Abstract:
We study approximation problems for infinitely differentiable multivariate
functions in the worst-case setting.
Using a series of information-based algorithms as approximation tools, in which each
algorithm is constructed by performing finitely many standard information
operations, we prove that the
$L_\infty$-approximation problem is
exponentially convergent.
As a corollary, we show that the
corresponding integral problem is exponentially convergent as well.
Keywords:
infinitely differentiable function class,
standard information, worst-case setting.
This work was supported in part
by The National Natural Science Foundation of China under grants 11471043 and 11671271 and, in part, by
the Beijing Municipal Natural Science Foundation under grant 1172004.
Citation:
Yongping Liu, Guiqiao Xu, Jie Zhang, “Exponential Convergence of an Approximation Problem
for Infinitely Differentiable Multivariate Functions”, Math. Notes, 103:5 (2018), 769–779