Advances in Distributed Optimization and Learning Algorithms
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 May 2027 | Viewed by 20
Editor
Special Issue Information
Dear Colleagues,
Distributed optimization and learning algorithms constitute a vital and rapidly evolving research frontier, providing a mathematically principled framework for the formulation and rigorous analysis of decentralized computational paradigms. The systematic integration of advanced mathematical techniques, encompassing convex and non-convex analysis, spectral graph theory, operator splitting methods, and stochastic approximation, engenders substantive interdisciplinary collaboration, catalyzes novel theoretical insights, and accelerates progress across a broad spectrum of scientific and engineering disciplines. Methodologically robust distributed algorithms are paramount to enhancing the efficacy, fault tolerance, and practical deployability of high-impact applications, including (but not limited to) privacy-preserving federated learning, decentralized coordination in autonomous multi-agent systems, and real-time resource optimization in intelligent infrastructure. Furthermore, these mathematically grounded approaches facilitate the development of highly adaptable algorithmic frameworks capable of generalizing across heterogeneous network topologies, non-identically distributed data sources, and stringent communication constraints, thereby enabling the construction of flexible, scalable, and resilient technological systems. Consequently, sustained advances in distributed optimization and learning algorithms are of profound importance to the continued advancement of the field and the successful translation of foundational theoretical innovations into consequential real-world applications.
Considering your expertise in this field, I would like to invite you to submit an article. If you are unable to contribute at this time, please feel free to share this invitation with your colleagues. Alternatively, you may provide us with the contact details of anyone who might be interested, and we will reach out to them directly.
Thank you for considering this opportunity. I look forward to receiving your contributions.
Dr. Jin Xie
Guest Editor
Manuscript Submission Information
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Keywords
- distributed optimization
- distributed learning
- multi-agent consensus
- decentralized learning
- decentralized federal learning
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