10 citations · 40 across the 35 of their papers we have counts for
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math.OC2015
Approximately Truthful Multi-Agent Optimization Using Cloud-Enforced Joint Differential Privacy
M. T. Hale, M. Egerstedt
Multi-agent coordination problems often require agents to exchange state information in order to reach some collective goal, such as agreement on a final state value. In some cases…
math.OC2015
Cloud-Based Centralized/Decentralized Multi-Agent Optimization with Communication Delays
Matthew T. Hale, Angelia Nedic, Magnus Egerstedt
We present and analyze a computational hybrid architecture for performing multi-agent optimization. The optimization problems under consideration have convex objective and constrai…
math.OC2015
Cloud-Enabled Differentially Private Multi-Agent Optimization with Constraints
Matthew Hale, Magnus Egerstedt
We present an optimization framework for solving multi-agent nonlinear programs subject to inequality constraints while keeping the agents' state trajectories private. Each agent h…