arXiv Machine Learning August 4, 2026 By Mohammadreza Doostmohammadian, Amir Ahmad Ghods, Alireza Aghasi, Zulfiya R. Gabidullina, Hamid R. Rabiee

Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap

Read the original on arXiv Machine Learning →

arXiv:2608. 01220v1 Announce Type: cross Abstract: In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learning (ML) solutions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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