Estimation Network Design framework for efficient distributed optimization
Mattia Bianchi, Sergio Grammatico

TL;DR
This paper introduces Estimation Network Design (END), a flexible framework that leverages problem sparsity to enhance efficiency and scalability in distributed optimization algorithms.
Contribution
It extends END to distributed optimization, enabling sparsity exploitation in methods like ADMM, AugDGM, and Push-Sum DGD without complex convergence analysis.
Findings
Enhanced convergence speed in sensor network simulations
Significant reduction in communication and memory costs
Effective sparsity exploitation in various distributed algorithms
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Alternating Direction Method of Multipliers
