# Automatic Performance Estimation for Decentralized Optimization

arXiv: https://arxiv.org/abs/2203.05963 · HTML: https://mlsanity.com/paper/2203.05963/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [Several Performance Bounds on Decentralized Online Optimization are Highly Conservative and Potentially Misleading](https://mlsanity.com/paper/2509.06466/) (2025)
- [Local Updates in Distributed Optimization: Provable Acceleration and Topology Effects](https://mlsanity.com/paper/2601.03442/) (2026)
- [Accelerating Optimization and Machine Learning through Decentralization](https://mlsanity.com/paper/2604.19518/) (2026)

## Abstract (arXiv)

We present a methodology to automatically compute worst-case performance bounds for a large class of first-order decentralized optimization algorithms. These algorithms aim at minimizing the average of local functions that are distributed across a network of agents. They typically combine local computations and consensus steps. Our methodology is based on the approach of Performance Estimation Problem (PEP), which allows computing the worst-case performance and a worst-case instance of first-order optimization algorithms by solving an SDP. We propose two ways of representing consensus steps in PEPs, which allow writing and solving PEPs for decentralized optimization. The first formulation is exact but specific to a given averaging matrix. The second formulation is a relaxation but provides guarantees valid over an entire class of averaging matrices, characterized by their spectral range. This formulation often allows recovering a posteriori the worst possible averaging matrix for the given algorithm. We apply our methodology to three different decentralized methods. For each of them, we obtain numerically tight worst-case performance bounds that significantly improve on the existing ones, as well as insights about the parameters tuning and the worst communication networks.