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Secondary measure : ウィキペディア英語版
Secondary measure

In mathematics, the secondary measure associated with a measure of positive density ρ when there is one, is a measure of positive density μ, turning the secondary polynomials associated with the orthogonal polynomials for ρ into an orthogonal system.
==Introduction==
Under certain assumptions that we will specify further, it is possible to obtain the existence of a secondary measure and even to express it.
For example, if one works in the Hilbert space ''L''2((1 ), R, ρ)
: \forall x \in (), \qquad \mu(x)=\frac + \pi^2\rho^2(x)}
with
: \varphi(x) = \lim_ 2\int_0^1\frac \, dt
in the general case, or:
: \varphi(x) = 2\rho(x)\text\left(\frac\right) - 2 \int_0^1\frac \, dt
when ρ satisfies a Lipschitz condition.
This application φ is called the reducer of ρ.
More generally, μ et ρ are linked by their Stieltjes transformation with the following formula:
: S_(z)=z-c_1-\frac
in which ''c''1 is the moment of order 1 of the measure ρ.
These secondary measures, and the theory around them, lead to some surprising results, and make it possible to find in an elegant way quite a few traditional formulas of analysis, mainly around the Euler Gamma function, Riemann Zeta function, and Euler's constant.
They also allowed the clarification of integrals and series with a tremendous effectiveness, though it is a priori difficult.
Finally they make it possible to solve integral equations of the form
: f(x)=\int_0^1\frac\rho(t)\,dt
where ''g'' is the unknown function, and lead to theorems of convergence towards the Chebyshev and Dirac measures.

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