# A Parameter-free Decentralized Algorithm for Composite Convex Optimization

arXiv: https://arxiv.org/abs/2508.01466 · HTML: https://mlsanity.com/paper/2508.01466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Adaptive Decentralized Composite Optimization via Three-Operator Splitting](https://mlsanity.com/paper/2602.17545/) (2026)

## Abstract (arXiv)

The paper studies decentralized optimization over networks, where agents minimize a composite objective consisting of the sum of smooth convex functions--the agents' losses--and an additional nonsmooth convex extended value function. We propose a decentralized algorithm wherein agents it adaptively adjust their stepsize using local backtracking procedures that require it no it global (network) information or extensive inter-agent communications. Our adaptive decentralized method enjoys robust convergence guarantees, outperforming existing decentralized methods, which are not adaptive. Our design is centered on a three-operator splitting, applied to a reformulation of the optimization problem. This reformulation utilizes a proposed BCV metric, which facilitates decentralized implementation and local stepsize adjustments while guarantying convergence.