{"ID":2840704,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13584","arxiv_id":"2511.13584","title":"HBNET-GIANT: A communication-efficient accelerated Newton-type fully distributed optimization algorithm","abstract":"This article presents a second-order fully distributed optimization algorithm, HBNET-GIANT, driven by heavy-ball momentum, for $L$-smooth and $μ$-strongly convex objective functions. A rigorous convergence analysis is performed, and we demonstrate global linear convergence under certain sufficient conditions. Through extensive numerical experiments, we show that HBNET-GIANT with heavy-ball momentum achieves acceleration, and the corresponding rate of convergence is strictly faster than its non-accelerated version, NETWORK-GIANT. Moreover, we compare HBNET-GIANT with several state-of-the-art algorithms, both momentum-based and without momentum, and report significant performance improvement in convergence to the optimum. We believe that this work lays the groundwork for a broader class of second-order Newton-type algorithms with momentum and motivates further investigation into open problems, including an analytical proof of local acceleration in the fully distributed setting for convex optimization problems.","short_abstract":"This article presents a second-order fully distributed optimization algorithm, HBNET-GIANT, driven by heavy-ball momentum, for $L$-smooth and $μ$-strongly convex objective functions. A rigorous convergence analysis is performed, and we demonstrate global linear convergence under certain sufficient conditions. Through e...","url_abs":"https://arxiv.org/abs/2511.13584","url_pdf":"https://arxiv.org/pdf/2511.13584v1","authors":"[\"Souvik Das\",\"Luca Schenato\",\"Subhrakanti Dey\"]","published":"2025-11-17T16:46:53Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"eess.SP\"]","methods":"[]","has_code":false}