In the first part we have shown that, for $L_2$-approximation of functions from a separable Hilbert space in the worst-case setting, linear algorithms based on function values are almost as powerful as arbitrary linear algorithms if the approximation numbers are square-summable. That is, they achieve the same polynomial rate of convergence. In this sequel, we prove a similar result for separable Banach spaces and other classes of functions.
翻译:在第一部分,我们已表明,对于在最坏的情况下从可分离的Hilbert空格上以2美元为单位的功能,以函数值为基础的线性算法,如果近似数字是可平和的,则几乎与任意的线性算法一样强大。也就是说,它们达到相同的多面趋同率。在本次续集中,我们证明对可分离的Banach空格和其他类型的函数也有类似的结果。