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arXiv 1204.6448math.NAcs.NA
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通过分布算子格林函数方法推广Sobolev空间的再生核

Reproducing Kernels of Generalized Sobolev Spaces via a Green Function Approach with Distributional Operators

Illinois Institute of Technology(伊利诺伊理工学院)

Gregory E. Fasshauer, Qi Ye

AI总结:

本文通过定义由向量分布算子构成的半内积引入广义Sobolev空间,证明全空间格林函数是条件正定函数,并建立其原生空间与广义Sobolev空间的等距关系,最后通过Matern核和高斯核等实例展示常见再生核希尔伯特空间与广义Sobolev空间的等距等价性。

摘要:

本文通过定义一个由向量分布算子$\mathbf{P}$(由有限或可数个定义在Schwartz空间对偶空间上的分布算子$P_n$组成)构成的半内积,引入了一个广义Sobolev空间。我们考虑的算子类型不仅包括微分算子,还包括更一般的分布算子,如伪微分算子。我们推导出,关于$L:=\mathbf{P}^{\ast T}\mathbf{P}$的某个适当的全空间格林函数$G$成为条件正定函数。为了支持这一论断,我们确保$\mathbf{P}$的分布伴随算子$\mathbf{P}^{\ast}$在分布意义下是良定义的。在充分条件下,与格林函数$G$相关的原生空间(再生核希尔伯特空间)可以等距嵌入甚至等距等价于一个广义Sobolev空间。作为应用,我们取格林函数平移的线性组合(可能添加多项式项),并构造一个多元最小范数插值函数$s_{f,X}$,用于从位于某个集合$X \subset \mathbb{R}^d$中的数据点处采样的未知广义Sobolev函数$f$的数据值。我们提供了几个例子,如Matern核或高斯核,说明了许多已知再生核的再生核希尔伯特空间如何等距等价于一个广义Sobolev空间。这些例子进一步说明了我们如何通过向量分布算子$\mathbf{P}$重新缩放Sobolev空间。将尺度的概念作为广义Sobolev空间定义的一部分,可能有助于我们为基于核的逼近方法选择“最佳”核函数。

英文摘要:

In this paper we introduce a generalized Sobolev space by defining a semi-inner product formulated in terms of a vector distributional operator $\mathbf{P}$ consisting of finitely or countably many distributional operators $P_n$, which are defined on the dual space of the Schwartz space. The types of operators we consider include not only differential operators, but also more general distributional operators such as pseudo-differential operators. We deduce that a certain appropriate full-space Green function $G$ with respect to $L:=\mathbf{P}^{\ast T}\mathbf{P}$ now becomes a conditionally positive definite function. In order to support this claim we ensure that the distributional adjoint operator $\mathbf{P}^{\ast}$ of $\mathbf{P}$ is well-defined in the distributional sense. Under sufficient conditions, the native space (reproducing-kernel Hilbert space) associated with the Green function $G$ can be isometrically embedded into or even be isometrically equivalent to a generalized Sobolev space. As an application, we take linear combinations of translates of the Green function with possibly added polynomial terms and construct a multivariate minimum-norm interpolant $s_{f,X}$ to data values sampled from an unknown generalized Sobolev function $f$ at data sites located in some set $X \subset \mathbb{R}^d$. We provide several examples, such as Matérn kernels or Gaussian kernels, that illustrate how many reproducing-kernel Hilbert spaces of well-known reproducing kernels are isometrically equivalent to a generalized Sobolev space. These examples further illustrate how we can rescale the Sobolev spaces by the vector distributional operator $\mathbf{P}$. Introducing the notion of scale as part of the definition of a generalized Sobolev space may help us to choose the "best" kernel function for kernel-based approximation methods.

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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