Recent theoretical advances in decentralized distributed convex optimization
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Abstract (arXiv)
In the last few years, the theory of decentralized distributed convex optimization has made significant progress. The lower bounds on communications rounds and oracle calls have appeared, as well as methods that reach both of these bounds. In this paper, we focus on how these results can be explained based on optimal algorithms for the non-distributed setup. In particular, we provide our recent results that have not been published yet and that could be found in details only in arXiv preprints.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). First posted on arXiv 26 Nov 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021Acceleration in Distributed Optimization under Similaritycited by 17
- 2021ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networkscited by 13
- 2022DADAO: Decoupled Accelerated Decentralized Asynchronous Optimizationcited by 8
- 2021Decentralized Feature-Distributed Optimization for Generalized Linear Modelscited by 0
- 2026Decentralized Optimization with Cross-Coupled Mixed Affine Constraintscited by 0
- 2026Decentralized Optimization with Cross-Coupled Mixed Affine Constraints26 Sep 2026
- 2022DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization26 Jul 2022
- 2021Decentralized Feature-Distributed Optimization for Generalized Linear Models28 Oct 2021
- 2021Acceleration in Distributed Optimization under Similarity24 Oct 2021
- 2021ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks18 Feb 2021