{"day":"19","external_id":{"arxiv":["2403.10656"]},"doi":"10.1109/ISIT57864.2024.10619367","oa_version":"Preprint","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2403.10656 ","open_access":"1"}],"type":"conference","language":[{"iso":"eng"}],"_id":"17893","title":"Properties of the strong data processing constant for Rényi divergence","citation":{"ama":"Jin L, Esposito AR, Gastpar M. Properties of the strong data processing constant for Rényi divergence. In: <i>Proceedings of the 2024 IEEE International Symposium on Information Theory</i>. Institute of Electrical and Electronics Engineers; 2024:3178-3183. doi:<a href=\"https://doi.org/10.1109/ISIT57864.2024.10619367\">10.1109/ISIT57864.2024.10619367</a>","ieee":"L. Jin, A. R. Esposito, and M. Gastpar, “Properties of the strong data processing constant for Rényi divergence,” in <i>Proceedings of the 2024 IEEE International Symposium on Information Theory</i>, Athens, Greece, 2024, pp. 3178–3183.","chicago":"Jin, Lifu, Amedeo Roberto Esposito, and Michael Gastpar. “Properties of the Strong Data Processing Constant for Rényi Divergence.” In <i>Proceedings of the 2024 IEEE International Symposium on Information Theory</i>, 3178–83. Institute of Electrical and Electronics Engineers, 2024. <a href=\"https://doi.org/10.1109/ISIT57864.2024.10619367\">https://doi.org/10.1109/ISIT57864.2024.10619367</a>.","ista":"Jin L, Esposito AR, Gastpar M. 2024. Properties of the strong data processing constant for Rényi divergence. Proceedings of the 2024 IEEE International Symposium on Information Theory. ISIT: International Symposium on Information Theory, 3178–3183.","short":"L. Jin, A.R. Esposito, M. Gastpar, in:, Proceedings of the 2024 IEEE International Symposium on Information Theory, Institute of Electrical and Electronics Engineers, 2024, pp. 3178–3183.","apa":"Jin, L., Esposito, A. R., &#38; Gastpar, M. (2024). Properties of the strong data processing constant for Rényi divergence. In <i>Proceedings of the 2024 IEEE International Symposium on Information Theory</i> (pp. 3178–3183). Athens, Greece: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/ISIT57864.2024.10619367\">https://doi.org/10.1109/ISIT57864.2024.10619367</a>","mla":"Jin, Lifu, et al. “Properties of the Strong Data Processing Constant for Rényi Divergence.” <i>Proceedings of the 2024 IEEE International Symposium on Information Theory</i>, Institute of Electrical and Electronics Engineers, 2024, pp. 3178–83, doi:<a href=\"https://doi.org/10.1109/ISIT57864.2024.10619367\">10.1109/ISIT57864.2024.10619367</a>."},"author":[{"first_name":"Lifu","last_name":"Jin","full_name":"Jin, Lifu"},{"full_name":"Esposito, Amedeo Roberto","id":"9583e921-e1ad-11ec-9862-cef099626dc9","last_name":"Esposito","first_name":"Amedeo Roberto"},{"first_name":"Michael","last_name":"Gastpar","full_name":"Gastpar, Michael"}],"oa":1,"article_processing_charge":"No","acknowledgement":"The work in this paper was supported in part by the Swiss National Science Foundation under Grant 200364.\r\n","conference":{"end_date":"2024-07-12","location":"Athens, Greece","name":"ISIT: International Symposium on Information Theory","start_date":"2024-07-07"},"date_created":"2024-09-08T22:01:12Z","year":"2024","publication":"Proceedings of the 2024 IEEE International Symposium on Information Theory","publisher":"Institute of Electrical and Electronics Engineers","status":"public","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"3178-3183","corr_author":"1","department":[{"_id":"MaMo"}],"date_updated":"2024-09-11T06:10:18Z","scopus_import":"1","quality_controlled":"1","publication_identifier":{"issn":["2157-8095"],"isbn":["9798350382846"]},"month":"08","abstract":[{"lang":"eng","text":"Strong data processing inequalities (SDPI) are an important object of study in Information Theory and have been well studied for f -divergences. Universal upper and lower bounds have been provided along with several applications, connecting them to impossibility (converse) results, concentration of measure, hypercontractivity, and so on. In this paper, we study Renyi divergence and the corresponding SDPI constant whose behavior seems to deviate from that of ordinary <1>-divergences. In particular, one can find examples showing that the universal upper bound relating its SDPI constant to the one of Total Variation does not hold in general. In this work, we prove, however, that the universal lower bound involving the SDPI constant of the Chi-square divergence does indeed hold. Furthermore, we also provide a characterization of the distribution that achieves the supremum when is equal to 2 and consequently compute the SDPI constant for Renyi divergence of the general binary channel."}],"date_published":"2024-08-19T00:00:00Z"}