|Table of Contents|

Effects of Probabilistic Quantization on Distributed Multi-agent Optimization Problem

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2011年02期
Page:
209-212
Research Field:
Publishing date:

Info

Title:
Effects of Probabilistic Quantization on Distributed Multi-agent Optimization Problem
Author(s):
YUAN De-mingXU Sheng-yuanZHAO Huan-yuSHEN Hao
School of Automation,NUST,Nanjing 210094,China
Keywords:
multi-agent systems consensus algorithms subgradient methods probabilistic quantization
PACS:
O224
DOI:
-
Abstract:
This paper studies the problem of optimizing the sum of the individual local convex objective functions which are known to each agent accordingly.Based on the average consensus algorithm,this paper defines a subgradient step to solve this problem when the underlying network topology is fixed and the data transmitted through agents are probabilistically quantized.This paper derives an upper bound which is related to the quantization resolution and the underlying network connectivity on the convergence rate of the proposed method.The proposed algorithm guarantees that each agent’s state value in expectation can converge to the optimal objective value within some error if a fixed step size is used.

References:

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Last Update: 2012-04-30