Introduces Full-Covariance-Consensus, Partial-Covariance-Consensus, and Mean-Consensus distributed covariance steering methods via non-convex ADMM, with convergence guarantees for the latter two and demonstrations of scalability to thousands of agents.
Linearized ADMM converges to second-order stationary points for non-convex problems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Distributed Covariance Steering via Non-Convex ADMM for Large-Scale Multi-Agent Systems
Introduces Full-Covariance-Consensus, Partial-Covariance-Consensus, and Mean-Consensus distributed covariance steering methods via non-convex ADMM, with convergence guarantees for the latter two and demonstrations of scalability to thousands of agents.