# Distributed Optimization With Local Domains: Applications in MPC and Network Flows

arXiv: https://arxiv.org/abs/1305.1885 · HTML: https://mlsanity.com/paper/1305.1885/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2013.

## Built on by (most cited first, 6 of 6)

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- [Dencentralized learning in the presence of low-rank noise](https://mlsanity.com/paper/2203.09810/) (2022)
- [Estimation Network Design framework for efficient distributed optimization](https://mlsanity.com/paper/2404.15273/) (2024)
- [A non-smooth regularization framework for learning over multitask graphs](https://mlsanity.com/paper/2509.17728/) (2025)
- [HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization](https://mlsanity.com/paper/2604.13179/) (2026)

## Abstract (arXiv)

In this paper we consider a network with P nodes, where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector x^star minimizing the sum of all the functions. We make the additional assumption that the functions have intersecting local domains, i.e., each function depends only on some components of the variable. Consequently, each node is interested in knowing only some components of x^star, not the entire vector. This allows for improvement in communication-efficiency. We apply our algorithm to model predictive control (MPC) and to network flow problems and show, through experiments on large networks, that our proposed algorithm requires less communications to converge than prior algorithms.