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Pick your Neighbor: Local Gauss-Southwell Rule for Fast Asynchronous Decentralized Optimization
In decentralized optimization environments, each agent $i$ in a network of $n$ nodes has its own private function $f_i$, and nodes communicate with their neighbors to cooperatively minimize the aggregate objective $\sum_{i=1}^n f_i$.
Problem-Adapted Artificial Intelligence for Online Network Optimization
To this end, we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, online learning, and AI-based techniques.
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