# Estimation Network Design framework for efficient distributed optimization

arXiv: https://arxiv.org/abs/2404.15273 · HTML: https://mlsanity.com/paper/2404.15273/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 19 of 19)

- [EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization](https://mlsanity.com/paper/1404.6264/) (2014)
- [Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs](https://mlsanity.com/paper/1607.03218/) (2016)
- [NEXT: In-Network Nonconvex Optimization](https://mlsanity.com/paper/1602.00591/) (2016)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates](https://mlsanity.com/paper/1704.07807/) (2017)
- [A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks](https://mlsanity.com/paper/1809.00710/) (2018)
- [Communication-Efficient Topologies for Decentralized Learning with O(1) Consensus Rate](https://mlsanity.com/paper/2210.07881/) (2022)
- [Distributed Coordinate Descent Method for Learning with Big Data](https://mlsanity.com/paper/1310.2059/) (2013)
- [Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis](https://mlsanity.com/paper/2002.11534/) (2020)
- [Distributed Optimization With Local Domains: Applications in MPC and Network Flows](https://mlsanity.com/paper/1305.1885/) (2013)
- [Distributed Coupled Multi-Agent Stochastic Optimization](https://mlsanity.com/paper/1712.08817/) (2017)
- [Tracking-ADMM for Distributed Constraint-Coupled Optimization](https://mlsanity.com/paper/1907.10860/) (2019)
- [Distributed Partitioned Big-Data Optimization via Asynchronous Dual Decomposition](https://mlsanity.com/paper/1805.08460/) (2018)
- [A Proximal Diffusion Strategy for Multi-Agent Optimization with Sparse Affine Constraints](https://mlsanity.com/paper/1810.02124/) (2018)
- [Distributed Proximal Algorithms for Multi-Agent Optimization with Coupled Inequality Constraints](https://mlsanity.com/paper/1907.03245/) (2019)
- [Locality in Network Optimization](https://mlsanity.com/paper/1509.06246/) (2015)
- [Fully distributed Nash equilibrium seeking over time-varying communication networks with linear convergence rate](https://mlsanity.com/paper/2003.10871/) (2020)
- [Distributed synchronous and asynchronous algorithms for semi-definite programming with diagonal constraints](https://mlsanity.com/paper/2104.00360/) (2021)
- [The END: Estimation Network Design for games under partial-decision information](https://mlsanity.com/paper/2208.11377/) (2022)

## Abstract (arXiv)

Distributed decision problems features a group of agents that can only communicate over a peer-to-peer network, without a central memory. In applications such as network control and data ranking, each agent is only affected by a small portion of the decision vector: this sparsity is typically ignored in distributed algorithms, while it could be leveraged to improve efficiency and scalability. To address this issue, our recent paper introduces Estimation Network Design (END), a graph theoretical language for the analysis and design of distributed iterations. END algorithms can be tuned to exploit the sparsity of specific problem instances, reducing communication overhead and minimizing redundancy, yet without requiring case-by-case convergence analysis. In this paper, we showcase the flexility of END in the context of distributed optimization. In particular, we study the sparsity-aware version of many established methods, including ADMM, AugDGM and Push-Sum DGD. Simulations on an estimation problem in sensor networks demonstrate that END algorithms can boost convergence speed and greatly reduce the communication and memory cost.