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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.07368 |
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| _version_ | 1866929341100720128 |
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| author | Kamatsuka, Akira Kazama, Koki Yoshida, Takahiro |
| author_facet | Kamatsuka, Akira Kazama, Koki Yoshida, Takahiro |
| contents | The problem of computing $α$-capacity for $α>1$ is equivalent to that of computing the correct decoding exponent. Various algorithms for computing them have been proposed, such as Arimoto and Jitsumatsu--Oohama algorithm. In this study, we propose a novel alternating optimization algorithm for computing the $α$-capacity for $α>1$ based on a variational characterization of the Augustin--Csisz{á}r mutual information. A comparison of the convergence performance of these algorithms is demonstrated through numerical examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07368 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A New Algorithm for Computing $α$-Capacity Kamatsuka, Akira Kazama, Koki Yoshida, Takahiro Information Theory The problem of computing $α$-capacity for $α>1$ is equivalent to that of computing the correct decoding exponent. Various algorithms for computing them have been proposed, such as Arimoto and Jitsumatsu--Oohama algorithm. In this study, we propose a novel alternating optimization algorithm for computing the $α$-capacity for $α>1$ based on a variational characterization of the Augustin--Csisz{á}r mutual information. A comparison of the convergence performance of these algorithms is demonstrated through numerical examples. |
| title | A New Algorithm for Computing $α$-Capacity |
| topic | Information Theory |
| url | https://arxiv.org/abs/2405.07368 |