On Optimal Recovery and Information Complexity in Numerical Differentiation and Summation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913574871367680 |
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| author | Semenova, Y. V. Solodky, S. G. |
| author_facet | Semenova, Y. V. Solodky, S. G. |
| contents | In this paper, we study optimization problems of numerical differentiation and summation methods on classes of univariate functions. Sharp estimates (in order) of the optimal recovery error and information complexity are calculated for these classes. Algorithms are constructed based on the truncation method and Chebyshev polynomials to implement these estimates. Moreover, we establish under what conditions the summation problem is well-posed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Optimal Recovery and Information Complexity in Numerical Differentiation and Summation Semenova, Y. V. Solodky, S. G. Numerical Analysis 65D25 In this paper, we study optimization problems of numerical differentiation and summation methods on classes of univariate functions. Sharp estimates (in order) of the optimal recovery error and information complexity are calculated for these classes. Algorithms are constructed based on the truncation method and Chebyshev polynomials to implement these estimates. Moreover, we establish under what conditions the summation problem is well-posed. |
| title | On Optimal Recovery and Information Complexity in Numerical Differentiation and Summation |
| topic | Numerical Analysis 65D25 |
| url | https://arxiv.org/abs/2405.20020 |