Gnecco, Giorgio and Morisi, Rita and Bemporad, Alberto Sparse Solutions to the Average Consensus Problem via Various Regularizations of the Fastest Mixing Markov-Chain Problem. IEEE Transactions on Network Science and Engineering, 2 (3). pp. 97-111. ISSN 2327-4697 (2015)
Full text not available from this repository.Abstract
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 l₁ -norm and l₀ -“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.
| Item Type: | Article |
|---|---|
| Identification Number: | 10.1109/TNSE.2015.2479086 |
| Uncontrolled Keywords: | Artificial neural networks; Convergence; Convex functions; Eigenvalues and eigenfunctions; Optimization; Symmetric matrices; Wireless sensor networks; Consensus; Fastest Mixing Markov-Chain problem; Optimization; Regularization; Sparsity |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Research Area: | Computer Science and Applications |
| Depositing User: | Caterina Tangheroni |
| Date Deposited: | 19 Oct 2015 09:22 |
| Last Modified: | 19 Oct 2015 09:22 |
| URI: | http://eprints.imtlucca.it/id/eprint/2774 |
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