DocumentCode :
3279369
Title :
Optimal linear iterations for distributed agreement
Author :
Qing Hui ; Haopeng Zhang
Author_Institution :
Dept. of Mech. Eng., Texas Tech Univ., Lubbock, TX, USA
fYear :
2010
fDate :
June 30 2010-July 2 2010
Firstpage :
81
Lastpage :
86
Abstract :
A new optimal distributed linear averaging (ODLA) problem is presented in this paper. This problem is motivated from the distributed averaging problem which arises in the context of distributed algorithms in computer science and coordination of groups of autonomous agents in engineering. The aim of the ODLA problem is to compute the average of the initial values at nodes of a graph through an optimal distributed algorithm in which the nodes in the graph can only communicate with their neighbors. Optimality is given by a minimization problem of a quadratic cost functional under infinite horizon. We show that this problem has a very close relationship with the notion of semistability. By developing new necessary and sufficient conditions for semistability of linear discrete-time systems, we convert the original ODLA problem into two equivalent optimization problems. These two problems are convex optimization problems and can be solved by using some numerical techniques.
Keywords :
graph theory; iterative methods; minimisation; mobile robots; autonomous agents; distributed averaging problem; equivalent optimization problems; infinite horizon; linear discrete-time systems; numerical techniques; optimal distributed algorithm; optimal distributed linear averaging; optimal linear iterations; quadratic cost functional; Autonomous agents; Computer science; Context; Cost function; Distributed algorithms; Distributed computing; Infinite horizon; Mobile robots; Optimal control; Sufficient conditions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2010
Conference_Location :
Baltimore, MD
ISSN :
0743-1619
Print_ISBN :
978-1-4244-7426-4
Type :
conf
DOI :
10.1109/ACC.2010.5530654
Filename :
5530654
Link To Document :
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