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arXiv 1807.06007math.NAcs.NAstat.ML
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关于勒贝格积分Quadrature

On Lebesgue Integral Quadrature

Vladislav Gennadievich Malyshkin

摘要:

提出了一种新的求积方法。对于给定的度量,高斯求积找到函数的参数(节点)和相应权重的最优值。与此相反,本文中开发的Lebesgue求积,找到函数(值节点)和相应的权重的最优值。高斯求积组通过函数自变量求和;它可以被视为$n$点离散测度,产生黎曼积分。勒贝格求积群通过函数值求和;它可以被看作是$n$点离散分布,产生勒贝格积分。在数学上,该问题归结为一个广义本征值问题:Lebesgue求积值节点是本征值,相应的权重是平均本征向量的平方。当分析不规则和随机过程时,作为勒贝格积分的积分的数值估计特别有利。该方法将结果(值节点)和结果的概率(权重)分开。因此,它特别适合于非高斯过程的研究。实现该理论的软件可从作者处获得。

英文摘要:

A new type of quadrature is developed. The Gaussian quadrature, for a given measure, finds optimal values of a function's argument (nodes) and the corresponding weights. In contrast, the Lebesgue quadrature developed in this paper, finds optimal values of function (value-nodes) and the corresponding weights. The Gaussian quadrature groups sums by function argument; it can be viewed as a $n$-point discrete measure, producing the Riemann integral. The Lebesgue quadrature groups sums by function value; it can be viewed as a $n$-point discrete distribution, producing the Lebesgue integral. Mathematically, the problem is reduced to a generalized eigenvalue problem: Lebesgue quadrature value-nodes are the eigenvalues and the corresponding weights are the square of the averaged eigenvectors. A numerical estimation of an integral as the Lebesgue integral is especially advantageous when analyzing irregular and stochastic processes. The approach separates the outcome (value-nodes) and the probability of the outcome (weight). For this reason, it is especially well-suited for the study of non-Gaussian processes. The software implementing the theory is available from the authors.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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