integrable step functions is dense in ${\displaystyle L^{p}(\mathbb {R} ^{d});}$ this space is the linear span of indicator functions of bounded intervals when ${\displaystyle d=1,}$ of bounded rectangles when ${\displaystyle d=2}$ and more generally of products of bounded intervals.

Several properties of general functions in ${\displaystyle L^{p}(\mathbb {R} ^{d})}$ 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 ${\displaystyle L^{p}(\mathbb {R} ^{d}),}$ in the following sense:

${\displaystyle \forall f\in L^{p}\left(\mathbb {R} ^{d}\right):\quad \left\|\tau _{t}f-f\right\|_{p}\to 0,\quad {\text{as }}\mathbb {R} ^{d}\ni t\to 0,}$
where
${\displaystyle (\tau _{t}f)(x)=f(x-t).}$

### Closed subspaces

If ${\displaystyle \mu }$ is a probability measure on a measurable space ${\displaystyle (S,\Sigma ),}$ ${\displaystyle 0 is any positive real number, and ${\displaystyle V\subseteq L^{\infty }(\mu )}$ is a vector subspace, then ${\displaystyle V}$ is a closed subspace of ${\displaystyle L^{p}(\mu )}$ if and only if ${\displaystyle V}$ is finite-dimensional[11] (note that ${\displaystyle V}$ was chosen independent of ${\displaystyle p}$). In this theorem, which is due to Alexander Grothendieck,[11] it is crucial that the vector space ${\displaystyle V}$ be a subset of ${\displaystyle L^{\infty }}$ since it is possible to construct an infinite-dimensional closed vector subspace of ${\displaystyle L^{1}\left(S^{1},{\tfrac {1}{2\pi }}\lambda \right)}$ (that is even a subset of ${\displaystyle L^{4}}$), where ${\displaystyle \lambda }$ is Lebesgue measure on the unit circle ${\displaystyle S^{1}}$ and ${\displaystyle {\tfrac {1}{2\pi }}\lambda }$ is the probability measure that results from dividing it by its mass ${\displaystyle \lambda (S^{1})=2\pi .}$[11]

## Lp (0 < p < 1)

Let ${\displaystyle (S,\Sigma ,\mu )}$ be a measure space. If ${\displaystyle 0 then ${\displaystyle L^{p}(\mu )}$ can be defined as above: it is the quotient vector space of those measurable functions ${\displaystyle f}$ such that

${\displaystyle N_{p}(f)=\int _{S}|f|^{p}\,d\mu <\infty .}$

As before, we may introduce the ${\displaystyle p}$-norm ${\displaystyle \|f\|_{p}=N_{p}(f)^{1/p},}$ but ${\displaystyle \|\cdot \|_{p}}$ does not satisfy the triangle inequality in this case, and defines only a quasi-norm. The inequality ${\displaystyle (a+b)^{p}\leq a^{p}+b^{p},}$ valid for ${\displaystyle a,b\geq 0,}$ implies that (Rudin 1991, §1.47)

${\displaystyle N_{p}(f+g)\leq N_{p}(f)+N_{p}(g)}$
and so the function
${\displaystyle d_{p}(f,g)=N_{p}(f-g)=\|f-g\|_{p}^{p}}$
is a metric on ${\displaystyle L^{p}(\mu ).}$ The resulting metric space is complete;[12] the verification is similar to the familiar case when ${\displaystyle p\geq 1.}$ The balls
${\displaystyle B_{r}=\{f\in L^{p}:N_{p}(f)
form a local base at the origin for this topology, as ${\displaystyle r>0}$ ranges over the positive reals.[12] These balls satisfy ${\displaystyle B_{r}=r^{1/p}B_{1}}$ for all real ${\displaystyle r>0,}$ which in particular shows that ${\displaystyle B_{1}}$ is a bounded neighborhood of the origin;[12] in other words, this space is locally bounded, just like every normed space, despite ${\displaystyle \|\cdot \|_{p}}$ not being a norm.

In this setting ${\displaystyle L^{p}}$ satisfies a reverse Minkowski inequality, that is for ${\displaystyle u,v\in L^{p}}$

${\displaystyle {\Big \|}|u|+|v|{\Big \|}_{p}\geq \|u\|_{p}+\|v\|_{p}}$

This result may be used to prove Clarkson's inequalities, which are in turn used to establish the uniform convexity of the spaces ${\displaystyle L^{p}}$ for ${\displaystyle 1 (Adams & Fournier 2003).

The space ${\displaystyle L^{p}}$ for ${\displaystyle 0 is an F-space: it admits a complete translation-invariant metric with respect to which the vector space operations are continuous. It is the prototypical example of an F-space that, for most reasonable measure spaces, is not locally convex: in ${\displaystyle \ell ^{p}}$ or ${\displaystyle L^{p}([0,1]),}$ every open convex set containing the ${\displaystyle 0}$ function is unbounded for the ${\displaystyle p}$-quasi-norm; therefore, the ${\displaystyle 0}$ vector does not possess a fundamental system of convex neighborhoods. Specifically, this is true if the measure space ${\displaystyle S}$ contains an infinite family of disjoint measurable sets of finite positive measure.

The only nonempty convex open set in ${\displaystyle L^{p}([0,1])}$ is the entire space (Rudin 1991, §1.47). As a particular consequence, there are no nonzero continuous linear functionals on ${\displaystyle L^{p}([0,1]);}$ the continuous dual space is the zero space. In the case of the counting measure on the natural numbers (producing the sequence space ${\displaystyle L^{p}(\mu )=\ell ^{p}}$), the bounded linear functionals on ${\displaystyle \ell ^{p}}$ are exactly those that are bounded on ${\displaystyle \ell ^{1},}$ namely those given by sequences in ${\displaystyle \ell ^{\infty }.}$ Although ${\displaystyle \ell ^{p}}$ 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 ${\displaystyle \mathbb {R} ^{n},}$ rather than work with ${\displaystyle L^{p}}$ for ${\displaystyle 0 it is common to work with the Hardy space Hp 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 Hp for ${\displaystyle p<1}$ (Duren 1970, §7.5).

### L0, the space of measurable functions

The vector space of (equivalence classes of) measurable functions on ${\displaystyle (S,\Sigma ,\mu )}$ is denoted ${\displaystyle L^{0}(S,\Sigma ,\mu )}$ (Kalton, Peck & Roberts 1984). By definition, it contains all the ${\displaystyle L^{p},}$ and is equipped with the topology of convergence in measure. When ${\displaystyle \mu }$ is a probability measure (i.e., ${\displaystyle \mu (S)=1}$), this mode of convergence is named convergence in probability.

The description is easier when ${\displaystyle \mu }$ is finite. If ${\displaystyle \mu }$ is a finite measure on ${\displaystyle (S,\Sigma ),}$ the ${\displaystyle 0}$ function admits for the convergence in measure the following fundamental system of neighborhoods

${\displaystyle V_{\varepsilon }={\Bigl \{}f:\mu {\bigl (}\{x:|f(x)|>\varepsilon \}{\bigr )}<\varepsilon {\Bigr \}},\qquad \varepsilon >0.}$

The topology can be defined by any metric ${\displaystyle d}$ of the form

${\displaystyle d(f,g)=\int _{S}\varphi {\bigl (}|f(x)-g(x)|{\bigr )}\,\mathrm {d} \mu (x)}$
where ${\displaystyle \varphi }$ is bounded continuous concave and non-decreasing on ${\displaystyle [0,\infty ),}$ with ${\displaystyle \varphi (0)=0}$ and ${\displaystyle \varphi (t)>0}$ when ${\displaystyle t>0}$ (for example, ${\displaystyle \varphi (t)=\min(t,1).}$ Such a metric is called Lévy-metric for ${\displaystyle L^{0}.}$ Under this metric the space ${\displaystyle L^{0}}$ is complete (it is again an F-space). The space ${\displaystyle L^{0}}$ is in general not locally bounded, and not locally convex.

For the infinite Lebesgue measure ${\displaystyle \lambda }$ on ${\displaystyle \mathbb {R} ^{n},}$ the definition of the fundamental system of neighborhoods could be modified as follows

${\displaystyle W_{\varepsilon }=\left\{f:\lambda \left(\left\{x:|f(x)|>\varepsilon {\text{ and }}|x|<{\tfrac {1}{\varepsilon }}\right\}\right)<\varepsilon \right\}}$

The resulting space ${\displaystyle L^{0}(\mathbb {R} ^{n},\lambda )}$ coincides as topological vector space with ${\displaystyle L^{0}(\mathbb {R} ^{n},g(x)\,\mathrm {d} \lambda (x)),}$ for any positive ${\displaystyle \lambda }$–integrable density ${\displaystyle g.}$

## Generalizations and extensions

### Weak Lp

Let ${\displaystyle (S,\Sigma ,\mu )}$ be a measure space, and ${\displaystyle f}$ a measurable function with real or complex values on ${\displaystyle S.}$ The distribution function of ${\displaystyle f}$ is defined for ${\displaystyle t\geq 0}$ by

${\displaystyle \lambda _{f}(t)=\mu \{x\in S:|f(x)|>t\}.}$

If ${\displaystyle f}$ is in ${\displaystyle L^{p}(S,\mu )}$ for some ${\displaystyle p}$ with ${\displaystyle 1\leq p<\infty ,}$ then by Markov's inequality,

${\displaystyle \lambda _{f}(t)\leq {\frac {\|f\|_{p}^{p}}{t^{p}}}}$

A function ${\displaystyle f}$ is said to be in the space weak ${\displaystyle L^{p}(S,\mu )}$, or ${\displaystyle L^{p,w}(S,\mu ),}$ if there is a constant ${\displaystyle C>0}$ such that, for all ${\displaystyle T>0,}$

${\displaystyle \lambda _{f}(t)\leq {\frac {C^{p}}{t^{p}}}}$

The best constant ${\displaystyle C}$ for this inequality is the ${\displaystyle L^{p,w}}$-norm of ${\displaystyle f,}$ and is denoted by

${\displaystyle \|f\|_{p,w}=\sup _{t>0}~t\lambda _{f}^{1/p}(t).}$

The weak ${\displaystyle L^{p}}$ coincide with the Lorentz spaces ${\displaystyle L^{p,\infty },}$ so this notation is also used to denote them.

The ${\displaystyle L^{p,w}}$-norm is not a true norm, since the triangle inequality fails to hold. Nevertheless, for ${\displaystyle f}$ in ${\displaystyle L^{p}(S,\mu ),}$

${\displaystyle \|f\|_{p,w}\leq \|f\|_{p}}$
and in particular ${\displaystyle L^{p}(S,\mu )\subset L^{p,w}(S,\mu ).}$

In fact, one has

${\displaystyle \|f\|_{L^{p}}^{p}=\int |f(x)|^{p}d\mu (x)\geq \int _{\{|f(x)|>t\}}t^{p}+\int _{\{|f(x)|\leq t\}}|f|^{p}\geq t^{p}\mu (\{|f|>t\}),}$
and raising to power ${\displaystyle 1/p}$ and taking the supremum in ${\displaystyle t}$ one has
${\displaystyle \|f\|_{L^{p}}\geq \sup _{t>0}t\;\mu (\{|f|>t\})^{1/p}=\|f\|_{L^{p,w}}.}$

Under the convention that two functions are equal if they are equal ${\displaystyle \mu }$ almost everywhere, then the spaces ${\displaystyle L^{p,w}}$ are complete (Grafakos 2004).

For any ${\displaystyle 0 the expression

${\displaystyle \||f|\|_{L^{p,\infty }}=\sup _{0<\mu (E)<\infty }\mu (E)^{-1/r+1/p}\left(\int _{E}|f|^{r}\,d\mu \right)^{1/r}}$
is comparable to the ${\displaystyle L^{p,w}}$-norm. Further in the case ${\displaystyle p>1,}$ this expression defines a norm if ${\displaystyle r=1.}$ Hence for ${\displaystyle p>1}$ the weak ${\displaystyle L^{p}}$ spaces are Banach spaces (Grafakos 2004).

A major result that uses the ${\displaystyle L^{p,w}}$-spaces is the Marcinkiewicz interpolation theorem, which has broad applications to harmonic analysis and the study of singular integrals.

### Weighted Lp spaces

As before, consider a measure space ${\displaystyle (S,\Sigma ,\mu ).}$ Let ${\displaystyle w:S\to [a,\infty ),a>0}$ be a measurable function. The ${\displaystyle w}$-weighted ${\displaystyle L^{p}}$ space is defined as ${\displaystyle L^{p}(S,w\,\mathrm {d} \mu ),}$ where ${\displaystyle w\,\mathrm {d} \mu }$ means the measure ${\displaystyle \nu }$ defined by

${\displaystyle \nu (A)\equiv \int _{A}w(x)\,\mathrm {d} \mu (x),\qquad A\in \Sigma ,}$

or, in terms of the Radon–Nikodym derivative, ${\displaystyle w={\tfrac {\mathrm {d} \nu }{\mathrm {d} \mu }}}$ the norm for ${\displaystyle L^{p}(S,w\,\mathrm {d} \mu )}$ is explicitly

${\displaystyle \|u\|_{L^{p}(S,w\,\mathrm {d} \mu )}\equiv \left(\int _{S}w(x)|u(x)|^{p}\,\mathrm {d} \mu (x)\right)^{1/p}}$

As ${\displaystyle L^{p}}$-spaces, the weighted spaces have nothing special, since ${\displaystyle L^{p}(S,w\,\mathrm {d} \mu )}$ is equal to ${\displaystyle L^{p}(S,\mathrm {d} \nu ).}$ But they are the natural framework for several results in harmonic analysis (Grafakos 2004); they appear for example in the Muckenhoupt theorem: for ${\displaystyle 1 the classical Hilbert transform is defined on ${\displaystyle L^{p}(\mathbf {T} ,\lambda )}$ where ${\displaystyle \mathbf {T} }$ denotes the unit circle and ${\displaystyle \lambda }$ the Lebesgue measure; the (nonlinear) Hardy–Littlewood maximal operator is bounded on ${\displaystyle L^{p}(\mathbb {R} ^{n},\lambda ).}$ Muckenhoupt's theorem describes weights ${\displaystyle w}$ such that the Hilbert transform remains bounded on ${\displaystyle L^{p}(\mathbf {T} ,w\,\mathrm {d} \lambda )}$ and the maximal operator on ${\displaystyle L^{p}(\mathbb {R} ^{n},w\,\mathrm {d} \lambda ).}$

### Lp spaces on manifolds

One may also define spaces ${\displaystyle L^{p}(M)}$ on a manifold, called the intrinsic ${\displaystyle L^{p}}$ spaces of the manifold, using densities.

### Vector-valued Lp spaces

Given a measure space ${\displaystyle (\Omega ,\Sigma ,\mu )}$ and a locally convex space ${\displaystyle E}$ (here assumed to be complete), it is possible to define spaces of ${\displaystyle p}$-integrable ${\displaystyle E}$-valued functions on ${\displaystyle \Omega }$ in a number of ways. One way is to define the spaces of Bochner integrable and Pettis integrable functions, and then endow them with locally convex TVS-topologies that are (each in their own way) a natural generalization of the usual ${\displaystyle L^{p}}$ topology. Another way involves topological tensor products of ${\displaystyle L^{p}(\Omega ,\Sigma ,\mu )}$ with ${\displaystyle E.}$ Element of the vector space ${\displaystyle L^{p}(\Omega ,\Sigma ,\mu )\otimes E}$ are finite sums of simple tensors ${\displaystyle f_{1}\otimes e_{1}+\cdots +f_{n}\otimes e_{n},}$ where each simple tensor ${\displaystyle f\times e}$ may be identified with the function ${\displaystyle \Omega \to E}$ that sends ${\displaystyle x\mapsto ef(x).}$ This tensor product ${\displaystyle L^{p}(\Omega ,\Sigma ,\mu )\otimes E}$ is then endowed with a locally convex topology that turns it into a topological tensor product, the most common of which are the projective tensor product, denoted by ${\displaystyle L^{p}(\Omega ,\Sigma ,\mu )\otimes _{\pi }E,}$ and the injective tensor product, denoted by ${\displaystyle L^{p}(\Omega ,\Sigma ,\mu )\otimes _{\varepsilon }E.}$ In general, neither of these space are complete so their completions are constructed, which are respectively denoted by ${\displaystyle L^{p}(\Omega ,\Sigma ,\mu ){\widehat {\otimes }}_{\pi }E}$ and ${\displaystyle L^{p}(\Omega ,\Sigma ,\mu ){\widehat {\otimes }}_{\varepsilon }E}$ (this is analogous to how the space of scalar-valued simple functions on ${\displaystyle \Omega ,}$ when seminormed by any ${\displaystyle \|\cdot \|_{p},}$ is not complete so a completion is constructed which, after being quotiented by ${\displaystyle \ker \|\cdot \|_{p},}$ is isometrically isomorphic to the Banach space ${\displaystyle L^{p}(\Omega ,\mu )}$). Alexander Grothendieck showed that when ${\displaystyle E}$ is a nuclear space (a concept he introduced), then these two constructions are, respectively, canonically TVS-isomorphic with the spaces of Bochner and Pettis integral functions mentioned earlier; in short, they are indistinguishable.

## Notes

1. ^ Rolewicz, Stefan (1987), Functional analysis and control theory: Linear systems, Mathematics and its Applications (East European Series), vol. 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[page needed]
2. ^ Maddox, I. J. (1988), Elements of Functional Analysis (2nd ed.), Cambridge: CUP, page 16
3. ^ Rafael Dahmen, Gábor Lukács: Long colimits of topological groups I: Continuous maps and homeomorphisms. in: Topology and its Applications Nr. 270, 2020. Example 2.14
4. ^ Garling, D. J. H. (2007). Inequalities: A Journey into Linear Analysis. Cambridge University Press. p. 54. ISBN 978-0-521-87624-7.
5. ^ a b Bahouri, Chemin & Danchin 2011, pp. 1–4.
6. ^ Bahouri, Chemin & Danchin 2011, p. 4.
7. Bahouri, Chemin & Danchin 2011, pp. 7–8.
8. ^ Rudin, Walter (1980), Real and Complex Analysis (2nd ed.), New Delhi: Tata McGraw-Hill, ISBN 9780070542341, Theorem 6.16
9. ^ Schechter, Eric (1997), Handbook of Analysis and its Foundations, London: Academic Press Inc. See Sections 14.77 and 27.44–47
10. ^ Villani, Alfonso (1985), "Another note on the inclusion Lp(μ) ⊂ Lq(μ)", Amer. Math. Monthly, 92 (7): 485–487, doi:10.2307/2322503, JSTOR 2322503, MR 0801221
11. ^ a b c Rudin 1991, pp. 117–119.
12. ^ a b c Rudin 1991, p. 37.
1. ^ The condition ${\displaystyle \sup \operatorname {range} |x|<+\infty .}$ is not equivalent to ${\displaystyle \sup \operatorname {range} |x|}$ being finite, unless ${\displaystyle X\neq \varnothing .}$
2. ^ If ${\displaystyle X=\varnothing }$ then ${\displaystyle \sup \operatorname {range} |x|=-\infty .}$
3. ^ If ${\displaystyle \mu (S)=0}$ then ${\displaystyle \operatorname {esssup} |f|=-\infty .}$
4. ^ a b For example, if a non-empty measurable set ${\displaystyle N\neq \varnothing }$ of measure ${\displaystyle \mu (N)=0}$ exists then its indicator function ${\displaystyle \mathbf {1} _{N}}$ satisfies ${\displaystyle \|\mathbf {1} _{N}\|_{p}=0}$ although ${\displaystyle \mathbf {1} _{N}\neq 0.}$
5. ^ Explicitly, the vector space operations are defined by:
{\displaystyle {\begin{aligned}(f+g)(x)&=f(x)+g(x),\\(sf)(x)&=sf(x)\end{aligned}}}
for all ${\displaystyle f,g\in {\mathcal {L}}^{p}(S,\,\mu )}$ and all scalars ${\displaystyle s.}$ These operations make ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu )}$ into a vector space because if ${\displaystyle s}$ is any scalar and ${\displaystyle f,g\in {\mathcal {L}}^{p}(S,\,\mu )}$ then both ${\displaystyle sf}$ and ${\displaystyle f+g}$ also belong to ${\displaystyle {\mathcal {L}}^{p}(S,\,\mu ).}$
1. ^ When ${\displaystyle 1\leq p<\infty ,}$ the inequality ${\displaystyle \|f+g\|_{p}^{p}\leq 2^{p-1}\left(\|f\|_{p}^{p}+\|g\|_{p}^{p}\right)}$ can be deduced from the fact that the function ${\displaystyle F:[0,\infty )\to \mathbb {R} }$ defined by ${\displaystyle F(t)=t^{p}}$ is convex, which by definition means that ${\displaystyle F(tx+(1-t)y)\leq tF(x)+(1-t)F(y)}$ for all ${\displaystyle 0\leq t\leq 1}$ and all ${\displaystyle x,y}$ in the domain of ${\displaystyle F.}$ Substituting ${\displaystyle |f|,|g|,}$ and ${\displaystyle {\tfrac {1}{2}}}$ in for ${\displaystyle x,y,}$ and ${\displaystyle t}$ gives ${\displaystyle \left({\tfrac {1}{2}}|f|+{\tfrac {1}{2}}|g|\right)^{p}\leq {\tfrac {1}{2}}|f|^{p}+{\tfrac {1}{2}}|g|^{p},}$ which proves that ${\displaystyle (|f|+|g|)^{p}\leq 2^{p-1}(|f|^{p}+|g|^{p}).}$ The triangle inequality ${\displaystyle |f+g|\leq |f|+|g|}$ now implies ${\displaystyle |f+g|^{p}\leq 2^{p-1}(|f|^{p}+|g|^{p}).}$ The desired inequality follows by integrating both sides. ${\displaystyle \blacksquare }$

## References

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