.png){kind=icon}
.png){kind=icon}
Return
An Approximate Distributed Gradient Estimation Method for Networked System Optimization Under Limited Communications
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2018.2867154.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper considers the networked system optimization problem by cooperatively finding an approximately optimal solution to the overall network convex cost function, which is the sum of the individual cost functions of nodes (agents) in a networked system. A new distributed gradient descent algorithm is proposed based on the distributed estimation of the sum of gradients of individual cost functions using a consensus-type coordination algorithm. The proposed algorithm can address unknown directed communication topologies and only requires limited communications and information exchanges among nodes in the networked system. Under the assumption that the communication graph is strongly connected, the convergence of the proposed algorithm is rigorously analyzed. Simulation examples are presented to demonstrate the applicability and effectiveness of the proposed algorithm.
Keywords:
Cost function
Estimation
Topology
Approximation algorithms
Computational modeling
Resource management
Consensus
distributed gradient descent
limited communication
networked system optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
