0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessOur objective is to find a necessary and sufficient condition for consensus over a random network generated by i.i.d. stochastic matrices. We show that the consensus problem in all different types of convergence (almost surely, in probability, and in <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$L^{p}$</tex> </formula> for every <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$p\geq 1$</tex></formula> ) are actually equivalent, thereby obtain the same necessary and sufficient condition for all of them. The main technique we used is based on the stability in a projected subspace of the concerned infinite sequences.
Qingshuo Song, Guanrong Chen, Daniel W. C. Ho (2011). On the Equivalence and Condition of Different Consensus Over a Random Network Generated by i.i.d. Stochastic Matrices. IEEE Transactions on Automatic Control, 56(5), pp. 1203-1207, DOI: 10.1109/tac.2011.2107130.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2011
Authors
3
Datasets
0
Total Files
0
Language
English
Journal
IEEE Transactions on Automatic Control
DOI
10.1109/tac.2011.2107130
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
