“Marcinkiewicz interpolation theorem”的意思、由来-开放百科全书

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Marcinkiewicz interpolation theorem

释义

Preliminaries
Formulation
Applications and examples
History
See also
References

In mathematics, the Marcinkiewicz interpolation theorem, discovered by {{harvs|txt|authorlink=Józef Marcinkiewicz|first=Józef |last=Marcinkiewicz|year=1939}}, is a result bounding the norms of non-linear operators acting on L^p spaces.

Marcinkiewicz' theorem is similar to the Riesz–Thorin theorem about linear operators, but also applies to non-linear operators.

Preliminaries

Let f be a measurable function with real or complex values, defined on a measure space (X, F, ω). The distribution function of f is defined by

Then f is called weak if there exists a constant C such that the distribution function of f satisfies the following inequality for all t > 0:

The smallest constant C in the inequality above is called the weak norm and is usually denoted by or Similarly the space is usually denoted by L^1,w or L^1,∞.

(Note: This terminology is a bit misleading since the weak norm does not satisfy the triangle inequality as one can see by considering the sum of the functions on given by and , which has norm 4 not 2.)

Any function belongs to L^1,w and in addition one has the inequality

This is nothing but Markov's inequality (aka Chebyshev's Inequality). The converse is not true. For example, the function 1/x belongs to L^1,w but not to L¹.

Similarly, one may define the weak space as the space of all functions f such that belong to L^1,w, and the weak norm using

More directly, the L^p,w norm is defined as the best constant C in the inequality

for all t > 0.

Formulation

Informally, Marcinkiewicz's theorem is

Theorem. Let T be a bounded linear operator from to and at the same time from to . Then T is also a bounded operator from to for any r between p and q.

In other words, even if you only require weak boundedness on the extremes p and q, you still get regular boundedness inside. To make this more formal, one has to explain that T is bounded only on a dense subset and can be completed. See Riesz-Thorin theorem for these details.

Where Marcinkiewicz's theorem is weaker than the Riesz-Thorin theorem is in the estimates of the norm. The theorem gives bounds for the norm of T but this bound increases to infinity as r converges to either p or q. Specifically {{harv|DiBenedetto|2002|loc=Theorem VIII.9.2}}, suppose that

so that the operator norm of T from L^p to L^p,w is at most N_p, and the operator norm of T from L^q to L^q,w is at most N_q. Then the following interpolation inequality holds for all r between p and q and all f ∈ L^r:

where

and

The constants δ and γ can also be given for q = ∞ by passing to the limit.

A version of the theorem also holds more generally if T is only assumed to be a quasilinear operator in the following sense: there exists a constant C > 0 such that T satisfies

for almost every x. The theorem holds precisely as stated, except with γ replaced by

An operator T (possibly quasilinear) satisfying an estimate of the form

is said to be of weak type (p,q). An operator is simply of type (p,q) if T is a bounded transformation from L^p to L^q:

A more general formulation of the interpolation theorem is as follows:

If T is a quasilinear operator of weak type (p₀, q₀) and of weak type (p₁, q₁) where q₀ ≠ q₁, then for each θ ∈ (0,1), T is of type (p,q), for p and q with p ≤ q of the form

The latter formulation follows from the former through an application of Hölder's inequality and a duality argument.{{Citation needed|reason=How to use Hölder's inequality and the special case?|date=June 2016}}

Applications and examples

A famous application example is the Hilbert transform. Viewed as a multiplier, the Hilbert transform of a function f can be computed by first taking the Fourier transform of f, then multiplying by the sign function, and finally applying the inverse Fourier transform.

Hence Parseval's theorem easily shows that the Hilbert transform is bounded from to . A much less obvious fact is that it is bounded from to . Hence Marcinkiewicz's theorem shows that it is bounded from to for any 1 < p < 2. Duality arguments show that it is also bounded for 2 < p < ∞. In fact, the Hilbert transform is really unbounded for p equal to 1 or ∞.

Another famous example is the Hardy–Littlewood maximal function, which is only sublinear operator rather than linear. While to bounds can be derived immediately from the to weak estimate by a clever change of variables, Marcinkiewicz interpolation is a more intuitive approach. Since the Hardy–Littlewood Maximal Function is trivially bounded from to , strong boundedness for all follows immediately from the weak (1,1) estimate and interpolation. The weak (1,1) estimate can be obtained from the Vitali covering lemma.

History

The theorem was first announced by {{harvtxt|Marcinkiewicz|1939}}, who showed this result to Antoni Zygmund shortly before he died in World War II. The theorem was almost forgotten by Zygmund, and was absent from his original works on the theory of singular integral operators. Later {{harvtxt|Zygmund|1956}} realized that Marcinkiewicz's result could greatly simplify his work, at which time he published his former student's theorem together with a generalization of his own.

References

{{citation | last=DiBenedetto|first=Emmanuele|title=Real analysis|publisher=Birkhäuser|year=2002|isbn=3-7643-4231-5}}.
{{citation|title=Elliptic partial differential equations of second order|first1=David|last1=Gilbarg|authorlink1=|first2=Neil S.|last2=Trudinger|authorlink2=Neil Trudinger|publisher=Springer-Verlag|year=2001|isbn=3-540-41160-7}}.
{{Citation | last1=Marcinkiewicz | first1=J. | title=Sur l'interpolation d'operations | year=1939 | journal=C. R. Acad. Sci. Paris | volume=208 | pages=1272–1273}}
{{citation|title=Introduction to Fourier analysis on Euclidean spaces|first1=Elias|last1=Stein|authorlink1=Elias Stein|first2=Guido|last2=Weiss|publisher=Princeton University Press|year=1971|isbn=0-691-08078-X}}.
{{Citation | last1=Zygmund | first1=A. | title=On a theorem of Marcinkiewicz concerning interpolation of operations |mr=0080887 | year=1956 | journal=Journal de Mathématiques Pures et Appliquées|series= Neuvième Série | issn=0021-7824 | volume=35 | pages=223–248}}

2 : Fourier analysis|Theorems in functional analysis

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Preliminaries

Formulation

Applications and examples

History

See also

References