# Recent theoretical advances in decentralized distributed convex optimization

arXiv: https://arxiv.org/abs/2011.13259 · HTML: https://mlsanity.com/paper/2011.13259/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Acceleration in Distributed Optimization under Similarity](https://mlsanity.com/paper/2110.12347/) (2021)
- [ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks](https://mlsanity.com/paper/2102.09234/) (2021)
- [DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization](https://mlsanity.com/paper/2208.00779/) (2022)
- [Decentralized Feature-Distributed Optimization for Generalized Linear Models](https://mlsanity.com/paper/2110.15283/) (2021)
- [Decentralized Optimization with Cross-Coupled Mixed Affine Constraints](https://mlsanity.com/paper/2609.33021/) (2026)

## 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.