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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) : with : in the general case, or: : 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: : 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 : where ''g'' is the unknown function, and lead to theorems of convergence towards the Chebyshev and Dirac measures. 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Secondary measure」の詳細全文を読む スポンサード リンク
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