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Journal of Applied Mathematics and Computation

ISSN Online: 2576-0653 Downloads: 310762 Total View: 2891453
Frequency: quarterly ISSN Print: 2576-0645 CODEN: JAMCEZ
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Article Open Access http://dx.doi.org/10.26855/jamc.2022.06.003

Probabilistic and Average Widths of Sobolev Spaces with Lower Smoothness and Gaussian Measure

Jiehua Zhou1, Yuewu Li2,*

1Hulunbuir Vocational and Technological College, 021000, Hailaer, Inner Mongolia, China.

2School of Mathematical and Statistics, Hulunbuir University, 021008, Hailaer, Inner Mongolia, China.

*Corresponding author: Yuewu Li

Published: April 21,2022

Abstract

In this paper, by using the discretization method, which is based on the reduction of the calculation of the widths of a given class    to the computation of widths of finite-dimensional set equipped with the standard Gaussian measure,  we determine the asymptotic degree of the probabilistic and average widths of Sobolev spaces with lower smoothness.  We only prove the upper estimates in Theorems 1, 2, but we conjecture the estimates are exact in the sense of order, and obtain excellent results.  Thus the estimate from bellow is still an important open problems.  It is very interesting and significant that the scope of parameter is extended from   to    in Theorems 3, 4.  From this, we may conclude the quantity  , which is the characteristic of smoothness and measure , is essential for probabilistic widths of the Sobolev space  with Gaussian measure.  In other words, under the probabilistic and average settings, it seems to be the fact that the condition of smoothness of functions could be weaker than classical settings.

Keywords

Probabilistic widths, Average widths, Sobolev spaces, Gaussian measure, Lower smoothness

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How to cite this paper

Probabilistic and Average Widths of Sobolev Spaces with Lower Smoothness and Gaussian Measure

How to cite this paper: Jiehua Zhou, Yuewu Li. (2022) Probabilistic and Average Widths of Sobolev Spaces with Lower Smoothness and Gaussian Measure. Journal of Applied Mathematics and Computation6(2), 188-197.

DOI: http://dx.doi.org/10.26855/jamc.2022.06.003