A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.
Achieving geometric convergence for distributed optimization over time-varying graphs,
2 Pith papers cite this work. Polarity classification is still indexing.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A fully distributed primal-dual algorithm solves nonsmooth strongly convex problems with coupled constraints on time-varying digraphs at O(1/k) rate without communicating primal variables.
citing papers explorer
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A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.
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Distributed Optimization with Coupled Constraints over Time-Varying Digraph
A fully distributed primal-dual algorithm solves nonsmooth strongly convex problems with coupled constraints on time-varying digraphs at O(1/k) rate without communicating primal variables.