TY - GEN
T1 - Convergence of distributed averaging and maximizing algorithms part I
T2 - 2013 1st American Control Conference, ACC 2013
AU - Shi, Guodong
AU - Johansson, Karl Henrik
PY - 2013
Y1 - 2013
N2 - In this paper, we formulate and investigate a generalized consensus algorithm which makes an attempt to unify distributed averaging and maximizing algorithms considered in the literature. Each node iteratively updates its state as a time-varying weighted average of its own state, the minimal state, and the maximal state of its neighbors. This part of the paper focuses on time-dependent communication graphs. We prove that finite-time consensus is almost impossible for averaging under this uniform model. Then various necessary and/or sufficient conditions are presented on the consensus convergence. The results characterize some similarities and differences between distributed averaging and maximizing algorithms.
AB - In this paper, we formulate and investigate a generalized consensus algorithm which makes an attempt to unify distributed averaging and maximizing algorithms considered in the literature. Each node iteratively updates its state as a time-varying weighted average of its own state, the minimal state, and the maximal state of its neighbors. This part of the paper focuses on time-dependent communication graphs. We prove that finite-time consensus is almost impossible for averaging under this uniform model. Then various necessary and/or sufficient conditions are presented on the consensus convergence. The results characterize some similarities and differences between distributed averaging and maximizing algorithms.
KW - Averaging algorithms
KW - Finite-time convergence
KW - Max-consensus
UR - http://www.scopus.com/inward/record.url?scp=84883548047&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781479901777
T3 - Proceedings of the American Control Conference
SP - 6096
EP - 6101
BT - 2013 American Control Conference, ACC 2013
Y2 - 17 June 2013 through 19 June 2013
ER -