# Lp space

(Redirected from Lp norm)

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. Lebesgue spaces have applications in physics, statistics, finance, engineering, and other disciplines.

## The p-norm in finite dimensions

Illustrations of unit circles in different 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 p).
Unit circle (superellipse) in p = 32 norm

The length of a vector x = (x1, x2, …, xn) in the n-dimensional real vector space Rn is usually given by the Euclidean norm:

$\ \|x\|_2=\left(x_1^2+x_2^2+\dotsb+x_n^2\right)^{\frac{1}{2}}$

The Euclidean distance between two points x and y is the length $\scriptstyle \|x \,-\, y\|_2$ 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. For example, taxi drivers in Manhattan should measure distance not in terms of the length of the straight line to their destination, but in terms of the Manhattan distance, which takes into account that streets are either orthogonal or parallel to each other. The class of 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 p ≥ 1, the p-norm or Lp-norm of x is defined by

$\ \|x\|_p=\left(|x_1|^p+|x_2|^p+\dotsb+|x_n|^p\right)^{\frac{1}{p}}$

The Euclidean norm from above falls into this class and is the 2-norm, and the 1-norm is the norm that corresponds to the Manhattan distance.

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

$\ \|x\|_\infty=\max \left\{|x_1|, |x_2|, \dotsc, |x_n|\right\}$

For all p ≥ 1, the 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, 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 Rn together with the p-norm is a Banach space. This Banach space is the Lp-space over Rn.

#### Relations between p-norms

It is intuitively clear that straight-line distances in Manhattan are generally shorter than taxi distances. Formally, this means that the Euclidean norm of any vector is bounded by its 1-norm:

$\|x\|_2 \leq \|x\|_1$

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

$\|x\|_{p+a} \leq \|x\|_{p}$ for any vector x and real numbers p ≥ 1 and a ≥ 0.

For the opposite direction, the following relation between the 1-norm and the 2-norm is known:

$\|x\|_1 \leq \sqrt{n}\|x\|_2$

This inequality depends on the dimension n of the underlying vector space and follows directly from the Cauchy–Schwarz inequality.

In general, for vectors in $\mathbb{C}^n$ where p > r > 0:

$\|x\|_p\leq\|x\|_r\leq n^{(1/r-1/p)}\|x\|_p$

### When 0 < p < 1

Astroid, unit circle in p = 23 metric

In Rn for n > 1, the formula

$\ \|x\|_p=\left(|x_1|^p+|x_2|^p+\dotsb+|x_n|^p\right)^{\frac{1}{p}}$

defines an absolutely homogeneous function for 0 < p < 1; however, the resulting function does not define an F-norm, because it is not subadditive. In Rn for n > 1, the formula for 0 < p < 1

$\|x\|_p = \left(|x_1|^p + |x_2|^p + \dotsb + |x_n|^p\right)$

defines a subadditive function, which does define an F-norm. This F-norm is not homogeneous.

However, the function

$d_p(x,y) = \sum_{i=1}^n |x_i-y_i|^p$

defines a metric. The metric space (Rn, dp) is denoted by ℓnp.

Although the p-unit ball Bnp around the origin in this metric is "concave", the topology defined on Rn by the metric dp is the usual vector space topology of Rn, hence ℓnp is a locally convex topological vector space. Beyond this qualitative statement, a quantitative way to measure the lack of convexity of ℓnp is to denote by Cp(n) the smallest constant C such that the multiple C Bnp of the p-unit ball contains the convex hull of Bnp, equal to Bn1. The fact that Cp(n) = n1/p – 1 tends to infinity with n (for fixed p < 1) reflects the fact that the infinite-dimensional sequence space ℓp defined below, is no longer locally convex.

### When p = 0

There is one l0 norm and another function called the l0 "norm" (with scare quotation marks).

The mathematical definition of the l0 norm was established by Banach's Theory of Linear Operations. The space of sequences has a complete metric topology provided by the F-norm $\scriptstyle (x_n) \,\mapsto\, \sum_n{2^{-n} |x_n|/(1 \,+\, |x_n| )}$, which is discussed by Stefan Rolewicz in Metric Linear Spaces.[1] The l0-normed space is studied in functional analysis, probability theory, and harmonic analysis.

Another function was called the l0 "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 x. Many authors abuse terminology by omitting the quotation marks. Defining 00 = 0, the zero "norm" of x is equal to $\scriptstyle |x_1|^0 \,+\, |x_2|^0 \,+\, \dotsb \,+\, |x_n|^0$. This is not a norm (B-norm, with "B" for Banach) because it is not homogeneous. 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.

## The p-norm in countably infinite dimensions

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

• $\scriptstyle \ell^1$, the space of sequences whose series is absolutely convergent,
• $\scriptstyle \ell^2$, the space of square-summable sequences, which is a Hilbert space, and
• $\scriptstyle \ell^\infty$, 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, for $\scriptstyle \ x \;=\; (x_1,\, x_2,\, \dotsc,\, x_n,\, x_{n+1},\, \dotsc)$ an infinite sequence of real (or complex) numbers, define the vector sum to be

\begin{align} &(x_1, x_2, \dotsc, x_n, x_{n+1},\dotsc)+(y_1, y_2, \dotsc, y_n, y_{n+1},\dotsc) =\\ &(x_1+y_1, x_2+y_2, \dotsc, x_n+y_n, x_{n+1}+y_{n+1},\dotsc) \end{align}

while the scalar action is given by

$\lambda(x_1, x_2, \dotsc, x_n, x_{n+1},\dotsc) = (\lambda x_1, \lambda x_2, \dotsc, \lambda x_n, \lambda x_{n+1},\dotsc)$

Define the p-norm

$\|x\|_p = \left(|x_1|^p + |x_2|^p + \dotsb+|x_n|^p + |x_{n+1}|^p + \dotsb\right)^{\frac{1}{p}}$

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, (1, 1, 1, …), will have an infinite p-norm (length) for every finite p ≥ 1. The space ℓp is then defined as the set of all infinite sequences of real (or complex) numbers such that the p-norm is finite.

One can check that as p increases, the set ℓp grows larger. For example, the sequence

$\left(1, \frac{1}{2}, \dotsc, \frac{1}{n}, \frac{1}{n+1},\dotsc\right)$

is not in ℓ1, but it is in ℓp for p > 1, as the series

$1^p + \frac{1}{2^p} + \dotsb + \frac{1}{n^p} + \frac{1}{(n+1)^p}+\dotsb$

diverges for p = 1 (the harmonic series), but is convergent for p > 1.

One also defines the ∞-norm using the supremum:

$\ \|x\|_\infty=\sup(|x_1|, |x_2|, \dotsc, |x_n|,|x_{n+1}|, \dotsc)$

and the corresponding space ℓ of all bounded sequences. It turns out that[2]

$\ \|x\|_\infty = \lim_{p\to\infty}\|x\|_p$

if the right-hand side is finite, or the left-hand side is infinite. Thus, we will consider ℓp spaces for 1 ≤ p ≤ ∞.

The p-norm thus defined on ℓp is indeed a norm, and ℓp together with this norm is a Banach space. The fully general Lp 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 p-norm.

## Lp spaces

Let 1 ≤ p < ∞ and (S, Σ, μ) be a measure space. Consider the set of all measurable functions from S to C (or R) whose absolute value raised to the p-th power has finite integral, or equivalently, that

$\|f\|_p \equiv \left({\int_S |f|^p\;\mathrm{d}\mu}\right)^{\frac{1}{p}}<\infty$

The set of such functions forms a vector space, with the following natural operations:

$(f+g)(x) = f(x)+g(x), \ \ \ \text{and} \ \ \ (\lambda f)(x) = \lambda f(x) \,$

for every scalar λ.

That the sum of two pth power integrable functions is again pth power integrable follows from the inequality |f + g|p ≤ 2p-1 (|f|p + |g|p). In fact, more is true. Minkowski's inequality says the triangle inequality holds for || · ||p. Thus the set of pth power integrable functions, together with the function || · ||p, is a seminormed vector space, which is denoted by $\scriptstyle \mathcal{L}^p(S,\, \mu)$.

This can be made into a normed vector space in a standard way; one simply takes the quotient space with respect to the kernel of || · ||p. Since for any measurable function f, we have that ||f||p = 0 if and only if f = 0 almost everywhere, the kernel of || · ||p does not depend upon p,

$N \equiv \mathrm{ker}(\|\cdot\|_p) = \{f : f = 0 \ \mu\text{-almost everywhere} \}$

In the quotient space, two functions f and g are identified if f = g almost everywhere. The resulting normed vector space is, by definition,

$L^p(S, \mu) \equiv \mathcal{L}^p(S, \mu) / N$

For p = ∞, the space L(S, μ) is defined as follows. We start with the set of all measurable functions from S to C (or R) which are essentially bounded, i.e. bounded up to a set of measure zero. Again two such functions are identified if they are equal almost everywhere. Denote this set by L(S, μ). For f in L(S, μ), its essential supremum serves as an appropriate norm:

$\|f\|_\infty \equiv \inf \{ C\ge 0 : |f(x)| \le C \mbox{ for almost every } x\}.$

As before, we have

$\|f\|_\infty=\lim_{p\to\infty}\|f\|_p$

if fL(S, μ) ∩ Lq(S, μ) for some q < ∞.

For 1 ≤ p ≤ ∞, Lp(S, μ) is a Banach space. The fact that Lp is complete is often referred to as Riesz-Fischer theorem. Completeness can be checked using the convergence theorems for Lebesgue integrals.

When the underlying measure space S is understood, Lp(S, μ) is often abbreviated Lp(μ), or just Lp. The above definitions generalize to Bochner spaces.

### Special cases

When p = 2; like the ℓ2 space, the space L2 is the only Hilbert space of this class. In the complex case, the inner product on L2 is defined by

$\langle f, g \rangle = \int_S f(x) \overline{g(x)} \, \mathrm{d}\mu(x)$

The additional inner product structure allows for a richer theory, with applications to, for instance, Fourier series and quantum mechanics. Functions in L2 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 (Titchmarsh 1976).

If we use complex-valued functions, the space L 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 L defines a bounded operator on any Lp space by multiplication.

The ℓp spaces (1 ≤ p ≤ ∞) are a special case of Lp spaces, when S is the set N of positive integers, and the measure μ is the counting measure on N. More generally, if one considers any set S with the counting measure, the resulting L p space is denoted ℓp(S). For example, the space ℓp(Z) is the space of all sequences indexed by the integers, and when defining the p-norm on such a space, one sums over all the integers. The space ℓp(n), where n is the set with n elements, is Rn with its p-norm as defined above. As any Hilbert space, every space L2 is linearly isometric to a suitable ℓ2(I), where the cardinality of the set I is the cardinality of an arbitrary Hilbertian basis for this particular L2.

## Properties of Lp spaces

### Dual spaces

The dual space (the space of all continuous linear functionals) of Lp(μ) for 1 < p < ∞ has a natural isomorphism with Lq(μ), where q  is such that 1/p + 1/q = 1, which associates g ∈ Lq(μ) with the functional κp(g) ∈ Lp(μ) defined by

$\kappa_p(g) \colon f \in L^p(\mu) \mapsto \int f g \, \mathrm{d}\mu$

The fact that κp(g) is well defined and continuous follows from Hölder's inequality. The mapping κp is a linear mapping from Lq(μ) into Lp(μ), 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[3]) that any G ∈ Lp(μ) can be expressed this way: i.e., that κp is onto. Since κp is onto and isometric, it is an isomorphism of Banach spaces. With this (isometric) isomorphism in mind, it is usual to say simply that Lq  "is"  the dual of Lp.

When 1 < p < ∞, the space Lp(μ) is reflexive. Let κp be the above map and let κq be the corresponding linear isometry from Lp(μ) onto Lq(μ). The map

$j_p \colon L^p(\mu) \overset{\kappa_q}{\to} L^q(\mu)^* \overset{\,\,\left(\kappa_p^{-1}\right)^*}{\longrightarrow} L^p(\mu)^{**}$

from Lp(μ) to Lp(μ)∗∗, obtained by composing κq with the transpose (or adjoint) of the inverse of κp, coincides with the canonical embedding J  of Lp(μ) into its bidual. Moreover, the map jp is onto, as composition of two onto isometries, and this proves reflexivity.

If the measure μ on S is sigma-finite, then the dual of L1(μ) is isometrically isomorphic to L(μ) (more precisely, the map κ1 corresponding to p = 1 is an isometry from L(μ) onto L1(μ)).

The dual of L is subtler. Elements of (L(μ)) can be identified with bounded signed finitely additive measures on S that are absolutely continuous with respect to μ. See ba space for more details. If we assume the axiom of choice, this space is much bigger than L1(μ) except in some trivial cases. However, there are relatively consistent extensions of Zermelo-Fraenkel set theory in which the dual of is 1. This is a result of Shelah, discussed in Eric Schechter's book Handbook of Analysis and its Foundations.

### Embeddings

Colloquially, if 1 ≤ p < q ≤ ∞, Lp(Sμ) contains functions that are more locally singular, while elements of Lq(Sμ) can be more spread out. Consider the Lebesgue measure on the half line (0, ∞). A continuous function in L1 might blow up near 0 but must decay sufficiently fast toward infinity. On the other hand, continuous functions in L need not decay at all but no blow-up is allowed. The precise technical result is the following:

1. Let 0 ≤ p < q ≤ ∞. Lq(Sμ) is contained in Lp(S, μ) iff S does not contain sets of arbitrarily large measure, and
2. Let 0 ≤ p < q ≤ ∞. Lp(Sμ) is contained in Lq(Sμ) iff S does not contain sets of arbitrarily small non-zero measure.

In particular, if the domain S has finite measure, the bound (a consequence of Jensen's inequality)

$\ \|f\|_p \le \mu(S)^{\frac{1}{p} - \frac{1}{q}} \|f\|_q$

means the space Lq is continuously embedded in Lp. That is to say, the identity operator is a bounded linear map from Lq to Lp. The constant appearing in the above inequality is optimal, in the sense that the operator norm of the identity I : Lq(Sμ) → Lp(Sμ) is precisely

$\|I\|_{q,p} = \mu(S)^{\frac{1}{p} - \frac{1}{q}}$

the case of equality being achieved exactly when f = 1 a.e.[μ].

### Dense subspaces

It is assumed that 1 ≤ p < ∞ throughout this section.
Let (SΣμ) be a measure space. An integrable simple function f  on S  is one of the form

$f = \sum_{j=1}^n a_j \mathbf{1}_{A_j}$

where aj is scalar and Aj ∈ Σ  has finite measure, for j = 1, …, n. By construction of the integral, the vector space of integrable simple functions is dense in Lp(SΣμ).

More can be said when S  is a metrizable topological space and Σ  its Borel σ–algebra, i.e., the smallest σ–algebra of subsets of S  containing the open sets.

Suppose that V ⊂ S  is an open set with μ(V) < ∞. It can be proved that for every Borel set A ∈ Σ  contained in V, and for every ε > 0, there exist a closed set F  and an open set U  such that

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

It follows that there exists φ continuous on S  such that

$0 \le \varphi \le \mathbf{1}_V \ \text{and} \ \int_S |\mathbf{1}_A - \varphi| \, \mathrm{d}\mu < \varepsilon$

If S  can be covered by an increasing sequence (Vn) of open sets that have finite measure, then the space of p–integrable continuous functions is dense in Lp(SΣμ). More precisely, one can use bounded continuous functions that vanish outside one of the open sets Vn.

This applies in particular when S = Rd and when μ is the Lebesgue measure. The space of continuous and compactly supported functions is dense in Lp(Rd). Similarly, the space of integrable step functions  is dense in Lp(Rd); this space is the linear span of indicator functions of bounded intervals when d = 1, of bounded rectangles when d = 2 and more generally of products of bounded intervals.
Several properties of general functions in Lp(Rd) 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 Lp(Rd), in the following sense: for every f ∈ Lp(Rd),

$\|\tau_t f - f \|_p \rightarrow 0$

when t ∈ Rd tends to 0, where $\scriptstyle \tau_t f$ is the translated function defined by $\scriptstyle (\tau_t f)(x) \;=\; f(x \,-\, t)$.

## Applications

Lp spaces are widely used in mathematics and applications.

### Hausdorff–Young inequality

The Fourier transform for the real line (resp. for periodic functions, cf. Fourier series) maps Lp(R) to Lq(R) (resp. Lp(T) to ℓq), where 1 ≤ p ≤ 2 and 1/p + 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 p > 2, the Fourier transform does not map into Lq.

### Hilbert spaces

Hilbert spaces are central to many applications, from quantum mechanics to stochastic calculus. The spaces L2 and ℓ2 are both Hilbert spaces. In fact, by choosing a Hilbert basis, one sees that all Hilbert spaces are isometric to ℓ2(E), where E is a set with an appropriate cardinality.

### Statistics

In statistics, measures of central tendency and statistical dispersion, such as the mean, median, and standard deviation, are defined in terms of Lp metrics, and measures of central tendency can be characterized as solutions to variational problems.

## Lp for 0 < p < 1

Let (S, Σ, μ) be a measure space. If 0 < p < 1, then Lp(μ) can be defined as above: it is the vector space of those measurable functions f such that

$N_p(f) = \int_S |f|^p\, d\mu < \infty$.

As before, we may introduce the p-norm || f ||p = Np(f)1/p, but || · ||p does not satisfy the triangle inequality in this case, and defines only a quasi-norm. The inequality (a + b)p ≤ ap + bp, valid for a ≥ 0 and b ≥ 0 implies that (Rudin 1991, §1.47)

$N_p(f+g)\le N_p(f) + N_p(g)$

and so the function

$d_p(f,g) = N_p(f-g) = \|f - g\|_p^p$

is a metric on Lp(μ). The resulting metric space is complete; the verification is similar to the familiar case when p ≥ 1.

In this setting Lp satisfies a reverse Minkowski inequality, that is for u and v in Lp

$\|\,|u|+|v|\,\|_p\geq \|u\|_p+\|v\|_p$

This result may be used to prove Clarkson's inequalities, which are in turn used to establish the uniform convexity of the spaces Lp for 1 < p < ∞ (Adams & Fournier 2003).

The space Lp for 0 < p < 1 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 p ≥ 1. It is the prototypical example of an F-space that, for most reasonable measure spaces, is not locally convex: in ℓp or Lp([0, 1]), every open convex set containing the 0 function is unbounded for the p-quasi-norm; therefore, the 0 vector does not possess a fundamental system of convex neighborhoods. Specifically, this is true if the measure space S contains an infinite family of disjoint measurable sets of finite positive measure.

The only nonempty convex open set in Lp([0, 1]) is the entire space (Rudin 1991, §1.47). As a particular consequence, there are no nonzero linear functionals on Lp([0, 1]): the dual space is the zero space. In the case of the counting measure on the natural numbers (producing the sequence space Lp(μ) = ℓp), the bounded linear functionals on ℓp are exactly those that are bounded on ℓ1, namely those given by sequences in ℓ. Although ℓp 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 Rn, rather than work with Lp for 0 < p < 1, 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 p < 1 (Duren 1970, §7.5).

### L0, the space of measurable functions

The vector space of (equivalence classes of) measurable functions on (S, Σ, μ) is denoted L0(S, Σ, μ) (Kalton, Peck & Roberts 1984). By definition, it contains all the Lp, and is equipped with the topology of convergence in measure. When μ is a probability measure (i.e., μ(S) = 1), this mode of convergence is named convergence in probability. The description is easier when μ is finite.

If μ is a finite measure on (SΣ), the 0 function admits for the convergence in measure the following fundamental system of neighborhoods

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

The topology can be defined by any metric d  of the form

$d(f, g) = \int_S \varphi \bigl( |f(x) - g(x)| \bigr) \, \mathrm{d}\mu(x)$

where φ  is bounded continuous concave and non-decreasing on [0, ∞), with φ(0) = 0 and φ(t) > 0 when t > 0 (for example, φ(t) = min(t, 1)). Such a metric is called Lévy-metric for L0. Under this metric the space L0 is complete (it is again an F-space). The space L0 is in general not locally bounded, and not locally convex.

For the infinite Lebesgue measure λ on Rn, the definition of the fundamental system of neighborhoods could be modified as follows

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

The resulting space L0(Rn, λ) coincides as topological vector space with L0(Rn, g(x) dλ(x)), for any positive λ–integrable density g.

## Weak Lp

Let (S, Σ, μ) be a measure space, and f a measurable function with real or complex values on S. The distribution function of f is defined for t > 0 by

$\lambda_f(t) = \mu\left\{x\in S\mid |f(x)| > t\right\}$

If f is in Lp(S, μ) for some p with 1 ≤ p < ∞, then by Markov's inequality,

$\lambda_f(t)\le \frac{\|f\|_p^p}{t^p}$

A function f is said to be in the space weak Lp(S, μ), or Lp,w(S, μ), if there is a constant C > 0 such that, for all t > 0,

$\lambda_f(t) \le \frac{C^p}{t^p}$

The best constant C for this inequality is the Lp,w-norm of f, and is denoted by

$\|f\|_{p,w} = \sup_{t > 0} ~ t \lambda_f^{\frac{1}{p}}(t)$

The weak Lp coincide with the Lorentz spaces Lp,∞, so this notation is also used to denote them.

The Lp,w-norm is not a true norm, since the triangle inequality fails to hold. Nevertheless, for f in Lp(S, μ),

$\|f\|_{p,w}\le \|f\|_p$

and in particular Lp(S, μ) ⊂ Lp,w(S, μ). Under the convention that two functions are equal if they are equal μ almost everywhere, then the spaces Lp,w are complete (Grafakos 2004).

For any 0 < r < p the expression

$||| f |||_{L^{p,\infty}}=\sup_{0<\mu(E)<\infty} \mu(E)^{-\frac{1}{r}+\frac{1}{p}}\left(\int_E |f|^r\,d\mu\right)^{\frac{1}{r}}$

is comparable to the Lp,w-norm. Further in the case p > 1, this expression defines a norm if r = 1. Hence for p > 1 the weak Lp spaces are Banach spaces (Grafakos 2004).

A major result that uses the Lp,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 (S, Σ, μ). Let $\scriptstyle w :\; S \,\to\, [0,\, + \infty)$ be a measurable function. The w-weighted Lp space is defined as Lp(S, w dμ), where w dμ means the measure ν defined by

$\ \nu (A) \equiv \int_{A} w(x) \, \mathrm{d} \mu (x), \ \ \ A \in \Sigma$

or, in terms of the Radon–Nikodym derivative,

$\ w = \frac{\mathrm{d} \nu}{\mathrm{d} \mu}$

The norm for Lp(S, w dμ) is explicitly

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

As Lp-spaces, the weighted spaces have nothing special, since Lp(S, w dμ) is equal to Lp(S, dν). But they are the natural framework for several results in harmonic analysis (Grafakos 2004); they appear for example in the Muckenhoupt theorem: for 1 < p < ∞, the classical Hilbert transform is defined on Lp(Tλ) where T denotes the unit circle and λ the Lebesgue measure; the (nonlinear) Hardy–Littlewood maximal operator is bounded on Lp(Rnλ). Muckenhoupt's theorem describes weights w such that the Hilbert transform remains bounded on Lp(T, w dλ) and the maximal operator on Lp(Rn, w dλ).

## Lp spaces on manifolds

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