论文标题
在不足集的情况下，与有条件正确定的内核近似
Approximation with Conditionally Positive Definite Kernels on Deficient Sets
论文作者
论文摘要
在不确定多项式的一组中心，考虑了具有条件正确定内核功能的插值和近似值。结果表明，多项式一致性足以定义基于内核的功能的数值近似，并具有最佳恢复的通常属性。应用程序示例包括在网格上为laplacian生成稀疏内核的数值分化公式，并且在椭圆上的函数的准确逼近。
Interpolation and approximation of functionals with conditionally positive definite kernels is considered on sets of centers that are not determining for polynomials. It is shown that polynomial consistency is sufficient in order to define kernel-based numerical approximation of the functional with usual properties of optimal recovery. Application examples include generation of sparse kernel-based numerical differentiation formulas for the Laplacian on a grid and accurate approximation of a function on an ellipse.
