# Robustness and Convergence Analysis of First-Order Distributed Optimization Algorithms over Subspace Constraints

arXiv: https://arxiv.org/abs/2210.16277 · HTML: https://mlsanity.com/paper/2210.16277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2022.

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

- [Exact Subspace Diffusion for Decentralized Multitask Learning](https://mlsanity.com/paper/2304.07358/) (2023)

## Abstract (arXiv)

This paper extends algorithms that remove the fixed point bias of decentralized gradient descent to solve the more general problem of distributed optimization over subspace constraints. Leveraging the integral quadratic constraint framework, we analyze the performance of these generalized algorithms in terms of worst-case robustness and convergence rate. The utility of our framework is demonstrated by showing how one of the extended algorithms, originally designed for consensus, is now able to solve a multitask inference problem.