.png){kind=icon}
返回
Automatic Performance Estimation for Decentralized Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2023.3251902.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, 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 a semidefinite program. 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.
Keyword:
Consensus
distributed optimization
performance estimation problem (PEP)
rates of convergence
worst-case analysis
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
Real-world effectiveness and safety of sofosbuvir/velpatasvir and ledipasvir/sofosbuvir hepatitis C treatment in a single centre in Germany.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Analysis and Design of First-Order Distributed Optimization Algorithms Over Time-Varying Graphs时变图一阶分布式优化算法的分析与设计OAAI
OAAI分享收藏Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North使用北部红河谷的土壤，作物和附近气象站数据预测田间土壤湿度的机器学习Soil SystemsOAAI
Soil SystemsIF0
OAAI分享收藏
查看更多内容