TY  - JOUR
PB  - IEEE
A1  - Gnecco, Giorgio
A1  - Morisi, Rita
A1  - Bemporad, Alberto
SP  - 97
Y1  - 2015/07//
IS  - 3
JF  - IEEE Transactions on Network Science and Engineering
VL  - 2
SN  - 2327-4697
N2  - In the consensus problem on multi-agent systems, in which the states of the agents represent opinions, the agents aim at reaching a common opinion (or consensus state) through local exchange of information. An important design problem is to choose the degree of interconnection of the subsystems to achieve a good trade-off between a small number of interconnections and a fast convergence to the consensus state, which is the average of the initial opinions under mild conditions. This paper addresses this problem through l? -norm and l? -?pseudo-norm? regularized versions of the well-known Fastest Mixing Markov-Chain (FMMC) problem. We show that such versions can be interpreted as robust forms of the FMMC problem and provide results to guide the choice of the regularization parameter.
KW  - Artificial neural networks; Convergence; Convex functions; Eigenvalues and eigenfunctions; Optimization; Symmetric matrices; Wireless sensor networks; Consensus; Fastest Mixing Markov-Chain problem; Optimization; Regularization; Sparsity
TI  - Sparse Solutions to the Average Consensus Problem via Various Regularizations of the Fastest Mixing Markov-Chain Problem
AV  - none
UR  - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7268908&filter%3DAND(p_IS_Number%3A7295693)
ID  - eprints2774
EP  - 111
ER  -